diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/aggr_base_count.R b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/aggr_base_count.R
deleted file mode 100755
index df4e0bd02efe530164e34ebf3cfac4982362065a..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/aggr_base_count.R
+++ /dev/null
@@ -1,237 +0,0 @@
-#!/usr/bin/env Rscript
-
-# aggr_base_count.R
-#
-# Given a count file, write tab-delimited file(s) aggregating the counts
-# at gene and/or exon level.
-#
-# (c) 2013 by Wibowo Arindrarto [LUMC - SASC]
-# Adapted from Peter-Bram 't Hoen's script: 'merge_table_script_shark_PH3.r'
-
-# General function to install package if it does not exist
-# Otherwise, it only loads the package
-usePackage <- function(p) {
- r <- getOption("repos")
- r["CRAN"] <- "http://cran.us.r-project.org"
- options(repos = r)
- rm(r)
- if (!is.element(p, installed.packages()[,1]))
- install.packages(p, dep = TRUE)
- require(p, character.only = TRUE)
-}
-
-usePackage("getopt")
-
-## FLAGS ##
-LEVELS <- c('gene', 'exon')
-OUT.DIR <- getwd()
-DEBUG <- FALSE
-if (DEBUG) {
- message("## DEBUG MODE ##")
-}
-
-
-## FUNCTIONS ##
-CheckCountFiles <- function(count.files, DEBUG=FALSE) {
- # Given a vector of sample names, checks whether the .count files exist.
- #
- # Count files are the input files used to analyze the RNA-Seq expression
- # levels. They must conform to the following file name:
- # '{sample_name}/{sample_name}.count'
- #
- # Args:
- # - paths: string vector of file paths
- for (cfile in count.files) {
-
- if (!file.exists(cfile)) {
- stop(paste("Path '", cfile, "' does not exist. Exiting.", sep=""))
- }
-
- if (DEBUG) {
- message("Path '", cfile, "' exists.", sep="")
- }
- }
-}
-
-CountBaseExons <- function(count.files, count.names,
- col.header=c("gene", "chr", "start", "stop")) {
- # Given a list of count files, return a data frame containing their values.
- #
- # The count files must be a tab-separate file containing the following
- # columns in the same order:
- # 1. chromosome
- # 2. start position
- # 3. stop position
- # 4. total nucleotide counts
- # 5. nucleotide counts per exon
- # 6. gene name
- #
- # The returned data frame has the following columns:
- #
- # 1. gene name
- # 2. chromosome
- # 3. start position
- # 4. stop position
- # 5... total nucleotide counts for each sample
- #
- # This function assumes that for all count files, the values of the first
- # three columns are the same for each row.
- #
- # Args:
- # - count.files: string vector of count file paths
- # - col.headers: string vector of default data frame output headers
-
- # given a count file path, extract its fourth column
- GetNucCount <- function(x) {
- read.table(x, as.is=TRUE)[4]
- }
-
- # initial data frame is from the first file
- exon.counts <- read.table(count.files[1], as.is=TRUE)
- exon.counts <- exon.counts[, c(6, 1:3, 4)]
- colnames(exon.counts)[1:5] <- append(col.header, count.names[1])
-
- if (length(count.files) > 1) {
- # why doesn't R provide a nice way to slice remaining items??
- remaining.files <- count.files[2: length(count.files)]
- remaining.names <- count.names[2: length(count.names)]
- # append all nucleotide counts from remaining files to exon.counts
- exon.counts <- cbind(exon.counts, lapply(remaining.files, GetNucCount))
- # and rename these columns accordingly
- end.idx <- 5 + length(remaining.files)
- colnames(exon.counts)[6:end.idx] <- remaining.names
- }
-
- return(exon.counts)
-}
-
-CountExons <- function(exon.df) {
- # Given a data frame containing exon counts, return a data frame consisting of
- # compacted exon counts.
- #
- # In a compacted exon count data frame, each exon has its own unique name
- # consisting of its gene source and its start-stop coordinates.
- #
- # Args:
- # - exon.df: data frame of complete exon counts
-
-
- # create new data frame of the exon counts, concatenating gene name, and the
- # exon start-stop coordinates
- exon.dis.counts <- cbind(paste(paste(exon.df$gene, exon.df$start,
- sep=":"), exon.df$stop, sep="-"),
- exon.df[5: length(exon.df)])
- colnames(exon.dis.counts)[1] <- "exon"
- counts.in.samples <- as.matrix(exon.dis.counts[2:ncol(exon.dis.counts)])
- exon.counts <- aggregate(counts.in.samples ~ exon, data=exon.dis.counts, FUN=sum,
- na.rm=TRUE)
- colnames(exon.counts)[2:ncol(exon.counts)] <- colnames(counts.in.samples)
-
- return (exon.counts)
-}
-
-CountGenes <- function(exon.df) {
- # Given a data frame containing exon counts, return a data frame of gene
- # counts.
- #
- # See CountBaseExons for the input data frame format.
- #
- # Args:
- # - exon.df: data frame of complete exon counts
-
- # basically an aggregate of exon counts with the same gene name
- counts.in.samples <- as.matrix(exon.df[5:ncol(exon.df)])
- gene.counts <- aggregate(counts.in.samples ~ gene, data=exon.df, FUN=sum,
- na.rm=TRUE)
- # first column is gene
- colnames(gene.counts)[2:ncol(gene.counts)] <- colnames(counts.in.samples)
-
- return(gene.counts)
-}
-
-
-# load package for arg parsing
-library('getopt')
-
-# create spec for arg parsing
-spec <- matrix(c(
- # colon-separated paths to each count files
- 'count-file', 'I', 1, 'character',
- # colon-separated paths of each count file label; order must be the same
- # as the count files
- 'count-name', 'N', 1, 'character',
- # output file for gene level counts
- 'gene-count', 'G', 1, 'character',
- # output file for exon level counts
- 'exon-count', 'E', 1, 'character',
- # help
- 'help', 'H', 0, 'logical'
-), byrow=TRUE, ncol=4)
-opt <- getopt(spec)
-
-# print help if requested
-if (!is.null(opt[['help']])) {
- cat(getopt(spec, usage=TRUE))
- q(status=1)
-}
-
-# we need gene-count and/or exon-count flag
-if (is.null(opt[['gene-count']]) & is.null(opt[['exon-count']])) {
- message("Error: Either '--gene-count' and/or '--exon-count' must have a value.")
- q(status=1)
-}
-
-# set fallback values for optional args
-if (!is.null(opt[['output-dir']])) {
- OUT.DIR <- normalizePath(opt[['output-dir']])
- # create directory if it doesn't exist
- dir.create(OUT.DIR, showWarnings=FALSE)
-}
-
-# parse the input file paths and check their presence
-if (!is.null(opt[['count-file']])) {
- count.files <- opt[['count-file']]
- count.files <- unlist(strsplit(gsub(' ', '', count.files), ':'))
- CheckCountFiles(count.files, DEBUG)
-} else {
- stop("Required input count file path(s) not present. Exiting.")
-}
-
-# parse the input count labels and check if its length is the same as the input
-# files
-if (!is.null(opt[['count-name']])) {
- count.names <- opt[['count-name']]
- count.names <- unlist(strsplit(gsub(' ', '', count.names), ':'))
- if (length(count.names) != length(count.files)) {
- stop("Mismatched count file paths and labels. Exiting.")
- }
-} else {
- stop("Required input count file label(s) not present. Exiting.")
-}
-
-# set output file name for gene counts
-if (!is.null(opt[['gene-count']])) {
- gene.out <- opt[['gene-count']]
-} else {
- gene.out <- NULL
-}
-
-# set output file name for exon counts
-if (!is.null(opt[['exon-count']])) {
- exon.out <- opt[['exon-count']]
-} else {
- exon.out <- NULL
-}
-
-# count base exons (complete with coordinates)
-base.exon.counts <- CountBaseExons(count.files, count.names)
-
-# and write output files, depending on the flags
-if (!is.null(gene.out)) {
- gene.counts <- CountGenes(base.exon.counts)
- write.table(gene.counts, file = gene.out, sep = "\t", quote = FALSE, row.names = FALSE)
-}
-if (!is.null(exon.out)) {
- exon.counts <- CountExons(base.exon.counts)
- write.table(exon.counts, file = exon.out, sep = "\t", quote = FALSE, row.names = FALSE)
-}
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/bam_rna.py b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/bam_rna.py
deleted file mode 100755
index d2c14cff2588f01c7a9e3ba87ab5558244cb856e..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/bam_rna.py
+++ /dev/null
@@ -1,391 +0,0 @@
-#!/usr/bin/env python2.7
-#
-# Biopet is built on top of GATK Queue for building bioinformatic
-# pipelines. It is mainly intended to support LUMC SHARK cluster which is running
-# SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
-# should also be able to execute Biopet tools and pipelines.
-#
-# Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
-#
-# Contact us at: sasc@lumc.nl
-#
-# A dual licensing mode is applied. The source code within this project that are
-# not part of GATK Queue is freely available for non-commercial use under an AGPL
-# license; For commercial users or users who do not want to follow the AGPL
-# license, please contact us to obtain a separate license.
-#
-
-
-# bam_rna.py
-#
-# Generate read pair and alignment statistics for a BAM file.
-#
-# Part of the Gentrap pipeline
-#
-# (c) 2013 Wibowo Arindrarto [SASC - LUMC]
-
-import argparse
-import json
-import locale
-import os
-from collections import OrderedDict
-from functools import partial
-
-import pysam
-
-# set locale to group digits
-locale.setlocale(locale.LC_ALL, '')
-# formatters for output
-int_fmt = partial(locale.format, grouping=True, percent='%i')
-float_fmt = partial(locale.format, grouping=True, percent='%.2f')
-
-# -F 0x4
-func_nmap = lambda rec: not rec.flag & 0x4
-# -f 0xC
-func_nunmap = lambda rec: (rec.flag & 0x4) and (rec.flag & 0x8)
-# -F 0xC
-func_nmap_pair = lambda rec: not rec.flag & 0xC
-# -f 0x2
-func_nmap_pair_ok = lambda rec: rec.flag & 0x2
-# mapped to different chromosomes / reference sequences
-func_nmap_diffchr = lambda rec: rec.rnext != rec.tid
-# -F 0x4 -f 0x8
-func_nmap_sgltn = lambda rec: (not rec.flag & 0x4) and (rec.flag & 0x8)
-# check if spliced
-func_splice = lambda rec: 'N' in rec.cigarstring
-# check if pass QC
-# not bool since we always need to run each read through it
-func_qc = lambda rec: 1 if rec.flag & 0x200 else 0
-
-FLAGS = OrderedDict((
- ('total', 'Total'),
- ('unmapped', 'Unmapped'),
- ('mapped', 'Mapped'),
- ('mappedPair', 'Mapped pairs'),
- ('mappedPairProper', 'Properly mapped pairs'),
- ('mappedDiffChr', 'Different chromosomes'),
- ('mappedDiffChrQ', 'Different chromosomes, MAPQ >=5'),
- ('singleton', 'Singletons'),
- ('totalSplice', 'Split reads, total'),
- ('splicePairProper', 'Split reads, properly mapped'),
- ('spliceSingleton', 'Split reads, singletons'),
-))
-
-class BarnStat(object):
-
- """Class representing a collection of BAM statistics for RNA-seq data."""
-
- def __init__(self, bamfile, read_pair_suffix_len=0, id_sorted=False,
- validate=False):
- assert os.path.exists(bamfile), "BAM file %r not found." % bamfile
- self.validate = validate
- self.bamfile = bamfile
-
- self.flags = FLAGS.keys()
- # length of read pair suffix (e.g. '/2' has len == 2)
- self.suflen = read_pair_suffix_len
- if not id_sorted:
- self._count_unsorted()
- else:
- self._count_sorted()
-
- self._format_counts()
-
- def _adjust_counts(self, alns, reads):
- """Adjusts the alignment and read counts."""
- # we need to adjust the unmapped counts for alignments as each
- # alignments consists of two reads (one read pair) which may be mapped
- # multiple times as singletons and/or whole read pairs.
- # so unmapped alns is always == unmapped read pairs + singleton *reads*
- # counts (proxy for the set of unmapped singletons)
- if 'unmapped' in alns:
- alns['unmapped'] += reads['singleton']
- else:
- # tophat, splits unmapped reads into its own BAM file
- alns['unmapped'] = 0
-
- return alns, reads
-
- def _count_sorted(self):
- """Counts read and alignment statistics for ID-sorted BAM file."""
- flags = self.flags
- reads, alns, alns_qc = {}, {}, {}
- read_flags = dict.fromkeys(flags, False)
- cur_qname = None
-
- # init counts
- for flag in flags:
- reads[flag], alns[flag], alns_qc[flag] = 0, 0, 0
-
- # iterate over each record
- # index for suffix removal, if suffix exist (> 0)
- if self.suflen:
- sufslice = slice(-self.suflen)
- else:
- sufslice = slice(None)
- for rec in pysam.Samfile(self.bamfile, 'rb'):
- # different qname mean we've finished parsing each unique read
- # so reset the flags and add them to the counters appropriately
- if cur_qname != rec.qname[sufslice]:
- for flag in flags:
- reads[flag] += int(read_flags[flag])
- # reset the qname tracker
- cur_qname = rec.qname[sufslice]
- # and the read flag tracker
- read_flags = dict.fromkeys(flags, False)
- # total, total splice
- alns['total'] += 1
- alns_qc['total'] += func_qc(rec)
- read_flags['total'] = True
- if func_splice(rec):
- alns['totalSplice'] += 1
- alns_qc['totalSplice'] += func_qc(rec)
- read_flags['totalSplice'] = True
- # unmapped
- if func_nunmap(rec):
- alns['unmapped'] += 1
- alns_qc['unmapped'] += func_qc(rec)
- read_flags['unmapped'] = True
- else:
- # mapped
- if func_nmap(rec):
- alns['mapped'] += 1
- alns_qc['mapped'] += func_qc(rec)
- read_flags['mapped'] = True
- # mapped pairs
- if func_nmap_pair(rec):
- alns['mappedPair'] += 1
- alns_qc['mappedPair'] += func_qc(rec)
- read_flags['mappedPair'] = True
- # proper pairs, proper pairs splice
- if func_nmap_pair_ok(rec):
- alns['mappedPairProper'] += 1
- alns_qc['mappedPairProper'] += func_qc(rec)
- read_flags['mappedPairProper'] = True
- if func_splice(rec):
- alns['splicePairProper'] += 1
- alns_qc['splicePairProper'] += func_qc(rec)
- read_flags['splicePairProper'] = True
- # mapped to different chromosomes
- elif func_nmap_diffchr(rec):
- alns['mappedDiffChr'] += 1
- alns_qc['mappedDiffChr'] += func_qc(rec)
- read_flags['mappedDiffChr'] = True
- if rec.mapq >= 5:
- alns['mappedDiffChrQ'] += 1
- alns_qc['mappedDiffChrQ'] += func_qc(rec)
- read_flags['mappedDiffChrQ'] = True
- # singletons, singletons splice
- elif func_nmap_sgltn(rec):
- alns['singleton'] += 1
- alns_qc['singleton'] += func_qc(rec)
- read_flags['singleton'] = True
- if func_splice(rec):
- alns['spliceSingleton'] += 1
- alns_qc['spliceSingleton'] += func_qc(rec)
- read_flags['spliceSingleton'] = True
-
- # for the last read, since we don't pass the qname check again
- for flag in flags:
- reads[flag] += int(read_flags[flag])
-
- self.aln_counts, self.read_counts = self._adjust_counts(alns, reads)
- self.aln_qc_counts = alns_qc
- if self.validate:
- assert self.validate_counts()
-
- def _count_unsorted(self):
- """Counts read and alignment statistics for non-ID-sorted BAM file."""
- flags = self.flags
- reads_total, reads_unmap, reads_map, reads_pair_map, reads_pair_proper, \
- reads_sgltn, reads_total_splice, reads_pair_proper_splice, \
- reads_sgltn_splice, reads_pair_diffchr, reads_pair_diffchr_h = \
- set(), set(), set(), set(), set(), set(), set(), set(), set(), \
- set(), set()
-
- reads, alns, alns_qc = {}, {}, {}
- for flag in flags:
- reads[flag], alns[flag], alns_qc[flag] = 0, 0, 0
- # index for suffix removal, if suffix exist (> 0)
- if self.suflen:
- sufslice = slice(-self.suflen)
- else:
- sufslice = slice(None)
- for rec in pysam.Samfile(self.bamfile, 'rb'):
- # remove '/1' or '/2' suffixes, to collapse read pair counts
- pass
- # do countings on alns and reads directly
- hname = hash(rec.qname[sufslice])
- # total, total splice
- alns['total'] += 1
- alns_qc['total'] += func_qc(rec)
- if hname not in reads_total:
- reads_total.add(hname)
- if func_splice(rec):
- alns['totalSplice'] += 1
- alns_qc['totalSplice'] += func_qc(rec)
- reads_total_splice.add(hname)
- # unmapped
- if func_nunmap(rec):
- alns['unmapped'] += 1
- alns_qc['unmapped'] += func_qc(rec)
- reads_unmap.add(hname)
- else:
- # mapped
- if func_nmap(rec):
- alns['mapped'] += 1
- alns_qc['mapped'] += func_qc(rec)
- reads_map.add(hname)
- # mapped pairs
- if func_nmap_pair(rec):
- alns['mappedPair'] += 1
- alns_qc['mappedPair'] += func_qc(rec)
- reads_pair_map.add(hname)
- # proper pairs, proper pairs splice
- if func_nmap_pair_ok(rec):
- alns['mappedPairProper'] += 1
- alns_qc['mappedPairProper'] += func_qc(rec)
- reads_pair_proper.add(hname)
- if func_splice(rec):
- alns['splicePairProper'] += 1
- alns_qc['splicePairProper'] += func_qc(rec)
- reads_pair_proper_splice.add(hname)
- # mapped to different chromosomes
- elif func_nmap_diffchr(rec):
- alns['mappedDiffChr'] += 1
- alns_qc['mappedDiffChr'] += func_qc(rec)
- reads_pair_diffchr.add(hname)
- if rec.mapq >= 5:
- alns['mappedDiffChrQ'] += 1
- alns_qc['mappedDiffChrQ'] += func_qc(rec)
- reads_pair_diffchr_h.add(hname)
- # singletons, singletons splice
- elif func_nmap_sgltn(rec):
- alns['singleton'] += 1
- alns_qc['singleton'] += func_qc(rec)
- reads_sgltn.add(hname)
- if func_splice(rec):
- alns['spliceSingleton'] += 1
- alns_qc['spliceSingleton'] += func_qc(rec)
- reads_sgltn_splice.add(hname)
-
- # set counts for reads
- reads['total'] = len(reads_total)
- reads['totalSplice'] = len(reads_total_splice)
- reads['unmapped'] = len(reads_unmap)
- reads['mapped'] = len(reads_map)
- reads['mappedPair'] = len(reads_pair_map)
- reads['mappedPairProper'] = len(reads_pair_proper)
- reads['mappedDiffChr'] = len(reads_pair_diffchr)
- reads['mappedDiffChrQ'] = len(reads_pair_diffchr_h)
- reads['splicePairProper'] = len(reads_pair_proper_splice)
- reads['singleton'] = len(reads_sgltn)
- reads['spliceSingleton'] = len(reads_sgltn_splice)
-
- # free the memory
- del reads_total, reads_map, reads_pair_map, reads_pair_proper, \
- reads_sgltn, reads_total_splice, reads_pair_proper_splice, \
- reads_sgltn_splice, reads_unmap, reads_pair_diffchr, \
- reads_pair_diffchr_h
-
- self.aln_counts, self.read_counts = self._adjust_counts(alns, reads)
- self.aln_qc_counts = alns_qc
- if self.validate:
- assert self.validate_counts()
-
- def validate_counts(self):
- """Checks whether all reads and alignment counts add up."""
