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Laros
lectures
Commits
2df3988c
Commit
2df3988c
authored
Dec 02, 2015
by
Laros
Browse files
Finalised melanoma lecture.
parent
82bc1c6f
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6
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lectures/melanoma/cnv.png
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lectures/melanoma/exome_genome.jpg
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lectures/melanoma/melanoma.tex
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2df3988c
\documentclass
[slidestop]
{
beamer
}
\author
{
Jeroen F.J. Laros
}
\title
{
Whole genome sequencing
in
Dutch melanoma families
}
\title
{
Whole genome sequencing
of
Dutch melanoma families
}
\providecommand
{
\mySubTitle
}{
Experiences and challenges
}
\providecommand
{
\myConference
}{
MTG
work discussion
}
\providecommand
{
\myConference
}{
MTG
meeting
}
\providecommand
{
\myDate
}{
3-dec-2015
}
\providecommand
{
\myGroup
}{
Leiden Genome Technology Center
}
\providecommand
{
\myDepartment
}{
Department of Human Genetics
}
...
...
@@ -21,21 +21,55 @@
% First page of the presentation.
\section
{
Introduction
}
\subsection
{
Samples
}
\subsection
{
Full genome sequencing
}
\begin{pframe}
X families, about Y members per family.
In contrast to
\emph
{
exome sequencing
}
, where we only the
\emph
{
coding
regions
}
are sequenced, we sequence everything.
\bigskip
\pause
Advantages of
\emph
{
full genome sequencing
}
:
\begin{itemize}
\item
Full genome.
\item
$
z
\times
$
coverage.
\item
Introns.
\begin{itemize}
\item
Branch points.
\item
Intronic splicing enhancers.
\end{itemize}
\item
Promoters.
\item
Transcription factor binding sites, insulators, etc.
\item
Consistent coverage.
\begin{itemize}
\item
Copy number variation.
\end{itemize}
\end{itemize}
\end{pframe}
\begin{pframe}
\begin{figure}
[]
\begin{center}
\includegraphics
[trim=0.3cm 1.5cm 0 3.4cm, clip, width=\textwidth]
{
exome
_
genome
}
\end{center}
\caption
{
Exome- versus genome sequencing.
}
\end{figure}
\end{pframe}
\subsection
{
Copy number variation
}
\begin{pframe}
\begin{figure}
[]
\begin{center}
\includegraphics
[trim=0 8.4cm 0 2.2cm, clip, width=\textwidth]
{
cnv
}
\end{center}
\caption
{
Coverage pattern over a whole chromosome.
}
\end{figure}
\end{pframe}
\section
{
Data generation
}
\subsection
{
Sequencing
}
\begin{pframe}
\begin{minipage}
[t]
{
0.47
\textwidth
}
\begin{figure}
\includegraphics
[width=\textwidth]
{
hiseq
_
2000
}
\includegraphics
[
trim=0 1cm 0 0.5cm, clip,
width=\textwidth]
{
hiseq
_
2000
}
\caption
{
HiSeq 2500.
}
\end{figure}
\end{minipage}
...
...
@@ -53,8 +87,18 @@
\end{pframe}
\begin{pframe}
Sequencing was one at the Sanger institute.
% 938528037 mapped reads
\begin{figure}
[]
\begin{center}
\includegraphics
[trim=0 1cm 0 23cm, clip, width=\textwidth]
{
single
_
paired
_
end
}
\end{center}
\caption
{
Paired end sequencing.
}
\end{figure}
\end{pframe}
\begin{pframe}
Sequencing was done at the Sanger institute.
% 938,528,037 mapped reads
% 1672174 unmapped reads
\begin{itemize}
...
...
@@ -62,11 +106,13 @@
\item
$
1
,
\!
000
,
\!
000
,
\!
000
$
reads.
\item
$
100
,
\!
000
,
\!
000
,
\!
000
$
nucleotides.
\item
$
150
$
GB of data per sample (compressed).
\item
$
35
\times
$
coverage.
\end{itemize}
\bigskip
\pause
A grand total of
$
4
.
5
$
TB was
us
ed, which completely filled up the
storage at
Sanger.
A grand total of
$
4
.
5
$
TB was
generat
ed, which completely filled up the
storage at
Sanger.
\vfill
\permfoot
{
\url
{
http://www.sanger.ac.uk/
}}
...
...
@@ -75,13 +121,25 @@
\subsection
{
Data transfer
}
\begin{pframe}
We need to make sure the data is transfered in a
\emph
{
secure
}
way.
\bigskip
Sending disks?
Data carrier:
\begin{itemize}
\item
Disks.
\item
Network.
\end{itemize}
\bigskip
\pause
Public server, GPG encryption.
\begin{itemize}
\item
Encrypted with my
\emph
{
public key
}
.
\item
The encrypted data is public.
\item
Can only be decrypted with my
\emph
{
private key
}
.
\end{itemize}
\end{pframe}
\section
{
Data analysis
}
\subsection
{
Pipelines
}
\begin{pframe}
\begin{figure}
[]
...
...
@@ -116,7 +174,7 @@
\end{pframe}
\begin{frame}
\frametitle
{
Principle of v
ariant calling
}
\frametitle
{
V
ariant calling
}
\begin{figure}
[]
\begin{center}
...
...
@@ -151,46 +209,6 @@
\end{itemize}
\end{pframe}
\section
{
Annotation
}
\subsection
{
Effect prediction
}
\begin{pframe}
In most cases we are still left with a lot of variants.
\bigskip
Variant annotation.
\begin{itemize}
\item
Frequency within a population.
\item
Location of the variant.
\begin{itemize}
\item
Gene panels.
