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SomaticSeq Documentation
Li Tai Fang
June 23, 2018
1
Introduction
SomaticSeq is a flexible post-somatic-mutation-calling workflow for improved accuracy. We have incorporated
multiple somatic mutation caller(s) to obtain a combined call set, and then it uses machine learning to
distinguish true mutations from false positives from that call set. We have incorporated the following
somatic mutation caller: MuTect/Indelocator/MuTect2, VarScan2, JointSNVMix, SomaticSniper, VarDict,
MuSE, LoFreq, Scalpel, Strelka, and TNscope. You may incorporate some or all of those callers into your
own pipeline with SomaticSeq.
The manuscript, An ensemble approach to accurately detect somatic mutations using SomatThe SomaticSeq project is located at
icSeq, is published in Genome Biology 2015, 16:197.
http://bioinform.github.io/somaticseq/ . The data described in the manuscript is also described at
http://bioinform.github.io/somaticseq/data.html. There have been some major improvements since the publication.
SomaticSeq.Wrapper.sh is a bash script that calls a series of scripts to combine the output of the somatic
mutation caller(s), after the somatic mutation callers are run. Then, depending on what R scripts are fed to
SomaticSeq.Wrapper.sh, it will either 1) train the call set into a classifier, 2) predict high-confidence somatic
mutations from the call set based on a pre-defined classifier, or 3) simply label the calls (i.e., PASS, LowQual,
or REJECT) based on majority vote of the tools.
1.1
Dependencies
• Python 3, plus regex, pysam, numpy, and scipy libraries. All the .py scripts are written in Python 3.
• R, plus the ada package in R.
• BEDTools (if there is an inclusion and/or an exclusion region file)
• Optional: dbSNP and COSMIC files in VCF format (if you want to use dbSNP features as a part of
the training).
• At least one of MuTect/Indelocator/MuTect2, VarScan2, JointSNVMix2, SomaticSniper, VarDict,
MuSE, LoFreq, Scalpel, Strelka2 and/or TNscope. Those are the tools we have incorporated in SomaticSeq. If there are other somatic tools that may be good addition to our list, please make the
suggestion to us.
1.2
Docker repos
SomaticSeq and most somatic mutation callers we have incorporated are now dockerized. The exceptions
are VarScan2 and TNscope because we do not have distribution rights.
• SomaticSeq is dockerized at https://hub.docker.com/r/lethalfang/somaticseq/.
• MuTect2 (tested with GATK4): https://hub.docker.com/r/broadinstitute/gatk
• VarScan2 (untested): https://hub.docker.com/r/djordjeklisic/sbg-varscan2/
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• JointSNVMix2: https://hub.docker.com/r/lethalfang/jointsnvmix2/
• SomaticSniper: https://hub.docker.com/r/lethalfang/somaticsniper/
• VarDict: https://hub.docker.com/r/lethalfang/vardictjava/
• MuSE: https://hub.docker.com/r/marghoob/muse/
• LoFreq: https://hub.docker.com/r/marghoob/lofreq/
• Scalpel: https://hub.docker.com/r/lethalfang/scalpel/
• Strelka2: https://hub.docker.com/r/lethalfang/strelka/
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To use SomaticSeq.Wrapper.sh
The SomaticSeq.Wrapper.sh is a wrapper script that calls a series of programs and procedures after you
have run your individual somatic mutation callers. In the next section, we will describe the workflow in more
detail, so you may not be dependent on this wrapper script. You can either modify this wrapper script or
create your own workflow.
2.1
To train data set into a classifier
To create a trained classifier, ground truth files are required for the data sets. There is also an option to
include a list of regions to include and/or exclude. The exclusion or inclusion regions can be VCF or BED
files. An inclusion region may be subset of the call sets where you have validated their true/false mutation
status, so that only those regions will be used for training. An exclusion region can be regions where the
“truth” is ambigious.
All the output VCF files from individual callers are optional. Those VCF files can be bgzipped if they
have .vcf.gz extensions. It is imperative that the parameters used for the training and prediction are identical.
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# For t r a i n i n g , truth f i l e and the c o r r e c t R s c r i p t are r e q u i r e d .