- for ctype in ('read_counts', 'aln_counts'):
- count = getattr(self, ctype)
- ntotal = count['total']
- nmap = count['mapped']
- nunmap = count['unmapped']
- nmap_pair = count['mappedPair']
- nmap_pair_ok = count['mappedPairProper']
- nmap_pair_diffchr = count['mappedDiffChr']
- nmap_sgltn = count['singleton']
- nsplice_total = count['totalSplice']
- nsplice_pair_ok = count['splicePairProper']
- nsplice_sgltn = count['spliceSingleton']
-
- assert nmap == nmap_pair + nmap_sgltn, \
- "Mismatch: %r == %r + %r" % (nmap, nmap_pair, nmap_sgltn)
- assert nmap_pair_ok + nmap_pair_diffchr <= nmap_pair
- assert nsplice_total <= ntotal
- assert nsplice_pair_ok <= nmap_pair_ok
- assert nsplice_sgltn <= nmap_sgltn
- # total is always == unmapped + mapped pair + singletons
- assert ntotal == nunmap + nmap_pair + nmap_sgltn, "Mismatch: " \
- "%r == %r + %r + %r" % (ntotal, nunmap, nmap_pair,
- nmap_sgltn)
-
- return True
-
- def show(self):
- """Prints the read and alignment counts in human-readable format to
- stdout."""
- import pprint
- pprint.pprint(dict(self.counts.items()))
-
- def write_json(self, out_file):
- """Writes JSON to the output file."""
- with open(out_file, 'w') as outfile:
- json.dump(self.counts, outfile, sort_keys=True, indent=4,
- separators=(',', ': '))
-
- def _format_counts(self):
- """Formats read and alignment counts into nice-looking numbers."""
- counts = OrderedDict()
- flags = self.flags
- for ctype in ('read_counts', 'aln_counts', 'aln_qc_counts'):
- count = getattr(self, ctype)
-
- ntotal = count['total']
- cont = {}
- lvl = 'aln' if ctype == 'aln_counts' else 'read'
-
- if ctype == 'aln_qc_counts':
- lvl = 'aln_qc'
- else:
- pct = lambda x: x * 100.0 / ntotal
- if ctype == 'read_counts':
- lvl = 'read'
- elif ctype == 'aln_counts':
- lvl = 'aln'
-
- for flag in flags:
- # format all counts
- cont[flag] = int_fmt(value=count[flag])
- # and add percentage values
- if lvl != 'aln_qc':
- if flag == 'total':
- cont['totalPct'] = '100'
- else:
- cont[flag + 'Pct'] = float_fmt(value=pct(count[flag]))
-
- counts[lvl] = cont
-
- self.counts = counts
-
-
-if __name__ == '__main__':
-
- parser = argparse.ArgumentParser()
- parser.add_argument('bamfile', help='Path to BAM file')
- parser.add_argument('--id-sorted', action='store_true',
- dest='id_sorted', help='Whether the BAM file is ID-sorted or not')
- parser.add_argument('--suffix-len', type=int, dest='suffix_len', default=0,
- help='Length of read pair suffix, if present')
- parser.add_argument('-o', '--outfile', dest='out_file', type=str,
- help='Path to output file')
- parser.add_argument('-f', '--outfmt', dest='out_fmt', type=str,
- choices=['json'], default='json',
- help='Format of output file')
- args = parser.parse_args()
-
- bamstat = BarnStat(args.bamfile, args.suffix_len, args.id_sorted)
-
- if args.out_file is None:
- bamstat.show()
- elif args.out_fmt == 'json':
- bamstat.write_json(args.out_file)
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/gc_dist.py b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/gc_dist.py
deleted file mode 100755
index 27f2d482867f0250b1ed8df9ff6f921de103191f..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/gc_dist.py
+++ /dev/null
@@ -1,173 +0,0 @@
-#!/usr/bin/env python
-#
-# Biopet is built on top of GATK Queue for building bioinformatic
-# pipelines. It is mainly intended to support LUMC SHARK cluster which is running
-# SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
-# should also be able to execute Biopet tools and pipelines.
-#
-# Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
-#
-# Contact us at: sasc@lumc.nl
-#
-# A dual licensing mode is applied. The source code within this project that are
-# not part of GATK Queue is freely available for non-commercial use under an AGPL
-# license; For commercial users or users who do not want to follow the AGPL
-# license, please contact us to obtain a separate license.
-#
-
-#
-# gc_dist.py
-#
-# Given a path to a FASTQ file, create plots of GC percentages.
-#
-# Part of the Gentrap pipeline.
-#
-# (c) 2013 Wibowo Arindrarto [SASC - LUMC]
-
-import argparse
-import locale
-import os
-import textwrap
-
-import numpy as np
-# for headless matplotlib
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gs
-
-from matplotlib.ticker import FuncFormatter, MultipleLocator
-
-
-# set locale and formatter to do digit grouping
-locale.setlocale(locale.LC_ALL, '')
-groupdig = lambda x, pos: locale.format('%d', x, grouping=True)
-major_formatter = FuncFormatter(groupdig)
-
-
-def read_seq(fp):
- """Given a FASTQ file, yield its sequences."""
- if isinstance(fp, basestring):
- assert os.path.exists(fp)
- fp = open(fp, 'r')
- for counter, line in enumerate(fp):
- if (counter + 3) % 4 == 0:
- yield line.strip()
-
-
-def drange(start, stop, step):
- """Like `range` but for floats."""
- cur = start
- while cur < stop:
- yield cur
- cur += step
-
-
-def graph_gc(fname, outname='test.png'):
- """Graphs the GC percentages of the given FASTQ file."""
- # count GC percentages per sequence
- gcs = []
- for seq in read_seq(fname):
- gc = sum(seq.lower().count(x) for x in ('g', 'c', 's'))
- gcs.append(gc * 100.0 / len(seq))
- # grab mean and std dev for plotting
- mean = np.mean(gcs)
- stdev = np.std(gcs)
-
- # set the subplots in the figure; top is histogram, bottom is boxplot
- fig = plt.figure(figsize=(8, 8))
- grids = gs.GridSpec(2, 1, height_ratios=[5, 1])
-
- ax0 = plt.subplot(grids[0])
- # set title and adjust distance to plot
- title = 'Distribution of GC Percentage'
- t = plt.title('\n'.join([title] + textwrap.wrap('%r' %
- os.path.basename(fname), 50)), fontsize=15)
- t.set_y(1.05)
-
- # start counting bins for width measurement
- total = len(gcs)
- min_hist = min(gcs)
- max_hist = max(gcs)
- low = high = np.median(gcs)
- step = 1
- widths = dict.fromkeys(range(20, 100, 20) + [99], (0, 0))
-
- while low >= min_hist or high <= max_hist:
- # cap the width marker at min or max gc values
- if high > max_hist: high = max_hist
- if low < min_hist: low = min_hist
-
- range_count = len([x for x in gcs if low < x < high])
- coverage = float(range_count) / total
-
- if coverage >= 0.2 and not any(widths[20]):
- widths[20] = (low, high)
- if coverage >= 0.4 and not any(widths[40]):
- widths[40] = (low, high)
- if coverage >= 0.6 and not any(widths[60]):
- widths[60] = (low, high)
- if coverage >= 0.8 and not any(widths[80]):
- widths[80] = (low, high)
- if coverage >= 0.99 and not any(widths[99]):
- widths[99] = (low, high)
-
- low -= step
- high += step
-
- # use the bin coordinates for partial background coloring
- for hstart, hend in widths.values():
- plt.axvspan(hstart, hend, facecolor='#0099ff', linestyle='dotted',
- linewidth=2.0, edgecolor='black', alpha=0.2)
-
- # plot the histogram
- bins = [0] + list(drange(2.5, 100, 5)) + [100]
- n, bins, patches = ax0.hist(gcs, bins=bins, facecolor='#009933', alpha=0.9)
- # set Y-axis ticks label formatting
- ax0.yaxis.set_major_formatter(major_formatter)
- ax0.yaxis.grid(True)
- plt.ylabel('Read count')
- ax0.text(0.02, 0.9, 'Mean: %.2f\nStdev: %.2f' % (mean, stdev),
- transform=ax0.transAxes, bbox=dict(facecolor='grey', alpha=0.5,
- edgecolor='none'), size=14)
-
- # plot the boxplot
- # shared X-axis, but invisible
- ax1 = plt.subplot(grids[1], sharex=ax0)
- plt.setp(ax1.get_xticklabels(), visible=False)
- # and set the Y-axis to be invisible completely
- ax1.axes.get_yaxis().set_visible(False)
- plot = ax1.boxplot(gcs, vert=False, widths=0.6, sym='r.')
- # line width and color settings for boxplot
- plot['fliers'][0].set_color('#e62e00')
- plot['fliers'][1].set_color('#e62e00')
- plot['boxes'][0].set_color('black')
- plot['boxes'][0].set_linewidth(1.2)
- plot['medians'][0].set_linewidth(1.2)
- plot['medians'][0].set_color('black')
- plot['whiskers'][0].set_color('black')
- plot['whiskers'][0].set_linewidth(1.2)
- plot['whiskers'][1].set_color('black')
- plot['whiskers'][1].set_linewidth(1.2)
- plot['caps'][0].set_linewidth(1.2)
- plot['caps'][1].set_linewidth(1.2)
- # set X-axis label and ticks
- ax0.xaxis.set_major_locator(MultipleLocator(10))
- ax0.xaxis.set_minor_locator(MultipleLocator(5))
- plt.xlabel('% GC')
-
- grids.update(hspace=0.075)
- plt.savefig(outname, bbox_inches='tight')
-
- return gcs
-
-
-if __name__ == '__main__':
-
- parser = argparse.ArgumentParser()
- parser.add_argument('input', help='input FASTQ file', default='reads.fq')
- parser.add_argument('output', help='output image file', default='test.png')
-
- args = parser.parse_args()
-
- gcs = graph_gc(args.input, args.output)
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/hist2count.py b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/hist2count.py
deleted file mode 100644
index e91519742c77c6d2bd7fdce66728ac927771118e..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/hist2count.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-#
-# Biopet is built on top of GATK Queue for building bioinformatic
-# pipelines. It is mainly intended to support LUMC SHARK cluster which is running
-# SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
-# should also be able to execute Biopet tools and pipelines.
-#
-# Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
-#
-# Contact us at: sasc@lumc.nl
-#
-# A dual licensing mode is applied. The source code within this project that are
-# not part of GATK Queue is freely available for non-commercial use under an AGPL
-# license; For commercial users or users who do not want to follow the AGPL
-# license, please contact us to obtain a separate license.
-#
-
-"""
-Convert a histogram generated by coverageBed to counts per region.
-
-
-A histogram file can be generated with the following command:
-coverageBed -split -hist -abam sample.bam -b selection.bed > selection.hist
-
-The output consists of four columns:
-- Chromosome name.
-- Start position.
-- End position.
-- Number of nucleotides mapped to this region.
-- Normalised expression for this region.
-
-If the -c option is used, additional columns can be added.
-"""
-
-import argparse
-import sys
-
-def hist2count(inputHandle, outputHandle, copy):
- """
- Split a fasta file on length.
-
- @arg inputHandle: Open readable handle to a histogram file.
- @type inputHandle: stream
- @arg outputHandle: Open writable handle to the counts file.
- @type outputHandle: stream
- @arg outputHandle: List of columns to copy to the output file.
- @type outputHandle: list[int]
- """
- def __copy():
- copyList = ""
- for i in copy:
- copyList += "\t%s" % data[i]
- return copyList
- #__copy
-
- def __write():
- outputHandle.write("%s\t%i\t%i\t%i\t%f%s\n" % (chromosome, start,
- end, count, float(count) / (end - start), copyList))
-
- chromosome = ""
- start = 0
- end = 0
- count = 0
-
- for line in inputHandle.readlines():
- data = line.split()
-
- if not data[0] == "all":
- start_temp = int(data[1])
- end_temp = int(data[2])
-
- if data[0] != chromosome or start_temp != start or end_temp != end:
- if chromosome:
- __write()
- chromosome = data[0]
- start = start_temp
- end = end_temp
- count = 0
- copyList = __copy()
- #if
- count += int(data[-4]) * int(data[-3])
- #if
- #for
- __write()
-#hist2count
-
-def main():
- """
- Main entry point.
- """
- usage = __doc__.split("\n\n\n")
- parser = argparse.ArgumentParser(
- formatter_class=argparse.RawDescriptionHelpFormatter,
- description=usage[0], epilog=usage[1])
- parser.add_argument("-i", dest="input", type=argparse.FileType("r"),
- default=sys.stdin, help="histogram input file (default=<stdin>)")
- parser.add_argument("-o", dest="output", type=argparse.FileType("w"),
- default=sys.stdout, help="file used as output (default=<stdout>)")
- parser.add_argument("-c", dest="copy", type=int, nargs="+", default=[],
- help="copy a column to the output file")
- args = parser.parse_args()
-
- hist2count(args.input, args.output, args.copy)
-#main
-
-if __name__ == '__main__':
- main()
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/insert_dist.py b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/insert_dist.py
deleted file mode 100755
index 332e7313409b84847ce899ebc3e51ba033fe78fc..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/insert_dist.py
+++ /dev/null
@@ -1,187 +0,0 @@
-#!/usr/bin/env python
-#
-# Biopet is built on top of GATK Queue for building bioinformatic
-# pipelines. It is mainly intended to support LUMC SHARK cluster which is running
-# SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
-# should also be able to execute Biopet tools and pipelines.
-#
-# Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
-#
-# Contact us at: sasc@lumc.nl
-#
-# A dual licensing mode is applied. The source code within this project that are
-# not part of GATK Queue is freely available for non-commercial use under an AGPL
-# license; For commercial users or users who do not want to follow the AGPL
-# license, please contact us to obtain a separate license.
-#
-
-#
-# insert_dist.py
-#
-# Given path to a text file containing Picard's CollectInsertSizeMetrics
-# results, create a new graph.
-#
-# (c) 2013 Wibowo Arindrarto [SASC - LUMC]
-
-import argparse
-import locale
-import os
-import re
-import textwrap
-
-from collections import namedtuple
-from functools import partial
-
-# for headless matplotlib
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-
-from matplotlib.ticker import FuncFormatter
-
-# set locale and formatter for axis ticks
-locale.setlocale(locale.LC_ALL, '')
-groupdig = lambda x, pos: locale.format('%d', x, grouping=True)
-major_formatter = FuncFormatter(groupdig)
-int_fmt = partial(locale.format, grouping=True, percent='%i')
-
-
-def multi_annotate(ax, title, xy_arr=[], *args, **kwargs):
- """Axis annotation function that targets multiple data points."""
- ans = []
- an = ax.annotate(title, xy_arr[0], *args, **kwargs)
- ans.append(an)
- d = {}
- if 'xycoords' in kwargs:
- d['xycoords'] = kwargs['xycoords']
- if 'arrowprops' in kwargs:
- d['arrowprops'] = kwargs['arrowprops']
- for xy in xy_arr[1:]:
- an = ax.annotate(title, xy, alpha=0.0, xytext=(0, 0), textcoords=an, **d)
- ans.append(an)
-
- return ans
-
-
-def parse_insert_sizes_histogram(fname):
- """Given a filename or a file object of a Picard COllectInsertSizeMetrics
- output, return the filename, the histogram column names, and the histogram
- data."""
-
- if isinstance(fname, basestring):
- fp = open(fname, 'r')
- else:
- fp = fname
-
- line = fp.readline()
- while True:
- if not line:
- raise ValueError("Unexpected end of file")
- # try to get the original bam file name
- elif 'net.sf.picard.analysis.CollectInsertSizeMetrics' in line:
- input = re.search('INPUT=([^\s]*)', line).group(1)
- bamname = os.path.basename(input)
- elif line.startswith('## HISTOGRAM'):
- break
- line = fp.readline()
-
- # get column names
- colnames = fp.readline().strip().split('\t')
-
- # iterate over the histogram data lines
- # and fill up missing data with 0s
- data = []
- counter = 0
- for line in fp:
- if not line.strip():
- break
- # bin number starts at 1
- tokens = [int(x) for x in line.split('\t')]
- numcol = len(tokens) - 1
- if counter == tokens[0] - 1:
- data.append(tokens[1:])
- counter += 1
- else:
- while tokens[0] - counter != 1:
- data.append([0] * numcol)
- counter += 1
- data.append(tokens[1:])
- counter += 1
-
- histogram = data
-
- return bamname, colnames, histogram
-
-
-def graph_insert_sizes(fname, outname='test.png'):
- """Given a Picard CollectInsertSizes text output filename, write graph(s)
- for the histogram."""
- bamname, colnames, hist = parse_insert_sizes_histogram(fname)
-
- # map Picard's insert type (based on its column name)
- # to our own name and color
- InsType = namedtuple('InsType', ['label', 'color'])
- design_map = {
- # 5' --F--> <--R-- 5
- 'fr_count': InsType('inward', '#009933'),
- # <--R-- 5' 5' --F-->
- 'rf_count': InsType('outward', 'orange'),
- # 5' --F--> 5' --F--> or <--R-- 5' <--R-- 5'
- 'tandem_count': InsType('same directions', '#e62e00'),
- }
-
- fig = plt.figure()
- ax = plt.subplot(111)
- for idx, col in enumerate(colnames[1:]):
- pcd_name = col.split('.')[-1]
- try:
- label = design_map[pcd_name].label
- color = design_map[pcd_name].color
- except KeyError:
- raise ValueError("Unexpected column name: %r" % col)
-
- data = [m[idx] for m in hist]
- plt.bar(range(len(hist)), data, width=1, linewidth=0, color=color,
- alpha=0.6, label=label)
-
- max_val = max(data)
- max_val_size = data.index(max_val)
- highest_points = [(idx, max_val) for idx, val in enumerate(data) if val == max_val]
- x_adj = int(len(data) * 0.1)
- y_adj = int(max_val * 0.1)
- bbox_props = dict(boxstyle="round", fc="w", edgecolor='black', alpha=1.0)
- multi_annotate(ax,
- 'max count: {0}\nsize: {1} bp'.format(int_fmt(value=max_val),
- ', '.join([str(x[0]) for x in highest_points])),
- xy_arr=highest_points,
- xytext=(max_val_size + x_adj, max_val + y_adj),
- fontsize=9, bbox=bbox_props,
- horizontalalignment='left', verticalalignment='center',
- arrowprops=dict(color='black', shrink=0.1, width=0.5, headwidth=2.5, ),)
-
- # adjust ylim to account for annotation box
- init_ylim = ax.get_ylim()
- ax.set_ylim(0, init_ylim[1] * 1.08)
-
- # set title and its spacing
- title = 'Insert Sizes Distribution'
- t = plt.title('\n'.join([title] + textwrap.wrap('%r' % bamname, 50)),
- fontsize=15)
- t.set_y(1.05)
- plt.legend()
- plt.xlabel("Insert Size")
- plt.ylabel("Alignment Count")
- ax.yaxis.set_major_formatter(major_formatter)
- ax.grid(True)
- plt.savefig(outname, bbox_inches='tight')
-
-
-if __name__ == '__main__':
-
- parser = argparse.ArgumentParser()
- parser.add_argument('input', help='input file')
- parser.add_argument('output', help='output image file', default='test.png')
-
- args = parser.parse_args()
-
- graph_insert_sizes(args.input, args.output)
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/parse_cuffcmp.py b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/parse_cuffcmp.py
deleted file mode 100755
index 81ff276046b29ccfa269a9eca8d9293edba58067..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/parse_cuffcmp.py
+++ /dev/null
@@ -1,157 +0,0 @@
-#!/usr/bin/env python
-#
-# Biopet is built on top of GATK Queue for building bioinformatic
-# pipelines. It is mainly intended to support LUMC SHARK cluster which is running
-# SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
-# should also be able to execute Biopet tools and pipelines.
-#
-# Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
-#
-# Contact us at: sasc@lumc.nl
-#
-# A dual licensing mode is applied. The source code within this project that are
-# not part of GATK Queue is freely available for non-commercial use under an AGPL
-# license; For commercial users or users who do not want to follow the AGPL
-# license, please contact us to obtain a separate license.
-#
-
-#
-# oarse_cuffcmp.py
-#
-# Parses cuffcompare's cuffcmp.stats output into a JSON file.
-#
-# Part of the Gentrap pipeline.