\item
Location within a gene.
\end{itemize}
\item
Conservation.
\end{itemize}
\end{pframe}
\subsection
{
Variant Effect Predictor
}
\begin{pframe}
A selection of VEP annotation:
\begin{itemize}
\item
Affected genes and transcripts.
\item
Location of the variant.
\begin{itemize}
\item
Upstream of a transcript, in coding sequence, in non-coding RNA,
in regulatory region.
\end{itemize}
\item
Consequence on the protein sequence.
\begin{itemize}
\item
Stop gained, missense, stop lost, frameshift.
\end{itemize}
\item
Minor allele frequencies from the 1000 Genomes Project.
\item
SIFT and PolyPhen scores for changes to protein sequence.
\end{itemize}
\vfill
\permfoot
{
\url
{
http://www.ensembl.org/info/docs/tools/vep/index.html
}}
\end{pframe}
\subsection
{
Databases
}
\begin{pframe}
In most cases we are not interested in common variants.
...
...
@@ -201,7 +219,7 @@
\end{itemize}
\medskip
A cut-off of
$
1
\%
$
is usually fine.
A cut-off of
$
1
$
\%
is usually fine.
\bigskip
\pause
...
...
@@ -220,26 +238,39 @@
\bs
{
\url
{
http://www.hgmd.cf.ac.uk/
}}
\end{pframe}
\subsection
{
Inheritance based filtering
}
\begin{pframe}
% iets moet in alle familieleden voorkomen, maar mag niet in een deel van een
% andere familie voorkomen maar wel in de gehele familie
We used the following intersection method.
\bigskip
{
\it
``A variant should be present in all affected members of a family, but
it may not occur in part of the affected members of an other family.''
}
\begin{table}
[]
\begin{center}
\begin{tabular}
{
ccl
}
Family one
&
Family two
&
Filter result
\\
\hline
$
5
/
5
$
&
$
4
/
4
$
&
Pass
ed
\\
$
0
/
5
$
&
$
4
/
4
$
&
Pass
ed
\\
$
5
/
5
$
&
$
4
/
4
$
&
Pass
\\
$
0
/
5
$
&
$
4
/
4
$
&
Pass
\\
$
3
/
5
$
&
$
4
/
4
$
&
Filtered
\\
$
3
/
5
$
&
$
3
/
4
$
&
Filtered
\\
\end{tabular}
\end{center}
\caption
{
The a
dvanced intersection.
}
\caption
{
A
dvanced intersection.
}
\end{table}
\vfill
\bs
{
P.J. van 't Hof
}
\end{pframe}
\subsection
{
Filters
}
\begin{pframe}
We first did some general filtering based on allele frequencies found in
databases and inheritance patterns.
\begin{table}
[]
\begin{center}
\begin{tabular}
{
lr
}
...
...
@@ -258,6 +289,8 @@
\end{pframe}
\begin{pframe}
By combining filters, we are left with a reasonable amount of variants.
\begin{table}
[]
\begin{center}
\begin{tabular}
{
lr
}
...
...
@@ -274,7 +307,48 @@
\caption
{
Combining filters.
}
\end{table}
(25.127 in all families)
Note:
$
25
,
\!
127
$
variants are present in every individual.
\end{pframe}
\section
{
Annotation
}
\subsection
{
Effect prediction
}
\begin{pframe}
We are still left with a lot of variants.
\bigskip
Variant annotation.
\begin{itemize}
\item
Frequency within a population.
\item
Location of the variant.
\begin{itemize}
\item
Gene panels.
\item
Location within a gene.
\item
Regulatory regions.
\end{itemize}
\item
Conservation.
\end{itemize}
\end{pframe}
\subsection
{
Variant Effect Predictor
}
\begin{pframe}
A selection of VEP annotation:
\begin{itemize}
\item
Affected genes and transcripts.
\item
Location of the variant.
\begin{itemize}
\item
Upstream of a transcript, in coding sequence, in non-coding RNA,
in regulatory region.
\end{itemize}
\item
Consequence on the protein sequence.
\begin{itemize}
\item
Stop gained, missense, stop lost, frameshift.
\end{itemize}
\item
Minor allele frequencies from the 1000 Genomes Project.
\item
SIFT and PolyPhen scores for changes to protein sequence.
\end{itemize}
\vfill
\permfoot
{
\url
{
http://www.ensembl.org/info/docs/tools/vep/index.html
}}
\end{pframe}
\subsection
{
Conservation
}
...
...
@@ -295,11 +369,15 @@
% Make the acknowledgements slide.
\makeAcknowledgementsSlide
{
\begin{tabular}
{
lll
}
\bf
Dermatology
&
\bf
SASC
&
\bf
Sanger
\\
Mijke Visser
&
Peter van 't Hof
&
Thomas Keane
\\
Nelleke Gruis
&
Sander van der Zeeuw
&
Kim Wong
\\
Nienke van der Stoep
&
Leon Mei
&
Daniela Espinoza
\\
Remco van Doorn
&
&
David Adams
\\
\bf
Dermatology
&
\bf
SASC
&
\bf
Sanger
\\
Mijke Visser
&
Peter van 't Hof
&
Thomas Keane
\\
Nelleke Gruis
&
Sander van der Zeeuw
&
Kim Wong
\\
Remco van Doorn
&
Leon Mei
&
Daniela Espinoza
\\
Remco van Doorn
&
&
David Adams
\\
\\
\bf
Clinical Genetics
&
\bf
Medical Statistics
\\
Nienke van der Stoep
&
Ramin Monajemi
\\
&
Jeanine Houwing
\\
\end{tabular}
}
...
...
lectures/melanoma/single_paired_end.png
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