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SomaticSeq . Wrapper . sh \
−−mutect
MuTect/ v a r i a n t s . snp . v c f \
−−mutect2
MuTect2/ v a r i a n t s . v c f \
−−i n d e l o c a t o r
Indelocator / variants . indel . vcf \
−−varscan−snv
VarScan2/ v a r i a n t s . snp . v c f \
−−varscan−i n d e l
VarScan2/ v a r i a n t s . i n d e l . v c f \
−−jsm
JointSNVMix2/ v a r i a n t s . snp . v c f \
−−s n i p e r
SomaticSniper / v a r i a n t s . snp . v c f \
−−v a r d i c t
VarDict/ v a r i a n t s . v c f \
−−muse
MuSE/ v a r i a n t s . snp . v c f \
−−l o f r e q −snv
LoFreq/ v a r i a n t s . snp . v c f \
−−l o f r e q −i n d e l
LoFreq/ v a r i a n t s . i n d e l . v c f \
−−s c a l p e l
Scalpel / variants . indel . vcf \
−−s t r e l k a −snv
S t r e l k a / v a r i a n t s . snv . v c f \
−−s t r e l k a −i n d e l
Strelka / variants . indel . vcf \
−−tnscope
TNscope . v c f . gz \
−−normal−bam
matched_normal . bam \
−−tumor−bam
tumor . bam \
−−ada−r−s c r i p t
ada_model_builder .R \
−−genome−r e f e r e n c e human_b37 . f a s t a \
−−cosmic
cosmic . b37 . v71 . v c f \
−−dbsnp
dbSNP . b37 . v141 . v c f \
−−e x c l u s i o n −r e g i o n i g n o r e . bed \
−−i n c l u s i o n −r e g i o n v a l i d a t e d . bed
−−truth−snv
truth . snp . v c f \
−−truth−i n d e l
truth . i n d e l . v c f \
−−output−d i r
$OUTPUT_DIR
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SomaticSeq.Wrapper.sh supports any combination of the somatic mutation callers we have incorporated
into the workflow. SomaticSeq will run based on the output VCFs you have provided. It will train for SNV
and/or INDEL if you provide the truth.snp.vcf and/or truth.indel.vcf file(s) as well as the proper R script
(ada_model_builder.R). Otherwise, it will fall back to the simple caller consensus mode.
2.2
To predict somatic mutation based on trained classifiers
Make sure the classifiers and the proper R script (ada_model_predictor.R) are supplied, Without either of
them, it will fall back to the simple caller consensus mode.
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# The ∗ . RData f i l e s are t r a i n e d c l a s s i f i e r from the t r a i n i n g mode .
SomaticSeq . Wrapper . sh \
−−mutect
MuTect/ v a r i a n t s . snp . v c f \
−−mutect2
MuTect2/ v a r i a n t s . v c f \
−−i n d e l o c a t o r
Indelocator / variants . indel . vcf \
−−varscan−snv
VarScan2/ v a r i a n t s . snp . v c f \
−−varscan−i n d e l
VarScan2/ v a r i a n t s . i n d e l . v c f \
−−jsm
JointSNVMix2/ v a r i a n t s . snp . v c f \
−−s n i p e r
SomaticSniper / v a r i a n t s . snp . v c f \
−−v a r d i c t
VarDict/ v a r i a n t s . v c f \
−−muse
MuSE/ v a r i a n t s . snp . v c f \
−−l o f r e q −snv
LoFreq/ v a r i a n t s . snp . v c f \
−−l o f r e q −i n d e l
LoFreq/ v a r i a n t s . i n d e l . v c f \
−−s c a l p e l
Scalpel / variants . indel . vcf \
−−s t r e l k a −snv
S t r e l k a / v a r i a n t s . snv . v c f \
−−s t r e l k a −i n d e l
Strelka / variants . indel . vcf \
−−tnscope
TNscope . v c f . gz \
−−normal−bam
matched_normal . bam \
−−tumor−bam
tumor . bam \
−−ada−r−s c r i p t
ada_model_predictor .R \
−−genome−r e f e r e n c e human_b37 . f a s t a \
−−cosmic
cosmic . b37 . v71 . v c f \
−−dbsnp
dbSNP . b37 . v141 . v c f \
−−s n p e f f −d i r
$PATH/TO/DIR/ s n p S i f t \
−−e x c l u s i o n −r e g i o n i g n o r e . bed \
−−i n c l u s i o n −r e g i o n v a l i d a t e d . bed
−−c l a s s i f i e r −snv
sSNV . C l a s s i f i e r . RData \
−−c l a s s i f i e r −i n d e l sINDEL . C l a s s i f i e r . RData \
−−pass−t h r e s h o l d
0.5 \
−−lowqual−t h r e s h o l d 0 . 1 \
−−output−d i r
$OUTPUT_DIR
If both MuTect2 and MuTect/Indelocator VCF files are provided, the script is written such that it will
use MuTect2 and ignore MuTect.