-#
-# (c) 2013 by Wibowo Arindrarto [LUMC - SASC]
-
-import argparse
-import json
-import locale
-import os
-import re
-
-
-# set locale to group digits
-locale.setlocale(locale.LC_ALL, '')
-
-# precompiled regex patterns
-_base_qr = '\s+(\d+)/(\d+)'
-_base_table = '\s+(.*?)\s+(.*?)\s+(.*?)\s+(.*?)\s+'
-# source transcripts gtf
-_re_dataset = re.compile(r'Summary for dataset:\s+(.*?)\s+:')
-# ref exons not covered by query exons, total ref exons
-_re_rexons = re.compile(r'Missed exons:%s' % _base_qr)
-# query exons not covered by ref exons, total query exons
-_re_qexons = re.compile(r'Novel exons:%s' % _base_qr)
-# ref introns not covered by query introns, total ref introns
-_re_rintrons = re.compile(r'Missed introns:%s' % _base_qr)
-# query introns not covered by ref introns, total query introns
-_re_qintrons = re.compile(r'Novel introns:%s' % _base_qr)
-# ref loci not covered by query loci, total ref loci
-_re_rloci = re.compile(r'Missed loci:%s' % _base_qr)
-# query loci not covered by ref loci, total query loci
-_re_qloci = re.compile(r'Novel loci:%s' % _base_qr)
-# base level metrics
-_re_base = re.compile(r'Base level:%s' % _base_table)
-# exon level metrics
-_re_exon = re.compile(r'Exon level:%s' % _base_table)
-# intron level metrics
-_re_intron = re.compile(r'Intron level:%s' % _base_table)
-# intron chain level metrics
-_re_intron_chain = re.compile(r'Intron chain level:%s' % _base_table)
-# transcript level metrics
-_re_transcript = re.compile(r'Transcript level:%s' % _base_table)
-# locus level metrics
-_re_locus = re.compile(r'Locus level:%s' % _base_table)
-
-
-def _fallback_search(re_pattern, string, match_type, fallback_str, group,
- replacement=None):
- """Function to handle cases when the regex match is of a different type,
- e.g. '-' instead of an integer."""
- match = re.search(re_pattern, string).group(group)
-
- if match == fallback_str:
- return replacement
- else:
- return match_type(match)
-
-
-def parse_cuffcmp_stats(stat_file):
- """Parses the statistics in the given cuffcmp.stats file into a
- dictionary."""
- assert os.path.exists(stat_file), "File %r not found" % stat_file
-
- with open(stat_file, 'r') as source:
- # not expecting a huge output, we can store everything in memory
- stat_str = source.read()
-
- stats = {
- 'dataSet': re.search(_re_dataset, stat_str).group(1),
- 'refExonsNotInQuery': int(re.search(_re_rexons, stat_str).group(1)),
- 'refExonsTotal': int(re.search(_re_rexons, stat_str).group(2)),
- 'queryExonsNotInRef': int(re.search(_re_qexons, stat_str).group(1)),
- 'queryExonsTotal': int(re.search(_re_qexons, stat_str).group(2)),
-
- 'refIntronsNotInQuery': int(re.search(_re_rintrons, stat_str).group(1)),
- 'refIntronsTotal': int(re.search(_re_rintrons, stat_str).group(2)),
- 'queryIntronsNotInRef': int(re.search(_re_qintrons, stat_str).group(1)),
- 'queryIntronsTotal': int(re.search(_re_qintrons, stat_str).group(2)),
-
- 'refLociNotInQuery': int(re.search(_re_rloci, stat_str).group(1)),
- 'refLociTotal': int(re.search(_re_rloci, stat_str).group(2)),
- 'queryLociNotInRef': int(re.search(_re_qloci, stat_str).group(1)),
- 'queryLociTotal': int(re.search(_re_qloci, stat_str).group(2)),
-
- 'baseLevelSn': _fallback_search(_re_base, stat_str, float, '-', 1),
- 'baseLevelSp': _fallback_search(_re_base, stat_str, float, '-', 2),
- 'baseLevelFSn': _fallback_search(_re_base, stat_str, float, '-', 3),
- 'baseLevelFSp': _fallback_search(_re_base, stat_str, float, '-', 4),
-
- 'exonLevelSn': _fallback_search(_re_exon, stat_str, float, '-', 1),
- 'exonLevelSp': _fallback_search(_re_exon, stat_str, float, '-', 2),
- 'exonLevelFSn': _fallback_search(_re_exon, stat_str, float, '-', 3),
- 'exonLevelFSp': _fallback_search(_re_exon, stat_str, float, '-', 4),
-
- 'intronLevelSn': _fallback_search(_re_intron, stat_str, float, '-', 1),
- 'intronLevelSp': _fallback_search(_re_intron, stat_str, float, '-', 2),
- 'intronLevelFSn': _fallback_search(_re_intron, stat_str, float, '-', 3),
- 'intronLevelFSp': _fallback_search(_re_intron, stat_str, float, '-', 4),
-
- 'intronChainLevelSn': _fallback_search(_re_intron_chain, stat_str, float, '-', 1),
- 'intronChainLevelSp': _fallback_search(_re_intron_chain, stat_str, float, '-', 2),
- 'intronChainLevelFSn': _fallback_search(_re_intron_chain, stat_str, float, '-', 3),
- 'intronChainLevelFSp': _fallback_search(_re_intron_chain, stat_str, float, '-', 4),
-
- 'transcriptLevelSn': _fallback_search(_re_transcript, stat_str, float, '-', 1),
- 'transcriptLevelSp': _fallback_search(_re_transcript, stat_str, float, '-', 2),
- 'transcriptLevelFSn': _fallback_search(_re_transcript, stat_str, float, '-', 3),
- 'transcriptLevelFSp': _fallback_search(_re_transcript, stat_str, float, '-', 4),
-
- 'locusLevelSn': _fallback_search(_re_locus, stat_str, float, '-', 1),
- 'locusLevelSp': _fallback_search(_re_locus, stat_str, float, '-', 2),
- 'locusLevelFSn': _fallback_search(_re_locus, stat_str, float, '-', 3),
- 'locusLevelFSp': _fallback_search(_re_locus, stat_str, float, '-', 4),
- }
-
- return stats
-
-
-if __name__ == '__main__':
-
- parser = argparse.ArgumentParser()
- parser.add_argument('input', type=str,
- help='Path to input cuffcmp.stats file')
- parser.add_argument('-o', '--output-json', dest='output', type=str,
- help='Path to JSON output file', default=None)
- args = parser.parse_args()
-
- stats = parse_cuffcmp_stats(args.input)
-
- if args.output is not None:
- with open(args.output, 'w') as jsonfile:
- json.dump(stats, jsonfile, sort_keys=True, indent=4,
- separators=(',', ': '))
- else:
- print json.dumps(stats, sort_keys=True, indent=4,
- separators=(',', ': '))
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/pdf_report.py b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/pdf_report.py
deleted file mode 100755
index b0c2b2e82e431d6340c74575ad07c0e48a19b623..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/pdf_report.py
+++ /dev/null
@@ -1,586 +0,0 @@
-#!/usr/bin/env python
-#
-# Biopet is built on top of GATK Queue for building bioinformatic
-# pipelines. It is mainly intended to support LUMC SHARK cluster which is running
-# SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
-# should also be able to execute Biopet tools and pipelines.
-#
-# Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
-#
-# Contact us at: sasc@lumc.nl
-#
-# A dual licensing mode is applied. The source code within this project that are
-# not part of GATK Queue is freely available for non-commercial use under an AGPL
-# license; For commercial users or users who do not want to follow the AGPL
-# license, please contact us to obtain a separate license.
-#
-
-from __future__ import print_function
-
-import argparse
-import json
-import locale
-import os
-import re
-import sys
-from os import path
-
-from jinja2 import Environment, FileSystemLoader
-
-
-# set locale for digit grouping
-locale.setlocale(locale.LC_ALL, "")
-
-
-class FastQCModule(object):
-
- """Class representing a FastQC analysis module."""
-
- def __init__(self, raw_lines, end_mark='>>END_MODULE'):
- """
-
- :param raw_lines: list of lines in the module
- :type raw_lines: list of str
- :param end_mark: mark of the end of the module
- :type end_mark: str
-
- """
- self.raw_lines = raw_lines
- self.end_mark = end_mark
- self._status = None
- self._name = None
- self._data = self._parse()
-
- def __repr__(self):
- return '%s(%s)' % (self.__class__.__name__,
- '[%r, ...]' % self.raw_lines[0])
-
- def __str__(self):
- return ''.join(self.raw_lines)
-
- @property
- def name(self):
- """Name of the module."""
- return self._name
-
- @property
- def columns(self):
- """Columns in the module."""
- return self._columns
-
- @property
- def data(self):
- """FastQC data."""
- return self._data
-
- @property
- def status(self):
- """FastQC run status."""
- return self._status
-
- def _parse(self):
- """Common parser for a FastQC module."""
- # check that the last line is a proper end mark
- assert self.raw_lines[-1].startswith(self.end_mark)
- # parse name and status from first line
- tokens = self.raw_lines[0].strip().split('\t')
- name = tokens[0][2:]
- self._name = name
- status = tokens[-1]
- assert status in ('pass', 'fail', 'warn'), "Unknown module status: %r" \
- % status
- self._status = status
- # and column names from second line
- columns = self.raw_lines[1][1:].strip().split('\t')
- self._columns = columns
- # the rest of the lines except the last one
- data = []
- for line in self.raw_lines[2:-1]:
- cols = line.strip().split('\t')
- data.append(cols)
-
- # optional processing for different modules
- if self.name == 'Basic Statistics':
- data = {k: v for k, v in data}
-
- return data
-
-
-class FastQC(object):
-
- """Class representing results from a FastQC run."""
-
- # module name -- attribute name mapping
- _mod_map = {
- '>>Basic Statistics': 'basic_statistics',
- '>>Per base sequence quality': 'per_base_sequence_quality',
- '>>Per sequence quality scores': 'per_sequence_quality_scores',
- '>>Per base sequence content': 'per_base_sequence_content',
- '>>Per base GC content': 'per_base_gc_content',
- '>>Per sequence GC content': 'per_sequence_gc_content',
- '>>Per base N content': 'per_base_n_content',
- '>>Sequence Length Distribution': 'sequence_length_distribution',
- '>>Sequence Duplication Levels': 'sequence_duplication_levels',
- '>>Overrepresented sequences': 'overrepresented_sequences',
- '>>Kmer content': 'kmer_content',
- }
-
- def __init__(self, fname):
- """
-
- :param fp: open file handle pointing to the FastQC data file
- :type fp: file handle
-
- """
- # get file name
- self.fname = fname
- self._modules = {}
-
- with open(fname, "r") as fp:
- line = fp.readline()
- while True:
-
- tokens = line.strip().split('\t')
- # break on EOF
- if not line:
- break
- # parse version
- elif line.startswith('##FastQC'):
- self.version = line.strip().split()[1]
- # parse individual modules
- elif tokens[0] in self._mod_map:
- attr = self._mod_map[tokens[0]]
- raw_lines = self._read_module(fp, line, tokens[0])
- self._modules[attr] = FastQCModule(raw_lines)
-
- line = fp.readline()
-
- def __repr__(self):
- return '%s(%r)' % (self.__class__.__name__, self.fname)
-
- def _filter_by_status(self, status):
- """Filter out modules whose status is different from the given status.
-
- :param status: module status
- :type status: str
- :returns: a list of FastQC module names with the given status
- :rtype: list of str
-
- """
- return [x.name for x in self._modules.values() if x.status == status]
-
- def _read_module(self, fp, line, start_mark):
- """Returns a list of lines in a module.
-
- :param fp: open file handle pointing to the FastQC data file
- :type fp: file handle
- :param line: first line in the module
- :type line: str
- :param start_mark: string denoting start of the module
- :type start_mark: str
- :returns: a list of lines in the module
- :rtype: list of str
-
- """
- raw = [line]
- while not line.startswith('>>END_MODULE'):
- line = fp.readline()
- raw.append(line)
-
- if not line:
- raise ValueError("Unexpected end of file in module %r" % line)
-
- return raw
-
- @property
- def modules(self):
- """All modules in the FastQC results."""
- return self._modules
-
- @property
- def passes(self):
- """All module names that pass QC."""
- return self._filter_by_status('pass')
-
- @property
- def passes_num(self):
- """How many modules have pass status."""
- return len(self.passes)
-
- @property
- def warns(self):
- """All module names with warning status."""
- return self._filter_by_status('warn')
-
- @property
- def warns_num(self):
- """How many modules have warn status."""
- return len(self.warns)
-
- @property
- def fails(self):
- """All names of failed modules."""
- return self._filter_by_status('fail')
-
- @property
- def fails_num(self):
- """How many modules failed."""
- return len(self.fails)
-
- @property
- def basic_statistics(self):
- """Basic statistics module results."""
- return self._modules['basic_statistics']
-
- @property
- def per_base_sequence_quality(self):
- """Per base sequence quality module results."""
- return self._modules['per_base_sequence_quality']
-
- @property
- def per_sequence_quality_scores(self):
- """Per sequence quality scores module results."""
- return self._modules['per_sequence_quality_scores']
-
- @property
- def per_base_sequence_content(self):
- """Per base sequence content module results."""
- return self._modules['per_base_sequence_content']
-
- @property
- def per_base_gc_content(self):
- """Per base GC content module results."""
- return self._modules['per_base_gc_content']
-
- @property
- def per_sequence_gc_content(self):
- """Per sequence GC content module results."""
- return self._modules['per_sequence_gc_content']
-
- @property
- def per_base_n_content(self):
- """Per base N content module results."""
- return self._modules['per_base_n_content']
-
- @property
- def sequence_length_distribution(self):
- """Per sequence length distribution module results."""
- return self._modules['sequence_length_distribution']
-
- @property
- def sequence_duplication_levels(self):
- """Sequence duplication module results."""
- return self._modules['sequence_duplication_levels']
-
- @property
- def overrepresented_sequences(self):
- """Overrepresented sequences module results."""
- return self._modules['overrepresented_sequences']
-
- @property
- def kmer_content(self):
- """Kmer content module results."""
- return self._modules['kmer_content']
-
-
-# HACK: remove this and use jinja2 only for templating
-class LongTable(object):
-
- """Class representing a longtable in LaTeX."""
-
- def __init__(self, caption, label, header, aln, colnum):
- self.lines = [
- "\\begin{center}",
- "\\captionof{table}{%s}" % caption,
- "\\label{%s}" % label,
- "\\begin{longtable}{%s}" % aln,
- "\\hline",
- "%s" % header,
- "\\hline \\hline",
- "\\endhead",
- "\\hline \\multicolumn{%i}{c}{\\textit{Continued on next page}}\\\\" % \
- colnum,
- "\\hline",
- "\\endfoot",
- "\\hline",
- "\\endlastfoot",
- ]
-
- def __str__(self):
- return "\n".join(self.lines)
-
- def add_row(self, row):
- self.lines.append(row)
-
- def end(self):
- self.lines.extend(["\\end{longtable}", "\\end{center}",
- "\\addtocounter{table}{-1}"])
-
-
-# filter functions for the jinja environment
-def nice_int(num, default="None"):
- if num is None:
- return default
- try:
- return locale.format("%i", int(num), grouping=True)
- except:
- return default
-
-
-def nice_flt(num, default="None"):
- if num is None:
- return default
- try:
- return locale.format("%.2f", float(num), grouping=True)
- except:
- return default
-
-
-def float2nice_pct(num, default="None"):
- if num is None:
- return default
- try:
- return locale.format("%.2f", float(num) * 100.0, grouping=True)
- except:
- return default
-
-
-# and some handy functions
-def natural_sort(inlist):
- key = lambda x: [int(a) if a.isdigit() else a.lower() for a in
- re.split("([0-9]+)", x)]
- inlist.sort(key=key)
- return inlist
-
-
-def write_template(run, template_file, logo_file):
-
- template_file = path.abspath(path.realpath(template_file))
- template_dir = path.dirname(template_file)
- # spawn environment and create output directory
- env = Environment(loader=FileSystemLoader(template_dir))
-
- # change delimiters since LaTeX may use "{{", "{%", or "{#"
- env.block_start_string = "((*"
- env.block_end_string = "*))"
- env.variable_start_string = "((("
- env.variable_end_string = ")))"
- env.comment_start_string = "((="
- env.comment_end_string = "=))"
-
- # trim all block-related whitespaces
- env.trim_blocks = True
- env.lstrip_blocks = True
-
- # put in out filter functions
- env.filters["nice_int"] = nice_int
- env.filters["nice_flt"] = nice_flt
- env.filters["float2nice_pct"] = float2nice_pct
- env.filters["basename"] = path.basename
-
- # write tex template for pdflatex
- jinja_template = env.get_template(path.basename(template_file))
- run.logo = logo_file
- render_vars = {
- "run": run,
- }
- rendered = jinja_template.render(**render_vars)
-
- print(rendered, file=sys.stdout)
-
-
-class GentrapLib(object):
-
- def __init__(self, run, sample, name, summary):
- assert isinstance(run, GentrapRun)
- assert isinstance(sample, GentrapSample)
- self.run = run
- self.sample = sample
- self.name = name
- self._raw = summary
- # flexiprep settings
- self.flexiprep = summary.get("flexiprep", {})
- self.flexiprep_files = summary.get("flexiprep", {}).get("files", {}).get("pipeline", {})
- self.clipping = not self.flexiprep["settings"]["skip_clip"]
- self.trimming = not self.flexiprep["settings"]["skip_trim"]
- self.is_paired_end = self.flexiprep["settings"]["paired"]
- if "fastqc_R1" in self.flexiprep["files"]:
- self.fastqc_r1_files = self.flexiprep["files"]["fastqc_R1"]
- self.fastqc_r1 = FastQC(self.fastqc_r1_files["fastqc_data"]["path"])
- if "fastqc_R2" in self.flexiprep["files"]:
- self.fastqc_r2_files = self.flexiprep["files"]["fastqc_R2"]
- self.fastqc_r2 = FastQC(self.fastqc_r2_files["fastqc_data"]["path"])
- if "fastqc_R1_qc" in self.flexiprep["files"]:
- self.fastqc_r1_qc_files = self.flexiprep["files"]["fastqc_R1_qc"]
- self.fastqc_r1_qc = FastQC(self.fastqc_r1_qc_files["fastqc_data"]["path"])
- if "fastqc_R2_qc" in self.flexiprep["files"]:
- self.fastqc_r2_qc_files = self.flexiprep["files"]["fastqc_R2_qc"]
- self.fastqc_r2_qc = FastQC(self.fastqc_r2_qc_files["fastqc_data"]["path"])
- # mapping metrics settings
- self.aln_metrics = summary.get("bammetrics", {}).get("stats", {}).get("CollectAlignmentSummaryMetrics", {})
- for k, v in self.aln_metrics.items():
- self.aln_metrics[k] = {a.lower(): b for a, b in v.items()}
- # insert size metrics files
- self.inserts_metrics_files = \
- summary.get("bammetrics", {}).get("files", {}).get("multi_metrics", {})
- # rna metrics files and stats
- self.rna_metrics_files = summary.get("bammetrics", {}).get("files", {}).get("rna", {})
- _rmetrics = summary.get("bammetrics", {}).get("stats", {}).get("rna", {})
- if _rmetrics:
- if "metrics" in _rmetrics:
- _rmetrics = _rmetrics["metrics"]
- if _rmetrics:
- _rmetrics = {k.lower(): v for k, v in _rmetrics.items() }
- self.rna_metrics = _rmetrics
- pf_bases = float(_rmetrics["pf_bases"])
- exonic_bases = int(_rmetrics.get("coding_bases", 0)) + int(_rmetrics.get("utr_bases", 0))
- # picard uses pct_ but it's actually ratio ~ we follow their convention
- pct_exonic_bases_all = exonic_bases / float(_rmetrics["pf_bases"])
- pct_exonic_bases = exonic_bases / float(_rmetrics.get("pf_aligned_bases", 0))
- self.rna_metrics.update({
- "exonic_bases": exonic_bases,
- "pct_exonic_bases_all": pct_exonic_bases_all,
- "pct_exonic_bases": pct_exonic_bases,
- "pct_aligned_bases": 1.0,
- "pct_aligned_bases_all": float(_rmetrics.get("pf_aligned_bases", 0.0)) / pf_bases,
- "pct_coding_bases_all": float(_rmetrics.get("coding_bases", 0.0)) / pf_bases,
- "pct_utr_bases_all": float(_rmetrics.get("utr_bases", 0.0)) / pf_bases,
- "pct_intronic_bases_all": float(_rmetrics.get("intronic_bases", 0.0)) / pf_bases,
- "pct_intergenic_bases_all": float(_rmetrics.get("intergenic_bases", 0.0)) / pf_bases,
- })
- if _rmetrics.get("ribosomal_bases", "") != "":
- self.rna_metrics["pct_ribosomal_bases_all"] = float(_rmetrics.get("pf_ribosomal_bases", 0.0)) / pf_bases
-
- def __repr__(self):
- return "{0}(sample=\"{1}\", lib=\"{2}\")".format(
- self.__class__.__name__, self.sample.name, self.name)
-
-
-class GentrapSample(object):
-
- def __init__(self, run, name, summary):
- assert isinstance(run, GentrapRun)
- self.run = run
- self.name = name
- self._raw = summary
- self.is_paired_end = summary.get("gentrap", {}).get("stats", {}).get("pipeline", {})["all_paired"]
- # mapping metrics settings
- self.aln_metrics = summary.get("bammetrics", {}).get("stats", {}).get("CollectAlignmentSummaryMetrics", {})
- for k, v in self.aln_metrics.items():
- self.aln_metrics[k] = {a.lower(): b for a, b in v.items()}
- # insert size metrics files
- self.inserts_metrics_files = \
- summary.get("bammetrics", {}).get("files", {}).get("multi_metrics", {})
- # rna metrics files and stats
- self.rna_metrics_files = summary.get("bammetrics", {}).get("files", {}).get("rna", {})
- _rmetrics = summary.get("bammetrics", {}).get("stats", {}).get("rna", {})
- if _rmetrics:
- if "metrics" in _rmetrics:
- _rmetrics = _rmetrics["metrics"]
- if _rmetrics:
- _rmetrics = {k.lower(): v for k, v in _rmetrics.items() }
- self.rna_metrics = _rmetrics
- pf_bases = float(_rmetrics["pf_bases"])
- exonic_bases = int(_rmetrics.get("coding_bases", 0)) + int(_rmetrics.get("utr_bases", 0))
- # picard uses pct_ but it's actually ratio ~ we follow their convention
- pct_exonic_bases_all = exonic_bases / float(_rmetrics["pf_bases"])
- pct_exonic_bases = exonic_bases / float(_rmetrics.get("pf_aligned_bases", 0))
- self.rna_metrics.update({
- "exonic_bases": exonic_bases,
- "pct_exonic_bases_all": pct_exonic_bases_all,
- "pct_exonic_bases": pct_exonic_bases,
- "pct_aligned_bases": 1.0,
- "pct_aligned_bases_all": float(_rmetrics.get("pf_aligned_bases", 0.0)) / pf_bases,
- "pct_coding_bases_all": float(_rmetrics.get("coding_bases", 0.0)) / pf_bases,
- "pct_utr_bases_all": float(_rmetrics.get("utr_bases", 0.0)) / pf_bases,
- "pct_intronic_bases_all": float(_rmetrics.get("intronic_bases", 0.0)) / pf_bases,
- "pct_intergenic_bases_all": float(_rmetrics.get("intergenic_bases", 0.0)) / pf_bases,
- })
- if _rmetrics.get("ribosomal_bases", "") != "":
- self.rna_metrics["pct_ribosomal_bases_all"] = float(_rmetrics.get("pf_ribosomal_bases", 0.0)) / pf_bases
-
- self.lib_names = sorted(summary["libraries"].keys())
- self.libs = \
- {l: GentrapLib(self.run, self, l, summary["libraries"][l]) \
- for l in self.lib_names}
-
- def __repr__(self):
- return "{0}(\"{1}\")".format(self.__class__.__name__, self.name)
-
-
-class GentrapRun(object):
-
- def __init__(self, summary_file):
-
- with open(summary_file, "r") as src:
- summary = json.load(src)
-
- self._raw = summary
- self.summary_file = summary_file
-
- self.files = summary["gentrap"].get("files", {}).get("pipeline", {})
- self.settings = summary["gentrap"]["settings"]
- self.version = self.settings.get("version", "unknown")
- # list containing all exes
- self.all_executables = summary["gentrap"]["executables"]
- # list containing exes we want to display
- executables = [
- ("cutadapt", "adapter clipping"),
- ("sickle", "base quality trimming"),
- ("fastqc", "sequence metrics collection"),
- ("gsnap", "alignment"),
- ("tophat", "alignment"),
- ("star", "alignment"),
- ("htseqcount", "fragment counting"),
- ]
- self.executables = {}
- for k, desc in executables:
- in_summary = self.all_executables.get(k)
- if in_summary is not None:
- self.executables[k] = in_summary
- self.executables[k]["desc"] = desc
- # since we get the version from the tools we use
- if self.all_executables.get("collectalignmentsummarymetrics") is not None:
- self.executables["picard"] = self.all_executables["collectalignmentsummarymetrics"]
- self.executables["picard"]["desc"] = "alignment_metrics_collection"
- # None means we are using the Queue built in Picard
- if self.executables["picard"].get("version") is None:
- self.executables["picard"]["version"] = "built-in"
- # since we get the version from the sub tools we use
- if self.all_executables.get("samtoolsview") is not None:
- self.executables["samtools"] = self.all_executables["samtoolsview"]
- self.executables["samtools"]["desc"] = "various post-alignment processing"
-
- self.sample_names = sorted(summary["samples"].keys())
- self.samples = \
- {s: GentrapSample(self, s, summary["samples"][s]) \
- for s in self.sample_names}
- self.libs = []
- for sample in self.samples.values():
- self.libs.extend(sample.libs.values())
- if all([s.is_paired_end for s in self.samples.values()]):
- self.lib_type = "all paired end"
- elif all([not s.is_paired_end for s in self.samples.values()]):
- self.lib_type = "all single end"
- else:
- self.lib_type = "mixed (single end and paired end)"
-
- def __repr__(self):
- return "{0}(\"{1}\")".format(self.__class__.__name__,
- self.summary_file)
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument("summary_file", type=str,
- help="Path to Gentrap summary file")
- parser.add_argument("template_file", type=str,
- help="Path to main template file")
- parser.add_argument("logo_file", type=str,
- help="Path to main logo file")
- args = parser.parse_args()
-
- run = GentrapRun(args.summary_file)
- write_template(run, args.template_file, args.logo_file)
-
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/plot_pca.R b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/plot_pca.R
deleted file mode 100755
index 419ec5b2cb30baf2cbde032b7e5ba11abba6c305..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/plot_pca.R
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/usr/bin/env Rscript
-#
-# Script for plotting PCA plots for the Gentrap pipeline
-
-
-# General function to install package if it does not exist
-# Otherwise, it only loads the package
-usePackage <- function(p) {
- r <- getOption("repos")
- r["CRAN"] <- "http://cran.us.r-project.org"
- options(repos = r)
- rm(r)
- if (!is.element(p, installed.packages()[,1]))
- install.packages(p, dep = TRUE)
- require(p, character.only = TRUE)
-}
-
-usePackage("getopt")
-usePackage("edgeR")
-usePackage("ggplot2")
-usePackage("gplots")
-usePackage("grid")
-usePackage("jsonlite")
-usePackage("reshape2")
-usePackage("MASS")
-usePackage("RColorBrewer")
-
-# create spec for arg parsing
-spec <- matrix(c(
- # input table (merge of all samples)
- 'input-table', 'I', 1, 'character',
- # output plot file
- 'output-plot', 'O', 1, 'character',
- # perform TMM-normalization (only if we are dealing with count data)
- 'tmm-normalize', 'T', 0, 'logical'
- # help
- 'help', 'H', 0, 'logical'
-), byrow=TRUE, ncol=4)
-opt <- getopt(spec)
-
-# print help if requested
-if (!is.null(opt[['help']])) {
- cat(getopt(spec, usage=TRUE))
- q(status=1)
-}
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/rna_metrics.py b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/rna_metrics.py
deleted file mode 100755
index 862bf7e0134a0b403cc925d69ce21f8d713ccedd..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/rna_metrics.py
+++ /dev/null
@@ -1,517 +0,0 @@
-#!/usr/bin/env python2
-#
-# Biopet is built on top of GATK Queue for building bioinformatic
-# pipelines. It is mainly intended to support LUMC SHARK cluster which is running
-# SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
-# should also be able to execute Biopet tools and pipelines.
-#
-# Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
-#
-# Contact us at: sasc@lumc.nl
-#
-# A dual licensing mode is applied. The source code within this project that are
-# not part of GATK Queue is freely available for non-commercial use under an AGPL
-# license; For commercial users or users who do not want to follow the AGPL
-# license, please contact us to obtain a separate license.
-#
-
-
-# rna_metrics.py
-#
-# Given a sorted, indexed BAM file from an RNA seq experiment,
-# output the annotation metrics using Picard CollectRnaSeqMetrics per chromosome
-
-import argparse
-import json
-import functools
-import locale
-import os
-import subprocess
-import threading
-import time
-import tempfile
-import warnings
-import Queue
-
-# valid column names
-# from http://picard.sourceforge.net/picard-metric-definitions.shtml#RnaSeqMetrics
-COL_NAMES = {
- 'PF_BASES': 'pfBases',
- 'PF_ALIGNED_BASES': 'pfAlignedBases',
- 'RIBOSOMAL_BASES': 'ribosomalBases',
- 'CODING_BASES': 'codingBases',
- 'UTR_BASES': 'utrBases',
- 'INTRONIC_BASES': 'intronicBases',
- 'INTERGENIC_BASES': 'intergenicBases',
- 'IGNORED_READS': 'ignoredReads',
- 'CORRECT_STRAND_READS': 'correctStrandReads',
- 'INCORRECT_STRAND_READS': 'incorrectStrandReads',
- 'PCT_RIBOSOMAL_BASES': 'pctRibosomalBases',
- 'PCT_CODING_BASES': 'pctCodingBases',
- 'PCT_UTR_BASES': 'pctUtrBases',
- 'PCT_INTRONIC_BASES': 'pctIntronicBases',
- 'PCT_INTERGENIC_BASES': 'pctIntergenicBases',
- 'PCT_MRNA_BASES': 'pctMrnaBases',
- 'PCT_USABLE_BASES': 'pctUsableBases',
- 'PCT_CORRECT_STRAND_READS': 'pctCorrectStrandReads',
- 'MEDIAN_CV_COVERAGE': 'medianCvCoverage',
- 'MEDIAN_5PRIME_BIAS': 'median5PrimeBias',
- 'MEDIAN_3PRIME_BIAS': 'median3PrimeBias',
- 'MEDIAN_5PRIME_TO_3PRIME_BIAS': 'median5PrimeTo3PrimeBias',
-}
-# executables, default to ones in PATH
-EXE_SAMTOOLS = 'samtools'
-EXE_JAVA = 'java'
-
-# set locale to group digits
-locale.setlocale(locale.LC_ALL, '')
-int_fmt = functools.partial(locale.format, grouping=True, percent='%i')
-float_fmt = functools.partial(locale.format, grouping=True, percent='%.2f')
-
-
-class MetricsTracker(object):
-
- """Class to track metrics file output."""
-
- def __init__(self, main_bams, chrs):
- self.lock = threading.Lock()
- self.chrs = chrs
- self.files = {}
- for rtype, bam in main_bams.items():
- self.files[bam] = {
- 'chrs': dict.fromkeys(chrs),
- 'strand': rtype,
- }
-
- def add_stat_file(self, main_bam, chr, chr_stat):
- self.lock.acquire()
- self.files[main_bam]['chrs'][chr] = chr_stat
- self.lock.release()
-
- def check_files(self):
- # only for strand-specific rna-seq
- for bam, data in self.files.items():
- for chr, path in data['chrs'].items():
- assert path is not None, "Missing statistics file for {0}, " \
- "chromosome {1}".format(bam, chr)
-
-
-class Worker(threading.Thread):
-
- """Class representing worker to execute jobs."""
-
- def __init__(self, queue, group=None, target=None, name=None, args=(),
- kwargs={}):
- threading.Thread.__init__(self, group, target, name, args, kwargs)
- self.queue = queue
- self.setDaemon(True)
- self.start()
-
- def run(self):
- while True:
- func, args, kwargs = self.queue.get()
- func(*args, **kwargs)
- self.queue.task_done()
-
-
-class ThreadPool(object):
-
- """Class representing thread pool to execute."""
-
- def __init__(self, num_threads):
- self.queue = Queue.Queue()
- for _ in range(num_threads):
- Worker(self.queue)
-
- def add_task(self, func, *args, **kwargs):
- self.queue.put((func, args, kwargs))
-
- def wait_completion(self):
- self.queue.join()
-
-
-def picard_metrics_worker(in_bam, chr, tracker, annot, jar,
- samtools_exe, java_exe):
- """Worker for collecting RNA-seq metrics."""
- # check if index exists
- assert os.path.exists(in_bam + '.bai')
- # create output directory, contains all chr stats
- out_dir = os.path.splitext(in_bam)[0] + '.rna_metrics'
- # create output directory
- try:
- os.makedirs(out_dir)
- except OSError:
- if not os.path.exists(out_dir):
- raise
- # if chr is none, do stat on all regions
- if chr is None:
- chr = 'ALL'
- out_stat = os.path.join(out_dir, chr + '.rna_metrics.txt')
- # split BAM file per chr, write to tmp file
- if chr != 'ALL':
- bam = tempfile.NamedTemporaryFile(prefix='tmp_rna_metrics_', delete=True)
- name = bam.name
- tokens = [samtools_exe, 'view', '-bh', '-o', bam.name, in_bam, chr]
- proc = subprocess.Popen(tokens, stdout=bam)
- while proc.poll() is None:
- time.sleep(1)
- else:
- name = in_bam
- picard_toks = [java_exe, '-jar', jar]
- for key, value in os.environ.items():
- if key.startswith('OPT_PICARD_COLLECTRNASEQMETRICS_'):
- # input, output, and annotation are handled separately
- if key.endswith('INPUT') or key.endswith('OUTPUT') or \
- key.endswith('REF_FLAT'):
- continue
- if value:
- picard_toks.append('%s=%s' %
- (key.replace('OPT_PICARD_COLLECTRNASEQMETRICS_', ''), value))
-
- picard_toks += ['REF_FLAT={0}'.format(annot),
- 'STRAND_SPECIFICITY=SECOND_READ_TRANSCRIPTION_STRAND',
- 'I={0}'.format(name), 'O={0}'.format(out_stat)]
- picard = subprocess.Popen(picard_toks)
- while picard.poll() is None:
- time.sleep(1)
- assert os.path.exists(out_stat)
- if chr != 'ALL':
- bam.close()
- tracker.add_stat_file(in_bam, chr, out_stat)
-
-
-def samtools_reads_per_region_count(bam_files, chrs, samtools_exe):
- """Counts read per chromosome using samtools (simple count of mapped read
- per region."""
- tokens = [samtools_exe, 'view', '-c', '-F', '0x4']
- keys = ['fwd', 'rev', 'mix']
- all_dict = dict.fromkeys(keys)
- for rtype, bam in bam_files.items():
- assert rtype in keys, "Unknown key: {0}".format(rtype)
- aggr_dict = {}
- for chr in chrs:
- if chr == 'ALL':
- continue
- proc = subprocess.Popen(tokens + [bam, chr], stdout=subprocess.PIPE)
- count = int(proc.stdout.read())
- aggr_dict[chr] = {
- 'metrics': {'countMapped': count}
- }
- name = os.path.basename(os.path.splitext(bam)[0])
- all_dict[rtype] = {}
- all_dict[rtype]['bamFile'] = name
- all_dict[rtype]['allMetrics'] = aggr_dict
-
- return all_dict
-
-
-def picard_reads_per_region_count(bam_files, chrs, annot, jar, samtools_exe, java_exe):
- """Counts read per chromosome using Picard and annotation files."""
- assert os.path.exists(annot), "Annotation file {0} not found".format(annot)
- # only analyze sense and antisense reads
- bam_files = {'fwd': bam_files['fwd'], 'rev': bam_files['rev']}
- # create tracker for metric files
- metrics_tracker = MetricsTracker(bam_files, chrs)
- # create main task pool
- metrics_pool = ThreadPool(args.threads)
- # add tasks to the pool
- for bam in bam_files.values():
- for chr in chrs:
- metrics_pool.add_task(picard_metrics_worker, in_bam=bam, chr=chr,
- tracker=metrics_tracker, annot=annot, jar=jar,
- samtools_exe=samtools_exe, java_exe=java_exe)
- metrics_pool.wait_completion()
- # checks whether all required stat files are present
- metrics_tracker.check_files()
- return aggregate_metrics(metrics_tracker, chrs)
-
-
-def prep_bam_file(bams, strand_spec, samtools_exe):
- """Index input BAM files and return a dictionary of BAM files to process."""
- for in_bam in in_bams.values():
- bam = os.path.abspath(in_bam)
- assert os.path.exists(bam), "File {0} does not exist".format(in_bam)
- if not os.path.exists(bam + '.bai'):
- subprocess.call([samtools_exe, 'index', bam])
- return bams
-
-
-def parse_metrics_file(metrics_path):
- """Given a path to a Picard CollectRnaSeqMetrics output file, return a
- dictionary consisting of its column, value mappings.
- """
- data_mark = 'PF_BASES'
- tokens = []
- with open(metrics_path, 'r') as source:
- line = source.readline().strip()
- fsize = os.fstat(source.fileno()).st_size
- while True:
- if not line.startswith(data_mark):
- # encountering EOF before metrics is an error
- if source.tell() == fsize:
- raise ValueError("Metrics not found inside %r" % \
- metrics_path)
- line = source.readline().strip()
- else:
- break
-
- assert line.startswith(data_mark)
- # split header line and append to tokens
- tokens.append(line.split('\t'))
- # and the values (one row after)
- tokens.append(source.readline().strip().split('\t'))
- data = {}
- for col, value in zip(tokens[0], tokens[1]):
- if not value:
- data[COL_NAMES[col]] = None
- elif col.startswith('PCT') or col.startswith('MEDIAN'):
- if value != '?':
- data[COL_NAMES[col]] = float(value)
- else:
- warnings.warn("Undefined value for %s in %s: %s" % (col,
- metrics_path, value))
- data[COL_NAMES[col]] = None
- else:
- assert col in COL_NAMES, 'Unknown column: %s' % col
- data[COL_NAMES[col]] = int(value)
-
- return data
-
-
-def write_json(out_file, data, **kwargs):
- with open(out_file, 'w') as jsonfile:
- json.dump(data, jsonfile, sort_keys=True, indent=4,
- separators=(',', ': '))
-
-
-def write_html(out_file, data, chrs, is_strand_spec):
- if is_strand_spec:
- table_func = build_table_html_ss
- tpl = open(prog_path('rna_metrics.html')).read()
- else:
- table_func = build_table_html_nonss
- tpl = open(prog_path('rna_metrics_nonss.html')).read()
-
- html_data = table_func(data, chrs)
- with open(out_file, 'w') as htmlfile:
- htmlfile.write(tpl.format(**html_data))
-
-
-def get_base_counts(metrics):
- res = {
- 'total': metrics['pfAlignedBases'],
- 'exonic': metrics['utrBases'] + metrics['codingBases'],
- 'intronic': metrics['intronicBases'],
- 'intergenic': metrics['intergenicBases'],
- }
- # count percentages
- for reg in ('exonic', 'intronic', 'intergenic'):
- res[reg + '_pct'] = res[reg] * 100.0 / res['total']
- # format for display
- for key, value in res.items():
- if key.endswith('_pct'):
- res[key] = float_fmt(value=value)
- else:
- res[key] = int_fmt(value=value)
-
- return res
-
-
-def build_table_html_nonss(data, chrs):
- assert data['mix'] is not None and (data['fwd'] is None and data['rev'] is
- None), "Invalid data %r" % data
- mix = data['mix']['allMetrics']
- read_table = [
- '<table>',
- '<tr>',
- '<th>Chromosome</th>',
- '<th>Reads</th>',
- '</tr>'
- ]
- rrow_tpl = '<tr><td>{0}</td><td>{1}</td>'
- for chr in chrs:
- # not showing all stats in table, per chr only
- if chr == 'ALL':
- continue
- count = int_fmt(value=mix[chr]['metrics']['countMapped'])
- read_table.append(rrow_tpl.format(chr, count))
- read_table.append('</table>')
-
- return {'table_read_count': '\n'.join(read_table),
- 'css': open(prog_path('rna_metrics.css')).read()}
-
-
-def build_table_html_ss(data, chrs):
- read_table = [
- '<table>',
- '<tr>',
- '<th rowspan="2">Chromosome</th>',
- '<th>Mapped</th>',
- '<th>Sense Annotation Only</th>',
- '<th>Antisense Annotation Only</th>',
- '</tr>'
- '<tr>',
- '<th>Both strands</th>',
- '<th><green>Sense</green> / <red>Antisense</red> Reads</th>',
- '<th><green>Antisense</green> / <red>Sense</red> Reads</th>',
- '</tr>'
- ]
- rrow_tpl = '<tr><td>{0}</td><td>{1}</td><td>{2}</td><td>{3}</td></tr>'
- rcell_tpl = '<green>{0}</green> / <red>{1}</red>'
-
- fwd_data, rev_data = data['fwd']['allMetrics'], data['rev']['allMetrics']
- mix_data = data['mix']['allMetrics']
- for chr in chrs:
- # not showing all stats in table, per chr only
- if chr == 'ALL':
- continue
- cur_fwd = fwd_data[chr]['metrics']
- cur_rev = rev_data[chr]['metrics']
- mix_count = mix_data[chr]['metrics']['countMapped']
- sense = rcell_tpl.format(
- int_fmt(value=cur_fwd['correctStrandReads']),
- int_fmt(value=cur_fwd['incorrectStrandReads']))
- antisense = rcell_tpl.format(
- int_fmt(value=cur_rev['correctStrandReads']),
- int_fmt(value=cur_rev['incorrectStrandReads']))
- read_table.append(rrow_tpl.format(chr, int_fmt(value=mix_count),
- sense, antisense))
- read_table.append('</table>')
-
- base_table = [
- '<table>',
- '<tr>',
- '<th rowspan="2">Region</th>',
- '<th colspan="2">Sense Annotation</th>',
- '<th colspan="2">Antisense Annotation</th>',
- '</tr>'
- '<tr>',
- '<th>Count</th>', '<th>%</th>',
- '<th>Count</th>', '<th>%</th>',
- '</tr>'
- ]
- crow_tpl = [
- '<tr>',
- '<td>{0}</td>', '<td>{1}</td>',
- '<td>{2}</td>', '<td>{3}</td>',
- '<td>{4}</td>',
- '</tr>',
- ]
- crow_tpl = ''.join(crow_tpl)
- fwd_bcounts = get_base_counts(fwd_data['ALL']['metrics'])
- rev_bcounts = get_base_counts(rev_data['ALL']['metrics'])
- for reg in ('exonic', 'intronic', 'intergenic'):
- pct = reg + '_pct'
- base_table.append(
- crow_tpl.format(reg.capitalize(), fwd_bcounts[reg],
- fwd_bcounts[pct], rev_bcounts[reg],
- rev_bcounts[pct]))
- base_table.append('</table>')
-
- return {'table_read_count': '\n'.join(read_table),
- 'table_base_count': '\n'.join(base_table),
- 'css': open(prog_path('rna_metrics.css')).read()}
-
-
-def aggregate_metrics(tracker, chrs):
- """Aggregates all RNA seq metrics data into a single file."""