2.3
To classify based on simple majority vote
Same as the command previously, but not including the R script or the ground truth files. Without those
information, SomaticSeq will fall back into a simple majority vote.
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The step-by-step SomaticSeq Workflow
We’ll describe the workflow here, so you may modify the workflow and/or create your own workflow instead
of using the wrapper script described previously.
3.1
Combine the call sets
We use utilities/getUniqueVcfPositions.py and vcfsorter.pl to combine the VCF files from different callers.
The intermediate VCF files were modified to separate SNVs and INDELs.
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VCF modifications were previously done to primarily make them compatible with GATK CombineVariants. We no longer depend on CombineVariants, so some of the steps are legacy.
1. Modify MuTect and/or Indelocator output VCF files. Since MuTect’s output VCF do not always put
the tumor and normal samples in the same columns, the script will determine that information based
on either the BAM files (the header has sample name information), or based on the sample information
that you tell it, and then determine which column belongs to the normal, and which column belongs
to the tumor.
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# Modify MuTect and I n d e l o c a t o r ’ s output VCF based on BAM f i l e s
modify_MuTect . py − i n f i l e input . v c f −o u t f i l e output . v c f −nbam normal . bam −tbam tumor . bam
# Based on the sample name you supply :
modify_MuTect . py − i n f i l e input . v c f −o u t f i l e output . v c f −nsm NormalSampleName −tsm
TumorSampleName
2. For MuTect2, this script will split multi-allelic records into one variant per line in the VCF file. This
is to make thing easier for the SSeq_merged.vcf2tsv.py script later.
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# Based on the sample name you supply :
modify_MuTect2 . py − i n f i l e MuTect2 . F i l t e r e d . v c f −snv mutect . snp . v c f −i n d e l mutect . i n d e l . v c f
3. Modify VarScan’s output VCF files to be rigorously concordant to VCF format standard, and to attach
the tag ’VarScan2’ to somatic calls.
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# Do i t f o r both the SNV and i n d e l
modify_VJSD . py −method VarScan2 − i n f i l e input . v c f −o u t f i l e output . v c f
4. JointSNVMix2 does not output VCF files. In our own workflow, we have already converted its text
file into a basic VCF file with an 2 awk one-liners, which you may see in the Run_5_callers directory,
which are:
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# To avoid t e x t f i l e s i n the order o f t e r a b y t e s , t h i s awk one−l i n e r keeps e n t r i e s where the
r e f e r e n c e i s not ”N” , and the somatic p r o b a b i l i t i e s are at l e a s t 0 . 9 5 .
awk −F ”\ t ” ’NR!=1 && $4!=”N” && $10+$11 >=0.95’
# This awk one−l i n e r c o n v e r t s the t e x t f i l e i n t o a b a s i c VCF f i l e
awk −F ”\ t ” ’{ p r i n t $1 ”\ t ” $2 ”\ t . \ t ” $3 ”\ t ” $4 ”\ t . \ t . \tAAAB=” $10 ” ;AABB=” $11 ”\tRD:AD\
t ” $5 ” : ” $6 ”\ t ” $7 ” : ” $8 } ’
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## The a c t u a l commands we ’ ve used i n our workflow :
echo −e ’##f i l e f o r m a t=VCFv4. 1 ’ > unsorted . v c f
echo −e ’##INFO=’ >> unsorted . v c f
echo −e ’##INFO=’ >> unsorted . v c f
echo −e ’##FORMAT=’ >> unsorted . v c f
echo −e ’##FORMAT=’ >> unsorted . v c f
echo −e ’#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tNORMAL\tTUMOR’ >> unsorted .