- all_dict = {}
- keys = ['fwd', 'rev', 'mix']
- all_dict = dict.fromkeys(keys)
- for bam, stats in tracker.files.items():
- assert stats['strand'] in keys
- aggr_dict = {}
- for chr, source in stats['chrs'].items():
- aggr_dict[chr] = {
- 'fileName': os.path.basename(source),
- 'metrics': parse_metrics_file(source),
- }
- name = os.path.basename(os.path.splitext(bam)[0])
- all_dict[stats['strand']] = {}
- all_dict[stats['strand']]['bamFile'] = name
- all_dict[stats['strand']]['allMetrics'] = aggr_dict
-
- return all_dict
-
-
-def prog_path(fname):
- prog_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)))
- return os.path.join(prog_dir, fname)
-
-
-if __name__ == '__main__':
- # params:
- # main bam file
- # thread num
- # option to compare with annotation source (produces correct/incorrect counts)
- # strand specific or not strand specific
- # path to picard's collectrnaseqmetrics
- # json output path
- # samtools binary (default: environment samtools)
- # java binary (default: environment java)
- # picard options:
- parser = argparse.ArgumentParser()
-
- parser.add_argument('m_bam', type=str,
- help='Path to BAM file containing both sense and antisense reads')
- parser.add_argument('--s-bam', dest='s_bam', type=str,
- help='Path to BAM file containing sense reads')
- parser.add_argument('--as-bam', dest='as_bam', type=str,
- help='Path to BAM file containing antisense reads')
- parser.add_argument('-o', '--outfile', dest='out_file', type=str,
- help='Path to output file')
- parser.add_argument('-t', '--threads', dest='threads',
- default=1, type=int, help='Number of threads to use')
- parser.add_argument('--chrs', dest='chrs',
- default=prog_path('chrs.txt'),
- help='Path to file containing chromosome names')
- parser.add_argument('-a', '--annotation', dest='annot',
- help='Annotation source')
- parser.add_argument('--html', dest='is_html',
- action='store_true',
- help='Output HTML file')
- parser.add_argument('--jar', dest='jar', type=str,
- help='Path to Picard\'s CollectRnaSeqMetrics.jar')
- parser.add_argument('--java', dest='java', type=str,
- default=EXE_JAVA,
- help='Path to java executable')
- parser.add_argument('--samtools', dest='samtools', type=str,
- default=EXE_SAMTOOLS,
- help='Path to samtools executable')
-
- args = parser.parse_args()
-
- if args.s_bam is not None and args.as_bam is not None:
- is_strand_spec = True
- in_bams = {'mix': args.m_bam, 'fwd': args.s_bam, 'rev': args.as_bam}
- elif args.s_bam is None and args.as_bam is None:
- is_strand_spec = False
- in_bams = {'mix': args.m_bam}
- else:
- raise ValueError("Incomplete argument: either sense or antisense BAM "
- "files are not specified.")
-
- chrs = [line.strip() for line in open(args.chrs, 'r')] + ['ALL']
- # check for paths and indices
- bam_files = prep_bam_file(in_bams, is_strand_spec, args.samtools)
- # use picard and samtools if it's strand-specific
- if is_strand_spec:
- aggr_data = picard_reads_per_region_count(bam_files, chrs, args.annot,
- args.jar, args.samtools, args.java)
- sam_data = samtools_reads_per_region_count(bam_files, chrs,
- args.samtools)
- aggr_data['mix'] = sam_data['mix']
- # otherwise use samtools only
- else:
- aggr_data = samtools_reads_per_region_count(bam_files, chrs,
- args.samtools)
-
- # write to output file
- if args.out_file is None:
- ext = '.html' if args.is_html else '.json'
- out_file = 'rna_metrics_out' + ext
- else:
- out_file = args.out_file
-
- write_func = write_html if args.is_html else write_json
- write_func(out_file, aggr_data, chrs=chrs, is_strand_spec=is_strand_spec)
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/gentrap_front.png b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/gentrap_front.png
deleted file mode 100644
index 7ecf40f6998c6b6ce833d1f352efe672b4e32859..0000000000000000000000000000000000000000
Binary files a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/gentrap_front.png and /dev/null differ
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib.tex b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib.tex
deleted file mode 100644
index 7650cbc6bb0125aadcdaa78ece93436cde2c0551..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib.tex
+++ /dev/null
@@ -1,32 +0,0 @@
-\section{Library "((( lib.name )))" Results}
-\label{lib:(((lib.name)))}
-
-\subsection{Input information}
-\label{sec:seq}
-
-\begin{center}
- \captionof{table}{Input files}
- \label{tab:annotfiles}
- \begin{longtable}{ l l p{0.4\textwidth} }
- \hline
- File & Checksum & Name\\
- \hline \hline
- \endhead
- \hline
- \multicolumn{3}{c}{\textit{Continued on next page}}\\
- \hline
- \endfoot
- \hline
- \endlastfoot
- Read 1 file & ((( lib.flexiprep_files.input_R1.md5|truncate(7, True, "") ))) & ((( lib.flexiprep_files.input_R1.path|basename )))\\
- ((* if lib.flexiprep_files.input_R2 *))
- Read 2 file & ((( lib.flexiprep_files.input_R2.md5|truncate(7, True, "") ))) & ((( lib.flexiprep_files.input_R2.path|basename )))\\
- ((* endif *))
- \end{longtable}
-\end{center}
-% HACK: to keep table counters in sync
-\addtocounter{table}{-1}
-
-((* include "lib_mapping.tex" *))
-\clearpage
-((* include "lib_seqeval.tex" *))
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib_mapping.tex b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib_mapping.tex
deleted file mode 100644
index 39ec179065ba964b6c9cf31af5451ee6227f827f..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib_mapping.tex
+++ /dev/null
@@ -1,112 +0,0 @@
-\subsection{Mapping}
-\label{sec:map-((( lib.sample.name )))-((( lib.name )))}
-
-\subsubsection{Mapping statistics}
-
-\indent
-
-% number + percentage of reads mapped to genome
-% number + percentage of properly paired reads
-\begin{center}
- \captionof{table}{Mapping Overview}
- \label{tab:bamstat-((( lib.sample.name )))-((( lib.name )))}
- \setlength{\tabcolsep}{11pt}
- ((* if lib.is_paired_end *))
- \begin{tabular}{ l r r r }
- \hline
- \multirow{2}{*}{Parameter} & \multicolumn{1}{c}{All Pairs} & \multicolumn{1}{c}{First in Pairs} & \multicolumn{1}{c}{Second in Pairs} \\
- & Value & Value & Value \\
- \hline \hline
- Total reads & ((( lib.aln_metrics.PAIR.total_reads|nice_int ))) & ((( lib.aln_metrics.FIRST_OF_PAIR.total_reads|nice_int ))) & ((( lib.aln_metrics.SECOND_OF_PAIR.total_reads|nice_int ))) \\
- Mean read length & ((( lib.aln_metrics.PAIR.mean_read_length|nice_flt ))) & ((( lib.aln_metrics.FIRST_OF_PAIR.mean_read_length|nice_flt ))) & ((( lib.aln_metrics.SECOND_OF_PAIR.mean_read_length|nice_flt ))) \\
- Strand balance & ((( lib.aln_metrics.PAIR.strand_balance|nice_flt ))) & ((( lib.aln_metrics.FIRST_OF_PAIR.strand_balance|nice_flt ))) & ((( lib.aln_metrics.SECOND_OF_PAIR.strand_balance|nice_flt ))) \\
- \% Mapped to reference & ((( lib.aln_metrics.PAIR.pct_pf_reads_aligned|float2nice_pct )))\% & ((( lib.aln_metrics.FIRST_OF_PAIR.pct_pf_reads_aligned|float2nice_pct )))\% & ((( lib.aln_metrics.SECOND_OF_PAIR.pct_pf_reads_aligned|float2nice_pct )))\% \\
- \% Mapped to reference (MAPQ >= 20) & ((( lib.aln_metrics.PAIR.pct_pf_reads_aligned|float2nice_pct )))\% & ((( lib.aln_metrics.FIRST_OF_PAIR.pct_pf_reads_aligned|float2nice_pct )))\% & ((( lib.aln_metrics.SECOND_OF_PAIR.pct_pf_reads_aligned|float2nice_pct )))\% \\
- Mismatch rate & ((( lib.aln_metrics.PAIR.pf_mismatch_rate|float2nice_pct )))\% & ((( lib.aln_metrics.FIRST_OF_PAIR.pf_mismatch_rate|float2nice_pct )))\% & ((( lib.aln_metrics.SECOND_OF_PAIR.pf_mismatch_rate|float2nice_pct )))\% \\
- Indel rate & ((( lib.aln_metrics.PAIR.pf_indel_rate|float2nice_pct )))\% & ((( lib.aln_metrics.FIRST_OF_PAIR.pf_indel_rate|float2nice_pct )))\% & ((( lib.aln_metrics.SECOND_OF_PAIR.pf_indel_rate|float2nice_pct )))\% \\
- Chimeras & ((( lib.aln_metrics.PAIR.pct_chimeras|float2nice_pct )))\% & ((( lib.aln_metrics.FIRST_OF_PAIR.pct_chimeras|float2nice_pct )))\% & ((( lib.aln_metrics.SECOND_OF_PAIR.pct_chimeras|float2nice_pct )))\% \\
- \hline
- ((* else *))
- \begin{tabular}{ l r }
- \hline
- \multirow{1}{*}{Parameter} & \multicolumn{1}{c}{Value} \\
- \hline \hline
- Total reads & ((( lib.aln_metrics.UNPAIRED.total_reads|nice_int ))) \\
- Mean read length & ((( lib.aln_metrics.UNPAIRED.mean_read_length|nice_flt ))) \\
- Strand balance & ((( lib.aln_metrics.UNPAIRED.strand_balance|nice_flt ))) \\
- \% Mapped to reference & ((( lib.aln_metrics.UNPAIRED.pct_pf_reads_aligned|float2nice_pct )))\% \\
- \% Mapped to reference (MAPQ >= 20) & ((( lib.aln_metrics.UNPAIRED.pct_pf_reads_aligned|float2nice_pct )))\% \\
- Mismatch rate & ((( lib.aln_metrics.UNPAIRED.pf_mismatch_rate|float2nice_pct )))\% \\
- Indel rate & ((( lib.aln_metrics.UNPAIRED.pf_indel_rate|float2nice_pct )))\% \\
- \hline
- ((* endif *))
- \end{tabular}
-\end{center}
-
-((* if lib.is_paired_end *))
-% inferred insert size distribution
-\subsubsection{Insert size distribution}
-
-\IfFileExists{((( lib.inserts_metrics_files.insert_size_histogram.path )))}
-{
- \begin{figure}[h!]
- \centering
- \includegraphics[width=0.7\textwidth]{((( lib.inserts_metrics_files.insert_size_histogram.path )))}
- \caption{Distribution of insert size length of paired-end reads mapped to opposite strands.}
- \end{figure}
-}
-((= TODO: strand-specific stats
-%{
-% \IfFileExists{((( vars['OUT_DIR'] )))/((( vars['SAMPLE'] ))).f.insertsizes.png}
-% {
-% \begin{figure}[h!]
-% \centering
-% \includegraphics[width=0.7\textwidth]{((( vars['OUT_DIR'] )))/((( vars['SAMPLE'] ))).f.insertsizes.png}
-% \caption{Distribution of insert size length of paired-end reads whose first read maps to the minus strand.}
-% \end{figure}
-% }{}
-% \IfFileExists{((( vars['OUT_DIR'] )))/((( vars['SAMPLE'] ))).r.insertsizes.png}
-% {
-% \begin{figure}[h!]
-% \centering
-% \includegraphics[width=0.7\textwidth]{((( vars['OUT_DIR'] )))/((( vars['SAMPLE'] ))).r.insertsizes.png}
-% \caption{Distribution of insert size length of paired-end reads whose first read maps to the plus strand.}
-% \end{figure}
-% }{}
-%}
-=))
-((* endif *))
-
-\subsubsection{RNA-specific metrics}
-
-\IfFileExists{((( lib.rna_metrics_files.output_chart.path )))}
-{
- \begin{figure}[h!]
- \centering
- \includegraphics[width=0.7\textwidth]{((( lib.rna_metrics_files.output_chart.path )))}
- \caption{Normalized coverage bias plot.}
- \end{figure}
-}
-
-\begin{center}
- \captionof{table}{Functional annotation metrics}
- \label{tab:fannot-((( lib.sample.name )))-((( lib.name ))))}
- \setlength{\tabcolsep}{11pt}
- \begin{tabular}{ l r r r }
- \hline
- \multirow{2}{*}{Parameter} & \multicolumn{3}{c}{Value} \\
- & Count & \% of all & \% of aligned \\
- \hline \hline
- Total bases & ((( lib.rna_metrics.pf_bases|nice_int ))) & 100\% & - \\
- Aligned bases & ((( lib.rna_metrics.pf_aligned_bases|nice_int ))) & ((( lib.rna_metrics.pct_aligned_bases_all|float2nice_pct )))\% & ((( lib.rna_metrics.pct_aligned_bases|float2nice_pct )))\% \\
- Exonic bases & ((( lib.rna_metrics.exonic_bases|nice_int ))) & ((( lib.rna_metrics.pct_exonic_bases_all|float2nice_pct )))\% & ((( lib.rna_metrics.pct_exonic_bases|float2nice_pct )))\% \\
- \hspace*{4mm}Coding bases & ((( lib.rna_metrics.coding_bases|nice_int ))) & ((( lib.rna_metrics.pct_coding_bases_all|float2nice_pct )))\% & ((( lib.rna_metrics.pct_coding_bases|float2nice_pct )))\% \\
- \hspace*{4mm}UTR bases & ((( lib.rna_metrics.utr_bases|nice_int ))) & ((( lib.rna_metrics.pct_utr_bases_all|float2nice_pct )))\% & ((( lib.rna_metrics.pct_utr_bases|float2nice_pct )))\% \\
- Intronic bases & ((( lib.rna_metrics.intronic_bases|nice_int ))) & ((( lib.rna_metrics.pct_intronic_bases_all|float2nice_pct )))\% & ((( lib.rna_metrics.pct_intronic_bases|float2nice_pct )))\% \\
- Intergenic bases & ((( lib.rna_metrics.intergenic_bases|nice_int ))) & ((( lib.rna_metrics.pct_intergenic_bases_all|float2nice_pct )))\% & ((( lib.rna_metrics.pct_intergenic_bases|float2nice_pct )))\% \\
- ((* if lib.rna_metrics.ribosomal_bases != "" *))
- Ribosomal bases & ((( lib.rna_metrics.ribosomal_bases|nice_int ))) & ((( lib.rna_metrics.pct_ribosomal_bases_all|float2nice_pct )))\% & ((( lib.rna_metrics.pct_ribosomal_bases|float2nice_pct )))\% \\
- ((* endif *))
- \hline
- \end{tabular}
-\end{center}
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib_seqeval.tex b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib_seqeval.tex
deleted file mode 100644
index 8e28e56e9defea0295be0a38005b4bcc6b68e59c..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/lib_seqeval.tex
+++ /dev/null
@@ -1,456 +0,0 @@
-\subsection{Sequencing Results Evaluation}
-\label{sec:seq}
-
-This section contains statistics of the raw sequencing results. Statistics of
-the preprocessing step(s) may be shown as well, depending on which preprocessing
-steps were performed.
-(Table~\ref{tab:pipelineparams}).
-
-\indent
-
-All statistics, except for the per sequence \%GC graph, were collected using
-FastQC. Visit the
-\href{http://www.bioinformatics.babraham.ac.uk/projects/fastqc/}{FastQC website}
-for more detailed explanations of them.
-
-\subsubsection{Overview}
-\label{subsec:seq-overview}
-There are four types of preprocessing that may be done:
-
-\begin{description}
- \item[\textit{none}] \hfill \\
- No preprocessing step.
- \item[\textit{adapter clipping}] \hfill \\
- Removal of known adapter sequences present in the sequencing
- reads. The list of sequences are retrieved from the FastQC contaminant
- list, which is packaged with the FastQC released used in this pipeline.
- \item[\textit{quality trimming}] \hfill \\
- Removal of all low-quality bases that are often found in the 5'
- or 3' ends of the reads.
- \item[\textit{adapter clipping followed by quality trimming}] \hfill \\
- Both adapter clipping and base quality trimming.
-\end{description}
-\indent
-Your chosen preprocessing method was:\textbf{
-((* if lib.clipping *))adapter clipping
-((* elif lib.trimming *))quality trimming
-((* elif lib.clipping and lib.trimming *))adapter clipping followed by quality trimming((* endif *))}.
-
-((* if lib.clipping *))
-\subsubsection{Adapter removal}
-Known adapter sequences found in the raw data are listed in
-Table~\ref{tab:adapters}. For each adapter sequence, the count of its
-occurence (partially or whole) is also listed. The presence of these
-adapters do not always result in the FASTQ records to be discarded.
-FASTQ records are only discarded if clipping of the adapter sequence
-results in sequences shorter than the threshold set in cutadapt.
-
-\indent
-
-For the complete list of known adapter sequences, consult the
-\href{http://www.bioinformatics.babraham.ac.uk/projects/fastqc/}{official FastQC website}.
-More information about clipping using cutadapt is available on the
-\href{https://code.google.com/p/cutadapt/}{official cutadapt website}.
-
-%\ClipContamTable
-\begin{center}
- \captionof{table}{Adapter Sequences Present in the Sample}
- \label{tab:adapters}
- \begin{longtable}{ p{14mm} r p{0.4\textwidth} r }
- \hline
- Read & Discarded & Adapter & Occurence\\
- \hline \hline
- \endhead
- \hline
- \multicolumn{4}{c}{\textit{Continued on next page}}\\
- \hline
- \endfoot
- \hline
- \endlastfoot
- ((* if lib.flexiprep.stats.clipping_R1 *))
- ((* if lib.flexiprep.stats.clipping_R1.adapters *))
- Read 1 & ((( lib.flexiprep.stats.clipping_R1.num_reads_affected|nice_int )))
- ((* for adapter, stat in lib.flexiprep.stats.clipping_R1.adapters.iteritems() *))
- ((* if loop.first *))
- & ((( adapter ))) & ((( stat|nice_int )))\\
- ((* else *))
- & & ((( adapter ))) & ((( stat|nice_int )))\\
- ((* endif *))
- ((* endfor *))
- ((* else *))
- Read 1 & 0 & \textit{none found} & 0\\
- ((* endif *))
- ((* if lib.is_paired_end *))
- ((* if lib.flexiprep.stats.clipping_R2.adapters *))
- Read 2 & ((( lib.flexiprep.stats.clipping_R2.num_reads_affected|nice_int )))
- ((* for adapter, stat in lib.flexiprep.stats.clipping_R2.adapters.iteritems() *))
- ((* if loop.first *))
- & ((( adapter ))) & ((( stat|nice_int )))\\
- ((* else *))
- & & ((( adapter ))) & ((( stat|nice_int )))\\
- ((* endif *))
- ((* endfor *))
- ((* else *))
- Read 2 & 0 & \textit{none found} & 0\\
- ((* endif *))
- ((* endif *))
- ((* endif *))
- \end{longtable}
-\end{center}
-\addtocounter{table}{-1}
-
-((* endif *))
-
-\vspace{2mm}
-((* if lib.trimming and lib.is_paired_end *))
-\subsubsection{Base quality trimming}
- Summary of the trimming step is available in Table~\ref{tab:trim}. In short,
- sickle is used to trim the 5' and 3' ends of each FASTQ records so that low
- quality bases are trimmed off. If after trimming the FASTQ record becomes
- shorter than the threshold set by sickle, the entire sequence is discarded. For this
- step, read pair completeness check was done along with trimming.
-
- \indent
-
- More information about sickle is available on the
- \href{https://github.com/najoshi/sickle}{official sickle website}.
-
- \begin{center}
- \captionof{table}{Summary of quality trimming step}
- \label{tab:trim}
- \begin{tabular}{ l r }
- \hline
- Parameter & Count\\ \hline \hline
- Discarded FASTQ records from read 1 & ((( lib.flexiprep.stats.trimming_R1.num_reads_discarded_total|nice_int )))\\
- Discarded FASTQ records from read 2 & ((( lib.flexiprep.stats.trimming_R2.num_reads_discarded_total|nice_int )))\\
- \hline
- \end{tabular}
- \end{center}
-((* endif *))
-%\vspace{2mm}
-
-\subsubsection{Basic statistics}
-%\label{subsec:seq_basic}
-
-Basic statistics on the FASTQ files are shown below. For paired-end reads, the
-read count numbers of read 1 and read 2, both before and after preprocessing,
-must match. Read lengths are likely to vary after preprocessing, due to selective
-clipping and trimming of the reads.
-
-\begin{center}
- \captionof{table}{Basic Run Statistics}
- %\label{tab:basestats}
-((* if lib.clipping or lib.trimming *))
- \begin{longtable}{ l r r }
- \hline
- Parameter & Raw & Preprocessed \\ \hline \hline
- \hline \hline
- \endhead
- \hline
- \multicolumn{3}{c}{\textit{Continued on next page}}\\
- \hline
- \endfoot
- \hline
- \endlastfoot
- Read 1 count & ((( lib.fastqc_r1.basic_statistics.data["Total Sequences"] ))) & ((( lib.fastqc_r1_qc.basic_statistics.data["Total Sequences"] )))\\
- Read 1 overall \%GC & ((( lib.fastqc_r1.basic_statistics.data["%GC"] ))) & ((( lib.fastqc_r1_qc.basic_statistics.data["%GC"] )))\\
- Read 1 length range & ((( lib.fastqc_r1.basic_statistics.data["Sequence length"] ))) & ((( lib.fastqc_r1_qc.basic_statistics.data["Sequence length"] )))\\
- \hline
- ((* if lib.is_paired_end *))
- Read 2 count & ((( lib.fastqc_r2.basic_statistics.data["Total Sequences"] ))) & ((( lib.fastqc_r2_qc.basic_statistics.data["Total Sequences"] )))\\
- Read 2 overall \%GC & ((( lib.fastqc_r2.basic_statistics.data["%GC"] ))) & ((( lib.fastqc_r2_qc.basic_statistics.data["%GC"] )))\\
- Read 2 length range & ((( lib.fastqc_r2.basic_statistics.data["Sequence length"] ))) & ((( lib.fastqc_r2_qc.basic_statistics.data["Sequence length"] )))\\
- \hline
- ((* endif *))
-((* else *))
- \begin{longtable}{ l r }
- \hline
- Parameter & Raw \\ \hline \hline
- \hline \hline
- \endhead
- \hline
- \multicolumn{2}{c}{\textit{Continued on next page}}\\
- \hline
- \endfoot
- \hline
- \endlastfoot
- Read 1 count & ((( lib.fastqc_r1.basic_statistics.data["Total Sequences"] )))\\
- Read 1 overall \%GC & ((( lib.fastqc_r1.basic_statistics.data["%GC"] )))\\
- Read 1 length range & ((( lib.fastqc_r1.basic_statistics.data["Sequence length"] )))\\
- \hline
- ((* if lib.is_paired_end *))
- Read 1 count & ((( lib.fastqc_r1.basic_statistics.data["Total Sequences"] )))\\
- Read 1 overall \%GC & ((( lib.fastqc_r1.basic_statistics.data["%GC"] )))\\
- Read 1 length range & ((( lib.fastqc_r1.basic_statistics.data["Sequence length"] )))\\
- \hline
- ((* endif *))
-((* endif *))
- \end{longtable}
-\end{center}
-
-% sequence length distribution
-\subsubsection{Read length distribution}
- Read length distribution for the raw read pair data are shown below.
- Depending on your chosen preprocessing step, the length distribution for the
- preprocessed data may be shown as well. The length distribution for the
- preprocessed data usually becomes less uniform compared to the raw data due
- to the variable removal of the bases in each read.
- \begin{figure}[h!]
- \centering
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_files.plot_sequence_length_distribution.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- ((* if lib.trimming or lib.clipping *))
- \subfloat[Preprocessed read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_qc_files.plot_sequence_length_distribution.path )))}
- }
- ((* endif *))
- \end{minipage}
- \caption{Read length distribution for read 1.}
- %\label{fig:length_dist_before_and_after_1}
- \end{figure}
-
-((* if lib.is_paired_end *))
- \begin{figure}[h!]
- \centering
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_files.plot_sequence_length_distribution.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- ((* if lib.trimming or lib.clipping *))
- \subfloat[Preprocessed read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_qc_files.plot_sequence_length_distribution.path )))}
- }
- ((* endif *))
- \end{minipage}
- \caption{Read length distribution for read 2.}
- %\label{fig:length_dist_before_and_after_2}
- \end{figure}
-((* endif *))
-
-% per base sequence quality
-\subsubsection{Per base sequence quality}
- Here, the sequence quality for different base positions in all read pairs
- are shown. For each figure, the central line represents the median value,
- the blue represents the mean, the yellow box represents the inter-quartile
- range (25\%-75\%), and the upper and lower whiskers represent the 10\% and
- 90\% points respectively. The green-shaded region marks good quality calls,
- orange-shaded regins mark reasonable quality calls, and red-shaded regions
- mark poor quality calls.
-
- \indent
-
- Note that the latter base positions shown in the figures are
- sometimes aggregates of multiple positions.
- \begin{figure}[h!]
- \centering
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_files.plot_per_base_quality.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- ((* if lib.trimming or lib.clipping *))
- \subfloat[Preprocessed read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_qc_files.plot_per_base_quality.path )))}
- }
- ((* endif *))
- \end{minipage}
- \caption{Per base quality before and after processing read 1.}
- %\label{fig:per_base_qual_before_and_after_1}
- \end{figure}
-
-((* if lib.is_paired_end *))
- \begin{figure}[h!]
- \centering
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_files.plot_per_base_quality.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- ((* if lib.trimming or lib.clipping *))
- \subfloat[Preprocessed read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_qc_files.plot_per_base_quality.path )))}
- }
- ((* endif *))
- \end{minipage}
- \caption{Per base quality before and after processing for read 2.}
- %\label{fig:per_base_qual_before_and_after_2}
- \end{figure}
-((* endif *))
-
-% per sequence quality scores
-\subsubsection{Per sequence quality scores}
- The read quality score distributions are shown below.
- \begin{figure}[h!]
- \centering
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_files.plot_per_sequence_quality.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- ((* if lib.trimming or lib.clipping *))
- \subfloat[Preprocessed read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_qc_files.plot_per_sequence_quality.path )))}
- }
- ((* endif *))
- \end{minipage}
- \caption{Per sequence quality scores before and after preprocessing read 1.}
- %\label{fig:per_seq_qual_before_and_after_1}
- \end{figure}
-
-((* if lib.is_paired_end *))
- \begin{figure}[h!]
- \centering
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_files.plot_per_sequence_quality.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- ((* if lib.trimming or lib.clipping *))
- \subfloat[Preprocessed read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_qc_files.plot_per_sequence_quality.path )))}
- }
- ((* endif *))
- \end{minipage}
- \caption{Per sequence quality scores before and after preprocessing for read 2.}
- %\label{fig:per_seq_qual_before_and_after_2}
- \end{figure}
-((* endif *))
-
-% per base sequence content
-\subsubsection{Per base sequence content}
- The figures below plot the occurence of each nucleotide in each position in
- the reads. In a completely random library, you would expect the differences
- among each nucleotide to be minor. You may sometimes see a skewed
- proportion of nucleotides near the start of the read due to the use of
- non-random cDNA during sample preparation.
-
- \indent
-
- Note that the latter base positions shown in the figures are
- sometimes aggregates of multiple positions.
- \begin{figure}[h!]
- \centering
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_files.plot_per_base_sequence_content.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- ((* if lib.trimming or lib.clipping *))
- \subfloat[Preprocessed read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_qc_files.plot_per_base_sequence_content.path )))}
- }
- ((* endif *))
- \end{minipage}
- \caption{Per base sequence content before and after preprocessing for read 1.}
- %\label{fig:per_base_content_before_and_after_1}
- \end{figure}
-
-((* if lib.is_paired_end *))
- \begin{figure}[h!]
- \centering
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_files.plot_per_base_sequence_content.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- ((* if lib.trimming or lib.clipping *))
- \subfloat[Preprocessed read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_qc_files.plot_per_base_sequence_content.path )))}
- }
- ((* endif *))
- \end{minipage}
- \caption{Per base sequence content before and after preprocessing for read 2.}
- %\label{fig:per_base_content_before_and_after_2}
- \end{figure}
-((* endif *))
-
-
-% per sequence GC content
-\subsubsection{Per sequence GC content}
- The figures below show the GC percentage distribution of all the read pair
- data.
- \begin{figure}[h!]
- \centering
- ((* if lib.trimming or lib.clipping *))
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_files.plot_per_sequence_gc_content.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Preprocessed read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_qc_files.plot_per_sequence_gc_content.path )))}
- }
- \end{minipage}
- ((* else *)))
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 1]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r1_files.plot_per_sequence_gc_content.path )))}
- }
- \end{minipage}
- ((* endif *))
- \caption{Per sequence GC content before and after preprocessing for read 1.}
- %\label{fig:per_base_content_before_and_after_1}
- \end{figure}
-
-((* if lib.is_paired_end *))
- \begin{figure}[h!]
- \centering
- ((* if lib.trimming or lib.clipping *))
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_files.plot_per_sequence_gc_content.path )))}
- }
- \end{minipage}
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Preprocessed read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_qc_files.plot_per_sequence_gc_content.path )))}
- }
- \end{minipage}
- ((* else *))
- \begin{minipage}[b]{0.48\textwidth}
- \centering
- \subfloat[Raw read 2]{
- \includegraphics[width=\textwidth]{((( lib.fastqc_r2_files.plot_per_sequence_gc_content.path )))}
- }
- \end{minipage}
- ((* endif *))
- \caption{Per sequence GC content before and after preprocessing for read 2.}
- %\label{fig:per_base_content_before_and_after_2}
- \end{figure}
-((* endif *))
-
-
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/main.tex b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/main.tex
deleted file mode 100644
index d9365883b62103c1272e330628b6d772b5fd1632..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/main.tex
+++ /dev/null
@@ -1,307 +0,0 @@
-\documentclass[a4paper,12pt]{article}
-\usepackage[a4paper,margin=1in]{geometry}
-\usepackage[T1]{fontenc}
-\usepackage[usenames,dvipsnames]{xcolor}
-\usepackage{longtable}
-\usepackage{graphicx}
-\usepackage{subfig}
-\usepackage{listings}
-\usepackage{verbatim}
-\usepackage{multirow}
-\usepackage{url}
-\usepackage{grffile}
-\usepackage[superscript]{cite}
-% must be located here, so we can handle filenames with underscores
-\newcommand{\UnderscoreCommands}{\do\IfFileExists \do\verbatiminput%
- \do\verbatimtabinput \do\citeNP \do\citeA \do\citeANP \do\citeN%
- \do\shortcite \do\shortciteNP \do\shortciteA \do\shortciteANP%
- \do\shortciteN \do\citeyear \do\citeyearNP%
-}
-\usepackage[strings]{underscore}
-
-\usepackage{fancyhdr}
-\usepackage{hyperref}
-
-\setlength{\tabcolsep}{20pt}
-\renewcommand{\arraystretch}{1.3}
-\renewcommand{\familydefault}{\sfdefault}
-
-% requires the titling package, for subtitles
-%\newcommand{\subtitle}[1]{
-% \posttitle{
-% \par\end{center}
-% \begin{center}\large#1\end{center}
-% \vskip0.5em}}
-
-\pagestyle{fancy}
-\setlength{\headheight}{15.2pt}
-
-\fancyhf{}
-\fancyhead[LE,RO]{\thepage}
-\fancyhead[RE]{\textit{\nouppercase{\leftmark}}}
-\fancyhead[LO]{\textit{\nouppercase{\rightmark}}}
-
-\begin{document}
-\setlength{\parindent}{0in}
-%\title{\Huge Gentrap Run Report}
-\title{\resizebox{0.7\linewidth}{!}{\itshape Gentrap Run Report}}
-\author{LUMC Sequencing Analysis Support Core}
-\maketitle
-\begin{center}
- {\LARGE version ((( run.version )))}
-\end{center}
-\begin{figure}[h!]
- \centering
- \includegraphics[width=0.8\textwidth]{((( run.logo )))}
-\end{figure}
-\thispagestyle{empty}
-\clearpage
-
-\addtocontents{toc}{\protect\hypertarget{toc}{}}
-\tableofcontents
-\clearpage
-
-
-\part{Overview}
-\label{sec:overview}
-
-This document outlines the results obtained from running Gentrap, a generic
-pipeline for transcriptome analysis. The pipeline itself is composed of several
-programs, listed in Table~\ref{tab:programs}. Note that the list only contains
-the programs used in this pipeline run. General pipeline settings that applies
-to all samples are shown in Table~\ref{tab:runparams}, while general annotation
-files are shown in Table~\ref{tab:annotfiles}.
-
-\begin{center}
- \captionof{table}{Programs in Gentrap}
- \label{tab:programs}
- \begin{longtable}{ l l l p{0.2\textwidth} }
- \hline
- Program & Version & Checksum & Usage\\
- \hline \hline
- \endhead
- \hline
- \multicolumn{3}{c}{\textit{Continued on next page}}\\
- \hline
- \endfoot
- \hline
- \endlastfoot
- Gentrap & ((( run.version ))) & - & the full pipeline\\
- ((* for program, info in run.executables.items()|sort *))
- ((( program ))) & ((( info.version ))) & ((( info.md5|truncate(7, True, "") ))) & ((( info.desc )))\\
- ((* endfor *))
- \end{longtable}
-\end{center}
-% HACK: to keep table counters in sync
-\addtocounter{table}{-1}
-
-\begin{center}
- \captionof{table}{General Run Parameters}
- \label{tab:runparams}
- \begin{longtable}{ p{0.4\textwidth} p{0.4\textwidth} }
- \hline
- Parameter & Value\\
- \hline \hline
- \endhead
- \hline
- \multicolumn{2}{c}{\textit{Continued on next page}}\\
- \hline
- \endfoot
- \hline
- \endlastfoot
- Number of samples & ((( run.samples|length )))\\
- Number of libraries & ((( run.libs|length )))\\
- Library types & ((( run.lib_type )))\\
- Expression value measures & ((( run.settings.expression_measures|join(", ") )))\\
- Strand protocol & ((( run.settings.strand_protocol|lower )))\\
- Variant calling & ((* if run.settings.variant_calling *))enabled((* else *))disabled((* endif *))\\
- Ribosomal reads removal & ((* if run.settings.remove_ribosomal_reads *))enabled((* else *))disabled((* endif *))\\
- \end{longtable}
-\end{center}
-\addtocounter{table}{-1}
-
-
-\begin{center}
- \captionof{table}{Annotation Files}
- \label{tab:annotfiles}
- \begin{longtable}{ l l p{0.4\textwidth} }
- \hline
- File & Checksum & Name\\
- \hline \hline
- \endhead
- \hline
- \multicolumn{3}{c}{\textit{Continued on next page}}\\
- \hline
- \endfoot
- \hline
- \endlastfoot
- General refFlat file & ((( run.files.annotation_refflat.md5|truncate(7, True, "") ))) & ((( run.files.annotation_refflat.path|basename )))\\
- ((* if run.files.annotation_gtf *))
- General GTF file & ((( run.files.annotation_gtf.md5|truncate(7, True, "") ))) & ((( run.files.annotation_gtf.path|basename )))\\
- ((* endif *))
- ((* if run.files.annotation_bed *))
- General BED file & ((( run.files.annotation_bed.md5|truncate(7, True, "") ))) & ((( run.files.annotation_bed.path|basename )))\\
- ((* endif *))
- ((* if run.files.ribosome_refflat *))
- Ribosome refFlat & ((( run.files.ribosome_refflat.md5|truncate(7, True, "") ))) & ((( run.files.ribosome_refflat.path|basename )))\\
- ((* endif *))
- \end{longtable}
-\end{center}
-% HACK: to keep table counters in sync
-\addtocounter{table}{-1}
-
-\clearpage
-
-((* if run.samples|length > 2 and run.settings.expression_measures|length > 0 *))
-\part{Multi Sample Results}
-\label{sec:msr}
-This section shows results that are computed from multiple samples.
-
-\begin{center}
- \captionof{table}{Multi Sample Result Files}
- \label{tab:annotfiles}
- \begin{longtable}{ l l p{0.4\textwidth} }
- \hline
- File & Checksum & Name\\
- \hline \hline
- \endhead
- \hline
- \multicolumn{3}{c}{\textit{Continued on next page}}\\
- \hline
- \endfoot
- \hline
- \endlastfoot
- ((* if run.files.gene_fragments_count *))
- Fragments per gene & ((( run.files.gene_fragments_count.md5|truncate(7, True, "") ))) & ((( run.files.gene_fragments_count.path|basename )))\\
- ((* endif *))
-
- ((* if run.files.exon_fragments_count *))
- Fragments per exon & ((( run.files.exon_fragments_count.md5|truncate(7, True, "") ))) & ((( run.files.exon_fragments_count.path|basename )))\\
- ((* endif *))
-
- ((* if run.files.gene_bases_count *))
- Bases per gene & ((( run.files.gene_bases_count.md5|truncate(7, True, "") ))) & ((( run.files.gene_bases_count.path|basename )))\\
- ((* endif *))
-
- ((* if run.files.exon_bases_count *))
- Bases per exon & ((( run.files.exon_bases_count.md5|truncate(7, True, "") ))) & ((( run.files.exon_bases_count.path|basename )))\\
- ((* endif *))
-
- ((* if run.files.gene_fpkm_cufflinks_strict *))
- Cufflinks (strict, gene) & ((( run.files.gene_fpkm_cufflinks_strict.md5|truncate(7, True, "") ))) & ((( run.files.gene_fpkm_cufflinks_strict.path|basename )))\\
- ((* endif *))
- ((* if run.files.isoform_fpkm_cufflinks_strict *))
- Cufflinks (strict, isoform) & ((( run.files.isoform_fpkm_cufflinks_strict.md5|truncate(7, True, "") ))) & ((( run.files.isoform_fpkm_cufflinks_strict.path|basename )))\\
- ((* endif *))
-
- ((* if run.files.gene_fpkm_cufflinks_guided *))
- Cufflinks (guided, gene) & ((( run.files.gene_fpkm_cufflinks_guided.md5|truncate(7, True, "") ))) & ((( run.files.gene_fpkm_cufflinks_guided.path|basename )))\\
- ((* endif *))
- ((* if run.files.isoform_fpkm_cufflinks_guided *))
- Cufflinks (guided, isoform) & ((( run.files.isoform_fpkm_cufflinks_guided.md5|truncate(7, True, "") ))) & ((( run.files.isoform_fpkm_cufflinks_guided.path|basename )))\\
- ((* endif *))
-
- ((* if run.files.gene_fpkm_cufflinks_blind *))
- Cufflinks (blind, gene) & ((( run.files.gene_fpkm_cufflinks_blind.md5|truncate(7, True, "") ))) & ((( run.files.gene_fpkm_cufflinks_blind.path|basename )))\\
- ((* endif *))
- ((* if run.files.isoform_fpkm_cufflinks_blind *))
- Cufflinks (blind, isoform) & ((( run.files.isoform_fpkm_cufflinks_blind.md5|truncate(7, True, "") ))) & ((( run.files.isoform_fpkm_cufflinks_blind.path|basename )))\\
- ((* endif *))
- \end{longtable}
-\end{center}
-% HACK: to keep table counters in sync
-\addtocounter{table}{-1}
-
-((* if run.files.gene_fragments_count *))
-\begin{figure}[h!]
- \centering
- \includegraphics[width=0.65\textwidth]{((( run.files.gene_fragments_count_heatmap.path )))}
- \caption{Between-samples correlation of fragment count per gene.}
-\end{figure}
-((* endif *))
-
-((* if run.files.exon_fragments_count *))
-\begin{figure}[h!]
- \centering
- \includegraphics[width=0.65\textwidth]{((( run.files.exon_fragments_count_heatmap.path )))}
- \caption{Between-samples correlation of fragment count per exon.}
-\end{figure}
-((* endif *))
-
-((* if run.files.gene_bases_count *))
-\begin{figure}[h!]
- \centering
- \includegraphics[width=0.65\textwidth]{((( run.files.gene_bases_count_heatmap.path )))}
- \caption{Between-samples correlation of base count per gene.}
-\end{figure}
-((* endif *))
-
-((* if run.files.exon_bases_count *))
-\begin{figure}[h!]
- \centering
- \includegraphics[width=0.65\textwidth]{((( run.files.exon_bases_count_heatmap.path )))}
- \caption{Between-samples correlation of base count per exon.}
-\end{figure}
-((* endif *))
-
-((* if run.files.gene_fpkm_cufflinks_strict_heatmap *))
-\begin{figure}[h!]
- \centering
- \includegraphics[width=0.65\textwidth]{((( run.files.gene_fpkm_cufflinks_strict_heatmap.path )))}
- \caption{Between-samples correlation of the gene level FPKM (Cufflinks strict mode).}
-\end{figure}
-((* endif *))
-
-((* if run.files.gene_fpkm_cufflinks_guided_heatmap *))
-\begin{figure}[h!]
- \centering
- \includegraphics[width=0.65\textwidth]{((( run.files.gene_fpkm_cufflinks_guided_heatmap.path )))}
- \caption{Between-samples correlation of the gene level FPKM (Cufflinks guided mode).}
-\end{figure}
-((* endif *))
-
-((* if run.files.gene_fpkm_cufflinks_blind_heatmap *))
-\begin{figure}[h!]
- \centering
- \includegraphics[width=0.65\textwidth]{((( run.files.gene_fpkm_cufflinks_blind_heatmap.path )))}
- \caption{Between-samples correlation of the gene level FPKM (Cufflinks blind mode).}
-\end{figure}
-((* endif *))
-
-((* endif *))
-
-\clearpage
-
-((* for sample in run.samples.values()|sort *))
-((* include "sample.tex" *))
-\clearpage
-((* endfor *))
-
-
-\part{About Gentrap}
-\label{apx:about}
-
-The Generic Transcriptome Analysis Pipeline (Gentrap) is a
-generic pipeline for analyzing transcripts from RNA-seq experiments. \\
-
-Gentrap was developed by Wibowo Arindrarto (\href{mailto:w.arindrarto@lumc.nl}{w.arindrarto@lumc.nl})
-based on raw scripts written by Jeroen Laros
-(\href{mailto:j.f.j.laros@lumc.nl}{j.f.j.laros@lumc.nl}) and
-Peter-Bram 't Hoen
-(\href{mailto:p.a.c._t_hoen@lumc.nl}{p.a.c._t_hoen@lumc.nl}) as part of the
-\href{https://git.lumc/nl/biopet/biopet}{Biopet framework}. \\
-
-The Biopet framework is developed by the
-\href{http://sasc.lumc.nl}{Sequencing Analysis Support Core} of the
-\href{http://lumc.nl}{Leiden University Medical Center}, by extending the
-\href{http://http://gatkforums.broadinstitute.org/discussion/1306/overview-of-queue}{Queue framework}.
-Please see the respective web sites for licensing information.
-
-\indent
-
-Cover page image: T7 RNA Polymerase and a dsDNA template (PDB ID \texttt{1msw}).
-Created by Thomas Splettstoesser, taken from
-\href{http://commons.wikimedia.org/wiki/File:T7_RNA_polymerase.jpg}{Wikimedia Commons}.
-
-
-\end{document}
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/sample.tex b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/sample.tex
deleted file mode 100644
index b5f31e50efa563c748fa03ada85b61a6139510fe..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/sample.tex
+++ /dev/null
@@ -1,11 +0,0 @@
-\part{Sample "((( sample.name )))" Results}
-\label{sample:(((sample.name)))}
-
-((* if sample.libs|length > 1 *))
-((* include "sample_mapping.tex" *))
-((* endif *))
-
-((* for lib in sample.libs.values() *))
-((* include "lib.tex" *))
-\clearpage
-((* endfor *))
diff --git a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/sample_mapping.tex b/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/sample_mapping.tex
deleted file mode 100644
index 4f4da850da797c3fb1a355bd32b94955d367f5b2..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/resources/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf/sample_mapping.tex
+++ /dev/null
@@ -1,122 +0,0 @@
-\section{Mapping}
-\label{sec:map-((( sample.name )))}
-
-\subsection{Mapping statistics}
-
-\indent
-
-% number + percentage of reads mapped to genome
-% number + percentage of properly paired reads
-\begin{center}
- \captionof{table}{Mapping Overview}
- \label{tab:bamstat-((( sample.name )))}
- \setlength{\tabcolsep}{11pt}
- ((* if sample.is_paired_end *))
- \begin{tabular}{ l r r r }
- \hline
- \multirow{2}{*}{Parameter} & \multicolumn{1}{c}{All Pairs} & \multicolumn{1}{c}{First in Pairs} & \multicolumn{1}{c}{Second in Pairs} \\
- & Value & Value & Value \\
- \hline \hline
- Total reads & ((( sample.aln_metrics.PAIR.total_reads|nice_int ))) & ((( sample.aln_metrics.FIRST_OF_PAIR.total_reads|nice_int ))) & ((( sample.aln_metrics.SECOND_OF_PAIR.total_reads|nice_int ))) \\
- Mean read length & ((( sample.aln_metrics.PAIR.mean_read_length|nice_flt ))) & ((( sample.aln_metrics.FIRST_OF_PAIR.mean_read_length|nice_flt ))) & ((( sample.aln_metrics.SECOND_OF_PAIR.mean_read_length|nice_flt ))) \\
- Strand balance & ((( sample.aln_metrics.PAIR.strand_balance|nice_flt ))) & ((( sample.aln_metrics.FIRST_OF_PAIR.strand_balance|nice_flt ))) & ((( sample.aln_metrics.SECOND_OF_PAIR.strand_balance|nice_flt ))) \\
- \% Mapped to reference & ((( sample.aln_metrics.PAIR.pct_pf_reads_aligned|float2nice_pct )))\% & ((( sample.aln_metrics.FIRST_OF_PAIR.pct_pf_reads_aligned|float2nice_pct )))\% & ((( sample.aln_metrics.SECOND_OF_PAIR.pct_pf_reads_aligned|float2nice_pct )))\% \\
- \% Mapped to reference (MAPQ >= 20) & ((( sample.aln_metrics.PAIR.pct_pf_reads_aligned|float2nice_pct )))\% & ((( sample.aln_metrics.FIRST_OF_PAIR.pct_pf_reads_aligned|float2nice_pct )))\% & ((( sample.aln_metrics.SECOND_OF_PAIR.pct_pf_reads_aligned|float2nice_pct )))\% \\
- Mismatch rate & ((( sample.aln_metrics.PAIR.pf_mismatch_rate|float2nice_pct )))\% & ((( sample.aln_metrics.FIRST_OF_PAIR.pf_mismatch_rate|float2nice_pct )))\% & ((( sample.aln_metrics.SECOND_OF_PAIR.pf_mismatch_rate|float2nice_pct )))\% \\
- Indel rate & ((( sample.aln_metrics.PAIR.pf_indel_rate|float2nice_pct )))\% & ((( sample.aln_metrics.FIRST_OF_PAIR.pf_indel_rate|float2nice_pct )))\% & ((( sample.aln_metrics.SECOND_OF_PAIR.pf_indel_rate|float2nice_pct )))\% \\
- Chimeras & ((( sample.aln_metrics.PAIR.pct_chimeras|float2nice_pct )))\% & ((( sample.aln_metrics.FIRST_OF_PAIR.pct_chimeras|float2nice_pct )))\% & ((( sample.aln_metrics.SECOND_OF_PAIR.pct_chimeras|float2nice_pct )))\% \\
- \hline
- ((* else *))
- \begin{tabular}{ l r }
- \hline
- \multirow{1}{*}{Parameter} & \multicolumn{1}{c}{Value} \\
- \hline \hline
- Total reads & ((( sample.aln_metrics.UNPAIRED.total_reads|nice_int ))) \\
- Mean read length & ((( sample.aln_metrics.UNPAIRED.mean_read_length|nice_flt ))) \\
- Strand balance & ((( sample.aln_metrics.UNPAIRED.strand_balance|nice_flt ))) \\
- \% Mapped to reference & ((( sample.aln_metrics.UNPAIRED.pct_pf_reads_aligned|float2nice_pct )))\% \\
- \% Mapped to reference (MAPQ >= 20) & ((( sample.aln_metrics.UNPAIRED.pct_pf_reads_aligned|float2nice_pct )))\% \\
- Mismatch rate & ((( sample.aln_metrics.UNPAIRED.pf_mismatch_rate|float2nice_pct )))\% \\
- Indel rate & ((( sample.aln_metrics.UNPAIRED.pf_indel_rate|float2nice_pct )))\% \\
- \hline
- ((* endif *))
- \end{tabular}
-\end{center}
-
-((* if sample.is_paired_end *))
-% inferred insert size distribution
-\subsubsection{Insert size distribution}
-
-\IfFileExists{((( sample.inserts_metrics_files.insert_size_histogram.path )))}
-{
- \begin{figure}[h!]
- \centering
- \includegraphics[width=0.7\textwidth]{((( sample.inserts_metrics_files.insert_size_histogram.path )))}
- \caption{Distribution of insert size length of paired-end reads mapped to opposite strands.}
- \end{figure}
-}
-((= TODO: strand-specific stats
-%{
-% \IfFileExists{((( vars['OUT_DIR'] )))/((( vars['SAMPLE'] ))).f.insertsizes.png}
-% {
-% \begin{figure}[h!]
-% \centering
-% \includegraphics[width=0.7\textwidth]{((( vars['OUT_DIR'] )))/((( vars['SAMPLE'] ))).f.insertsizes.png}
-% \caption{Distribution of insert size length of paired-end reads whose first read maps to the minus strand.}
-% \end{figure}
-% }{}
-% \IfFileExists{((( vars['OUT_DIR'] )))/((( vars['SAMPLE'] ))).r.insertsizes.png}
-% {
-% \begin{figure}[h!]
-% \centering
-% \includegraphics[width=0.7\textwidth]{((( vars['OUT_DIR'] )))/((( vars['SAMPLE'] ))).r.insertsizes.png}
-% \caption{Distribution of insert size length of paired-end reads whose first read maps to the plus strand.}
-% \end{figure}
-% }{}
-%}
-=))
-((* endif *))
-
-
-\subsection{RNA-specific metrics}
-
-\IfFileExists{((( sample.rna_metrics_files.output_chart.path )))}
-{
- \begin{figure}[h!]
- \centering
- \includegraphics[width=0.7\textwidth]{((( sample.rna_metrics_files.output_chart.path )))}
- \caption{Normalized coverage bias plot.}
- \end{figure}
-}
-
-\begin{center}
- \captionof{table}{Functional annotation metrics}
- \label{tab:fannot-((( sample.name )))}
- \setlength{\tabcolsep}{11pt}
- \begin{tabular}{ l r r r }
- \hline
- \multirow{2}{*}{Parameter} & \multicolumn{3}{c}{Value} \\
- & Count & \% of all & \% of aligned \\
- \hline \hline
- Total bases & ((( sample.rna_metrics.pf_bases|nice_int ))) & 100\% & - \\
- Aligned bases & ((( sample.rna_metrics.pf_aligned_bases|nice_int ))) & ((( sample.rna_metrics.pct_aligned_bases_all|float2nice_pct )))\% & ((( sample.rna_metrics.pct_aligned_bases|float2nice_pct )))\% \\
- Exonic bases & ((( sample.rna_metrics.exonic_bases|nice_int ))) & ((( sample.rna_metrics.pct_exonic_bases_all|float2nice_pct )))\% & ((( sample.rna_metrics.pct_exonic_bases|float2nice_pct )))\% \\
- \hspace*{4mm}Coding bases & ((( sample.rna_metrics.coding_bases|nice_int ))) & ((( sample.rna_metrics.pct_coding_bases_all|float2nice_pct )))\% & ((( sample.rna_metrics.pct_coding_bases|float2nice_pct )))\% \\
- \hspace*{4mm}UTR bases & ((( sample.rna_metrics.utr_bases|nice_int ))) & ((( sample.rna_metrics.pct_utr_bases_all|float2nice_pct )))\% & ((( sample.rna_metrics.pct_utr_bases|float2nice_pct )))\% \\
- Intronic bases & ((( sample.rna_metrics.intronic_bases|nice_int ))) & ((( sample.rna_metrics.pct_intronic_bases_all|float2nice_pct )))\% & ((( sample.rna_metrics.pct_intronic_bases|float2nice_pct )))\% \\
- Intergenic bases & ((( sample.rna_metrics.intergenic_bases|nice_int ))) & ((( sample.rna_metrics.pct_intergenic_bases_all|float2nice_pct )))\% & ((( sample.rna_metrics.pct_intergenic_bases|float2nice_pct )))\% \\
- ((* if sample.rna_metrics.ribosomal_bases != "" *))
- Ribosomal bases & ((( sample.rna_metrics.ribosomal_bases|nice_int ))) & ((( sample.rna_metrics.pct_ribosomal_bases_all|float2nice_pct )))\% & ((( sample.rna_metrics.pct_ribosomal_bases|float2nice_pct )))\% \\
- ((* endif *))
- \hline
- Median 5' bias & ((( sample.rna_metrics.median_5prime_bias ))) & - & - \\
- Median 3' bias & ((( sample.rna_metrics.median_3prime_bias ))) & - & - \\
- Median 5' to 3' bias & ((( sample.rna_metrics.median_5prime_to_3prime_bias ))) & - & - \\
- \hline
- ((* if sample.run.settings.strand_protocol != "non_specific" *))
- Correct strand reads & ((( sample.rna_metrics.correct_strand_reads|nice_int ))) & - & - \\
- Incorrect strand reads & ((( sample.rna_metrics.incorrect_strand_reads|nice_int ))) & - & - \\
- ((* endif *))
- \hline
- \end{tabular}
-\end{center}
diff --git a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/CufflinksProducer.scala b/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/CufflinksProducer.scala
deleted file mode 100644
index 0a2e48c00688c6cf097066498cd4f6a62f54b107..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/CufflinksProducer.scala
+++ /dev/null
@@ -1,103 +0,0 @@
-/**
- * Biopet is built on top of GATK Queue for building bioinformatic
- * pipelines. It is mainly intended to support LUMC SHARK cluster which is running
- * SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
- * should also be able to execute Biopet tools and pipelines.
- *
- * Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
- *
- * Contact us at: sasc@lumc.nl
- *
- * A dual licensing mode is applied. The source code within this project that are
- * not part of GATK Queue is freely available for non-commercial use under an AGPL
- * license; For commercial users or users who do not want to follow the AGPL
- * license, please contact us to obtain a separate license.
- */
-package nl.lumc.sasc.biopet.pipelines.gentrap
-
-import java.io.File
-
-import nl.lumc.sasc.biopet.extensions.{ Cufflinks, Ln }
-
-/** General trait for containing cufflinks results */
-trait CufflinksProducer {
-
- import Gentrap.ExpMeasures._
- import Gentrap.StrandProtocol._
- import Gentrap._
-
- //TODO: move vars that are used in gentrep
- protected def sampleDir: File
- protected def sampleId: String
- protected def pipeline: Gentrap
- protected def alnFile: File
-
- /** Valid cufflink measure types */
- protected val cufflinksMeasures = Set(CufflinksStrict, CufflinksGuided, CufflinksBlind)
-
- /** Cufflink's terms for strand specificity */
- lazy val strandedness: String = {
- //require(pipeline.config.contains("strand_protocol"))
- pipeline.strandProtocol match {
- case NonSpecific => "fr-unstranded"
- case Dutp => "fr-firststrand"
- case otherwise => throw new IllegalStateException("Unexpected strand type for cufflinks: " + otherwise.toString)
- }
- }
-
- /** Case class for containing cufflinks + its output symlink jobs */
- protected case class CufflinksJobSet(cuffType: ExpMeasures.Value) {
-
- require(cufflinksMeasures.contains(cuffType),
- "Cufflinks measurement type is either " + cufflinksMeasures.mkString(", ") + s"; not $cuffType")
-
- /** Base name for output file extensions and config path */
- lazy val name: String = cuffType match {
- case CufflinksStrict => "cufflinks_strict"
- case CufflinksGuided => "cufflinks_guided"
- case CufflinksBlind => "cufflinks_blind"
- case otherwise => throw new IllegalStateException("Unexpected cufflinks type: " + otherwise.toString)
- }
-
- /** Container for all jobs in this job set */
- def jobs = Seq(cufflinksJob, geneFpkmJob, isoformFpkmJob)
-
- /** The main cufflinks job */
- lazy val cufflinksJob: Cufflinks = {
- val job = new Cufflinks(pipeline) {
- override def configName = "cufflinks"
- override def configPath: List[String] = super.configPath ::: name :: Nil
- }
- job.input = alnFile
- job.library_type = Option(strandedness)
- job.output_dir = new File(sampleDir, name)
- /*
- job.GTF = cuffType match {
- case CufflinksStrict => pipeline.annotationGtf
- case otherwise => None
- }
- job.GTF_guide = cuffType match {
- case CufflinksGuided => pipeline.annotationGtf
- case otherwise => None
- }
- */
- job
- }
-
- /** Job for symlinking gene FPKM results so that it contains a standard filename (with the sample ID) */
- lazy val geneFpkmJob: Ln = {
- val job = new Ln(pipeline)
- job.input = cufflinksJob.outputGenesFpkm
- job.output = new File(cufflinksJob.output_dir, s"$sampleId.genes_fpkm_$name")
- job
- }
-
- /** Job for symlinking isoforms FPKM results so that it contains a standard filename (with the sample ID) */
- lazy val isoformFpkmJob: Ln = {
- val job = new Ln(pipeline)
- job.input = cufflinksJob.outputIsoformsFpkm
- job.output = new File(cufflinksJob.output_dir, s"$sampleId.isoforms_fpkm_$name")
- job
- }
- }
-}
diff --git a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/CustomVarScan.scala b/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/CustomVarScan.scala
deleted file mode 100644
index f2f11a03e775ebe5a7fa4161813a0157c8f7976a..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/CustomVarScan.scala
+++ /dev/null
@@ -1,102 +0,0 @@
-/**
- * Biopet is built on top of GATK Queue for building bioinformatic
- * pipelines. It is mainly intended to support LUMC SHARK cluster which is running
- * SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
- * should also be able to execute Biopet tools and pipelines.
- *
- * Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
- *
- * Contact us at: sasc@lumc.nl
- *
- * A dual licensing mode is applied. The source code within this project that are
- * not part of GATK Queue is freely available for non-commercial use under an AGPL
- * license; For commercial users or users who do not want to follow the AGPL
- * license, please contact us to obtain a separate license.
- */
-package nl.lumc.sasc.biopet.pipelines.gentrap.extensions
-
-import java.io.File
-
-import nl.lumc.sasc.biopet.core.{ Reference, BiopetCommandLineFunction }
-import nl.lumc.sasc.biopet.core.extensions.PythonCommandLineFunction
-import nl.lumc.sasc.biopet.utils.config.Configurable
-import nl.lumc.sasc.biopet.extensions.samtools.SamtoolsMpileup
-import nl.lumc.sasc.biopet.extensions.varscan.Mpileup2cns
-import nl.lumc.sasc.biopet.extensions.{ Bgzip, Tabix }
-import org.broadinstitute.gatk.utils.commandline.{ Input, Output }
-
-/** Ad-hoc extension for VarScan variant calling that involves 6-command pipe */
-// FIXME: generalize piping instead of building something by hand like this!
-// Better to do everything quick and dirty here rather than something half-implemented with the objects
-class CustomVarScan(val root: Configurable) extends BiopetCommandLineFunction with Reference { wrapper =>
-
- override def configName = "customvarscan"
-
- @Input(doc = "Input BAM file", required = true)
- var input: File = null
-
- @Output(doc = "Output VCF file", required = true)
- var output: File = null
-
- @Output(doc = "Output VCF file index", required = true)
- lazy val outputIndex: File = new File(output.toString + ".tbi")
-
- // mpileup, varscan, fix_mpileup.py, binom_test.py, bgzip, tabix
- private def mpileup = new SamtoolsMpileup(wrapper.root) {
- this.input = List(wrapper.input)
- override def configName = wrapper.configName
- disableBaq = true
- depth = Option(1000000)
- outputMappingQuality = true
-
- }
-
- private def fixMpileup = new PythonCommandLineFunction {
- setPythonScript("fix_mpileup.py", "/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/")
- override val root: Configurable = wrapper.root
- override def configName = wrapper.configName
- def cmdLine = getPythonCommand
- }
-
- private def removeEmptyPile() = new BiopetCommandLineFunction {
- override val root: Configurable = wrapper.root
- override def configName = wrapper.configName
- executable = config("exe", default = "grep", freeVar = false)
- override def cmdLine: String = required(executable) + required("-vP") + required("""\t\t""")
- }
-
- private val varscan = new Mpileup2cns(wrapper.root) {
- override def configName = wrapper.configName
- strandFilter = Option(0)
- outputVcf = Option(1)
- }
-
- private val compress = new Bgzip(wrapper.root)
-
- private val index = new Tabix(wrapper.root) {
- override def configName = wrapper.configName
- p = Option("vcf")
- }
-
- override def freezeFieldValues(): Unit = {
- varscan.output = Option(new File(wrapper.output.toString.stripSuffix(".gz")))
- compress.input = List(varscan.output.get)
- compress.output = this.output
- index.input = compress.output
- super.freezeFieldValues()
- varscan.qSettings = this.qSettings
- varscan.freezeFieldValues()
- }
-
- override def beforeGraph(): Unit = {
- super.beforeGraph()
- require(output.toString.endsWith(".gz"), "Output must have a .gz file extension")
- deps :+= referenceFasta()
- }
-
- def cmdLine: String = {
- // FIXME: manual trigger of commandLine for version retrieval
- mpileup.commandLine
- (mpileup | fixMpileup | removeEmptyPile() | varscan).commandLine + " && " + compress.commandLine + " && " + index.commandLine
- }
-}
diff --git a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/Pdflatex.scala b/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/Pdflatex.scala
deleted file mode 100644
index 4747856d6f58943b33f0407b63c37311ab1f67fb..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/Pdflatex.scala
+++ /dev/null
@@ -1,55 +0,0 @@
-/**
- * Biopet is built on top of GATK Queue for building bioinformatic
- * pipelines. It is mainly intended to support LUMC SHARK cluster which is running
- * SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
- * should also be able to execute Biopet tools and pipelines.
- *
- * Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
- *
- * Contact us at: sasc@lumc.nl
- *
- * A dual licensing mode is applied. The source code within this project that are
- * not part of GATK Queue is freely available for non-commercial use under an AGPL
- * license; For commercial users or users who do not want to follow the AGPL
- * license, please contact us to obtain a separate license.
- */
-package nl.lumc.sasc.biopet.pipelines.gentrap.extensions
-
-import java.io.File
-
-import nl.lumc.sasc.biopet.core.BiopetCommandLineFunction
-import nl.lumc.sasc.biopet.utils.config.Configurable
-import org.broadinstitute.gatk.utils.commandline.{ Argument, Input, Output }
-
-/**
- * Wrapper for the pdflatex executable
- */
-class Pdflatex(val root: Configurable) extends BiopetCommandLineFunction {
-
- executable = config("exe", default = "pdflatex", freeVar = false)
- override val executableToCanonicalPath = false
-
- @Input(doc = "Input LaTeX template", required = true)
- var input: File = null
-
- @Output(doc = "Output directory", required = true)
- var outputDir: File = null
-
- @Argument(doc = "Job name", required = true)
- var name: String = null
-
- @Output(doc = "Output PDF file")
- lazy val outputPdf: File = {
- require(name != null && outputDir != null, "Job name and output directory must be defined")
- new File(outputDir, name + ".pdf")
- }
-
- def cmdLine = {
- // repeating command 3x times to get internal references working correctly
- val singleCommand = required(executable) +
- required("-output-directory", outputDir) +
- required("-jobname", name) +
- required(input)
- Seq(singleCommand, singleCommand, singleCommand).mkString(" && ")
- }
-}
diff --git a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/RawBaseCounter.scala b/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/RawBaseCounter.scala
deleted file mode 100644
index 8be307a9d1323f0252d5b7f2e24f52c07148adcc..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/extensions/RawBaseCounter.scala
+++ /dev/null
@@ -1,114 +0,0 @@
-/**
- * Biopet is built on top of GATK Queue for building bioinformatic
- * pipelines. It is mainly intended to support LUMC SHARK cluster which is running
- * SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
- * should also be able to execute Biopet tools and pipelines.
- *
- * Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
- *
- * Contact us at: sasc@lumc.nl
- *
- * A dual licensing mode is applied. The source code within this project that are
- * not part of GATK Queue is freely available for non-commercial use under an AGPL
- * license; For commercial users or users who do not want to follow the AGPL
- * license, please contact us to obtain a separate license.
- */
-package nl.lumc.sasc.biopet.pipelines.gentrap.extensions
-
-import java.io.File
-
-import nl.lumc.sasc.biopet.core.BiopetCommandLineFunction
-import nl.lumc.sasc.biopet.utils.Logging
-import nl.lumc.sasc.biopet.utils.config.Configurable
-import nl.lumc.sasc.biopet.core.extensions.PythonCommandLineFunction
-import org.broadinstitute.gatk.utils.commandline.{ Input, Output }
-
-import scala.language.reflectiveCalls
-
-/** Ad-hoc extension for counting bases that involves 3-command pipe */
-// FIXME: generalize piping instead of building something by hand like this!
-// Better to do everything quick and dirty here rather than something half-implemented with the objects
-class RawBaseCounter(val root: Configurable) extends BiopetCommandLineFunction { wrapper =>
-
- override def configName = "rawbasecounter"
-
- @Input(doc = "Reference BED file", required = true)
- var annotationBed: File = null
-
- @Input(doc = "Input BAM file from both strands", required = false)
- var inputBoth: File = null
-
- @Input(doc = "Input BAM file from the plus strand", required = false)
- var inputPlus: File = null
-
- @Input(doc = "Input BAM file from the minus strand", required = false)
- var inputMinus: File = null
-
- @Output(doc = "Output base count file", required = true)
- var output: File = null
-
- /** Internal flag for mixed strand mode */
- private lazy val mixedStrand: Boolean = inputBoth != null && inputPlus == null && inputMinus == null
-
- /** Internal flag for distinct strand / strand-specific mode */
- private lazy val distinctStrand: Boolean = inputBoth == null && inputPlus != null && inputMinus != null
-
- private def grepForStrand = new BiopetCommandLineFunction {
- var strand: String = null
- override val root: Configurable = wrapper.root
- override def configName = wrapper.configName
- executable = config("exe", default = "grep", freeVar = false)
- override def cmdLine: String = required(executable) +
- required("-P", """\""" + strand + """$""") +
- required(annotationBed)
- }
-
- private def bedtoolsCovHist = new BiopetCommandLineFunction {
- var bam: File = null
- override def configName = "bedtoolscoverage"
- override val root: Configurable = wrapper.root
- executable = config("exe", default = "coverageBed", freeVar = false)
- override def cmdLine: String = required(executable) +
- required("-split") +
- required("-hist") +
- required("-abam", bam) +
- required("-b", if (mixedStrand) annotationBed else "stdin")
- }
-
- private def hist2Count = new PythonCommandLineFunction {
- setPythonScript("hist2count.py", "/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/")
- override def configName = wrapper.configName
- override val root: Configurable = wrapper.root
- def cmdLine = getPythonCommand + optional("-c", "3")
- }
-
- override def beforeGraph(): Unit = {
- if (annotationBed == null) Logging.addError("Annotation BED must be supplied")
- require(output != null, "Output must be defined")
- require((mixedStrand && !distinctStrand) || (!mixedStrand && distinctStrand),
- "Invalid input BAM combinations for RawBaseCounter")
- }
-
- def cmdLine: String =
- if (mixedStrand && !distinctStrand) {
-
- val btCov = bedtoolsCovHist
- btCov.bam = inputBoth
- btCov.commandLine + "|" + hist2Count.commandLine + " > " + output
-
- } else {
-
- val plusGrep = grepForStrand
- plusGrep.strand = "+"
- val plusBtCov = bedtoolsCovHist
- plusBtCov.bam = inputPlus
-
- val minusGrep = grepForStrand
- minusGrep.strand = "-"
- val minusBtCov = bedtoolsCovHist
- minusBtCov.bam = inputMinus
-
- plusGrep.commandLine + "|" + plusBtCov.commandLine + "|" + hist2Count.commandLine + " > " + required(output) + " && " +
- minusGrep.commandLine + "|" + minusBtCov.commandLine + "|" + hist2Count.commandLine + " >> " + required(output)
- }
-}
diff --git a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/AggrBaseCount.scala b/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/AggrBaseCount.scala
deleted file mode 100644
index 84941f6beeb080c1ffa2b7681eb8bb9e404d6449..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/AggrBaseCount.scala
+++ /dev/null
@@ -1,51 +0,0 @@
-/**
- * Biopet is built on top of GATK Queue for building bioinformatic
- * pipelines. It is mainly intended to support LUMC SHARK cluster which is running
- * SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
- * should also be able to execute Biopet tools and pipelines.
- *
- * Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
- *
- * Contact us at: sasc@lumc.nl
- *
- * A dual licensing mode is applied. The source code within this project that are
- * not part of GATK Queue is freely available for non-commercial use under an AGPL
- * license; For commercial users or users who do not want to follow the AGPL
- * license, please contact us to obtain a separate license.
- */
-package nl.lumc.sasc.biopet.pipelines.gentrap.scripts
-
-import java.io.File
-
-import nl.lumc.sasc.biopet.utils.config.Configurable
-import nl.lumc.sasc.biopet.pipelines.gentrap.extensions.RScriptCommandLineFunction
-import org.broadinstitute.gatk.utils.commandline.{ Input, Output }
-
-/**
- * Wrapper for the aggr_base_count.R script, used internally in Gentrap
- */
-class AggrBaseCount(val root: Configurable) extends RScriptCommandLineFunction {
-
- setRScript("aggr_base_count.R", "/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/")
-
- @Input(doc = "Raw base count files", required = true)
- var input: File = null
-
- @Output(doc = "Output count file", required = false)
- var output: File = null
-
- var inputLabel: String = null
- var mode: String = null
-
- override def beforeGraph(): Unit = {
- require(mode == "exon" || mode == "gene", "Mode must be either exon or gene")
- require(input != null, "Input raw base count table must be defined")
- }
-
- def cmdLine = {
- RScriptCommand +
- required("-I", input) +
- required("-N", inputLabel) +
- optional(if (mode == "gene") "-G" else "-E", output)
- }
-}
diff --git a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/Hist2Count.scala b/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/Hist2Count.scala
deleted file mode 100644
index 4a8002c9defcf843bfeb6f69b062d68dc6d3b7b6..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/Hist2Count.scala
+++ /dev/null
@@ -1,46 +0,0 @@
-/**
- * Biopet is built on top of GATK Queue for building bioinformatic
- * pipelines. It is mainly intended to support LUMC SHARK cluster which is running
- * SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
- * should also be able to execute Biopet tools and pipelines.
- *
- * Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
- *
- * Contact us at: sasc@lumc.nl
- *
- * A dual licensing mode is applied. The source code within this project that are
- * not part of GATK Queue is freely available for non-commercial use under an AGPL
- * license; For commercial users or users who do not want to follow the AGPL
- * license, please contact us to obtain a separate license.
- */
-package nl.lumc.sasc.biopet.pipelines.gentrap.scripts
-
-import java.io.File
-
-import nl.lumc.sasc.biopet.utils.config.Configurable
-import nl.lumc.sasc.biopet.core.extensions.PythonCommandLineFunction
-import org.broadinstitute.gatk.utils.commandline.{ Input, Output }
-
-/**
- * Wrapper for the hist2count.py script, used internally in Gentrap
- */
-class Hist2Count(val root: Configurable) extends PythonCommandLineFunction {
-
- setPythonScript("hist2count.py")
-
- @Input(doc = "Input histogram file (generated by bedtools coverage -hist)", required = true)
- var input: File = null
-
- @Output(doc = "Output count file", required = false)
- var outputGeneLevelCount: File = null
-
- /** index of column to copy to output file from input file */
- var copyColumn: List[Int] = List.empty[Int]
-
- def cmdLine = {
- getPythonCommand +
- required("-i", input) +
- required("-o", outputGeneLevelCount) +
- optional("-c", repeat(copyColumn), escape = false)
- }
-}
diff --git a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/PdfReportTemplateWriter.scala b/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/PdfReportTemplateWriter.scala
deleted file mode 100644
index eeab93c18279cf3e0510e810ef1199dbb27ee58a..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/PdfReportTemplateWriter.scala
+++ /dev/null
@@ -1,76 +0,0 @@
-/**
- * Biopet is built on top of GATK Queue for building bioinformatic
- * pipelines. It is mainly intended to support LUMC SHARK cluster which is running
- * SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
- * should also be able to execute Biopet tools and pipelines.
- *
- * Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
- *
- * Contact us at: sasc@lumc.nl
- *
- * A dual licensing mode is applied. The source code within this project that are
- * not part of GATK Queue is freely available for non-commercial use under an AGPL
- * license; For commercial users or users who do not want to follow the AGPL
- * license, please contact us to obtain a separate license.
- */
-package nl.lumc.sasc.biopet.pipelines.gentrap.scripts
-
-import java.io.{ File, FileOutputStream }
-
-import nl.lumc.sasc.biopet.utils.config.Configurable
-import nl.lumc.sasc.biopet.core.extensions.PythonCommandLineFunction
-import org.broadinstitute.gatk.utils.commandline.{ Input, Output }
-
-/**
- * Wrapper for the pdf_report.py script, used internally in Gentrap
- */
-class PdfReportTemplateWriter(val root: Configurable) extends PythonCommandLineFunction {
-
- @Input(doc = "Input summary file", required = true)
- var summaryFile: File = null
-
- @Input(doc = "Main report template", required = true) // def since we hard-code the template
- def mainTemplateFile: File = new File(templateWorkDir, "main.tex")
-
- @Input(doc = "Main report logo", required = true) // def since we hard-code the logo
- def logoFile: File = new File(templateWorkDir, "gentrap_front.png")
-
- @Output(doc = "Output file", required = true)
- var output: File = null
-
- val templateWorkDir: File = new File(".queue/tmp/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf")
- val templateFiles: Seq[String] = Seq(
- "main.tex", "gentrap_front.png",
- "sample.tex", "sample_mapping.tex",
- "lib.tex", "lib_seqeval.tex", "lib_mapping.tex"
- )
-
- protected def prepTemplate(name: String,
- subPackage: String = "/nl/lumc/sasc/biopet/pipelines/gentrap/templates/pdf"): Unit = {
- val target = new File(".queue/tmp" + subPackage, name)
- if (!target.getParentFile.exists) target.getParentFile.mkdirs()
- val is = getClass.getResourceAsStream(subPackage + "/" + name)
- val os = new FileOutputStream(target)
- org.apache.commons.io.IOUtils.copy(is, os)
- os.close()
-
- //python_script_name = script
- //python_script = new File(".queue/tmp/" + subpackage + python_script_name)
- //if (!python_script.getParentFile.exists) python_script.getParentFile.mkdirs
- //val is = getClass.getResourceAsStream(subpackage + python_script_name)
- //val os = new FileOutputStream(python_script)
- //org.apache.commons.io.IOUtils.copy(is, os)
- //os.close()
- }
-
- def cmdLine = {
- getPythonCommand +
- required(summaryFile) +
- required(mainTemplateFile) +
- required(logoFile.getAbsoluteFile) +
- " > " + required(output)
- }
-
- setPythonScript("pdf_report.py")
- templateFiles.foreach(t => prepTemplate(t))
-}
diff --git a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/PlotPca.scala b/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/PlotPca.scala
deleted file mode 100644
index dd13421069fd331c819653a9fcf2cb4cf3b7c851..0000000000000000000000000000000000000000
--- a/public/gentrap/src/main/scala/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/PlotPca.scala
+++ /dev/null
@@ -1,45 +0,0 @@
-/**
- * Biopet is built on top of GATK Queue for building bioinformatic
- * pipelines. It is mainly intended to support LUMC SHARK cluster which is running
- * SGE. But other types of HPC that are supported by GATK Queue (such as PBS)
- * should also be able to execute Biopet tools and pipelines.
- *
- * Copyright 2014 Sequencing Analysis Support Core - Leiden University Medical Center
- *
- * Contact us at: sasc@lumc.nl
- *
- * A dual licensing mode is applied. The source code within this project that are
- * not part of GATK Queue is freely available for non-commercial use under an AGPL
- * license; For commercial users or users who do not want to follow the AGPL
- * license, please contact us to obtain a separate license.
- */
-package nl.lumc.sasc.biopet.pipelines.gentrap.scripts
-
-import java.io.File
-
-import nl.lumc.sasc.biopet.utils.config.Configurable
-import nl.lumc.sasc.biopet.pipelines.gentrap.extensions.RScriptCommandLineFunction
-import org.broadinstitute.gatk.utils.commandline.{ Input, Output }
-
-/**
- * Wrapper for the plot_pca.R script, used internally in Gentrap
- */
-class PlotPca(val root: Configurable) extends RScriptCommandLineFunction {
-
- setRScript("plot_pca.R", "/nl/lumc/sasc/biopet/pipelines/gentrap/scripts/")
-
- @Input(doc = "Input table", required = true)
- var input: File = null
-
- @Output(doc = "Output plot", required = false)
- var output: File = null
-
- var tmmNormalize: Boolean = config("tmm_normalize", default = false)
-
- def cmdLine = {
- RScriptCommand +
- conditional(tmmNormalize, "-T") +
- required("-I", input) +
- required("-O", output)
- }
-}