vcf
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python $PATH/TO/jsm . py c l a s s i f y joint_snv_mix_two genome .GRCh37. f a normal . bam tumor . bam
t r a i n e d . parameter . c f g /dev/ stdout | \
awk −F ”\ t ” ’NR!=1 && $4!=”N” && $10+$11 >=0.95’ | \
awk −F ”\ t ” ’{ p r i n t $1 ”\ t ” $2 ”\ t . \ t ” $3 ”\ t ” $4 ”\ t . \ t . \tAAAB=” $10 ” ;AABB=” $11 ”\tRD:AD\
t ” $5 ” : ” $6 ”\ t ” $7 ” : ” $8 } ’ >> unsorted . v c f
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After that, you’ll also want to sort the VCF file. Now, to modify that basic VCF into something that
will be compatible with other VCF files under GATK CombineVariants:
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modify_VJSD . py −method JointSNVMix2 − i n f i l e input . v c f −o u t f i l e output . v c f
5. Modify SomaticSniper’s output:
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modify_VJSD . py −method SomaticSniper − i n f i l e input . v c f −o u t f i l e output . v c f
6. VarDict has both SNV and indel, plus some other variants in the same VCF file. Our script will
create two files, one for SNV and one for indel, while everything else is ignored for now. By default,
LikelySomatic/StrongSomatic and PASS calls will be labeled VarDict. However, in our SomaticSeq
paper, based on our experience in DREAM Challenge, we implemented two custom filters to relax the
VarDict tagging criteria.
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# Default VarDict tagging c r i t e r i a , only PASS ( and L i k e l y or Strong Somatic ) :
modify_VJSD . py −method VarDict − i n f i l e i n t p u t . v c f −o u t f i l e output . v c f
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# When running VarDict , i f var2vcf_paired . p l i s used to g e n e r a t e the VCF f i l e , you may r e l a x
the tagging c r i t e r i a with − f i l t e r p a i r e d
modify_VJSD . py −method VarDict − i n f i l e i n t p u t . v c f −o u t f i l e output . v c f − f i l t e r p a i r e d
# When running VarDict , i f var2vcf_somatic . p l i s used to g e n e r a t e the VCF f i l e , you may
r e l a x the tagging c r i t e r i a with − f i l t e r somatic
modify_VJSD . py −method VarDict − i n f i l e i n t p u t . v c f −o u t f i l e output . v c f − f i l t e r somatic
In the SomaticSeq paper, -filter somatic was used because var2vcf_somatic.pl was used to generate
VarDict’s VCF files. In the SomaticSeq.Wrapper.sh script, however, -filter paired is used because
VarDict authors have since recommended var2vcf_paired.pl script to create the VCF files. While there
are some differences (different stringencies in some filters) in what VarDict labels as PASS between
the somatic.pl and paired.pl scripts, the difference is miniscule after applying our custom filter (which
relaxes the filter, resulting in a difference about 5 calls out of 15,000).
The output files will be snp.output.vcf and indel.output.vcf.
7. MuSE was not a part of our analysis in the SomaticSeq paper. We have implemented it later.
modify_VJSD . py −method MuSE − i n f i l e input . v c f −o u t f i l e output . v c f
8. LoFreq and Scalpel do not require modification. LoFreq has no sample columns anyway.
9. Add “GT” field to sample columns to make it compatible with GATK CombineVariants.
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modify_Strelka . py − i n f i l e somatic . snvs . v c f . gz −o u t f i l e s t r a l k a . snv . v c f
10. Finally, with the VCF files modified, you may combine them with GATK CombineVariants: one for
SNV and one for indel separately. There is no particular reason to use GATK CombineVariants.
Other combiners should also work. The only useful thing here is to combine the calls and GATK
CombineVariants does it well and pretty fast.
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3.2
# Combine the VCF f i l e s f o r SNV. Any or a l l o f the VCF f i l e s may be p r e s e n t .
# −nt 6 means to use 6 threads i n p a r a l l e l
java −j a r $PATH/TO/GenomeAnalysisTK . j a r −T CombineVariants −R genome .GRCh37. f a −nt 6 −−
setKey n u l l −−genotypemergeoption UNSORTED −V mutect . v c f −V varscan . snp . v c f −V
jointsnvmix . v c f −V snp . v a r d i c t . v c f −V muse . v c f −−out CombineVariants . snp . v c f
java −j a r $PATH/TO/GenomeAnalysisTK . j a r −T CombineVariants −R genome .GRCh37. f a −nt 6 −−
setKey n u l l −−genotypemergeoption UNSORTED −V i n d e l o c a t o r . v c f −V varscan . snp . v c f −V
i n d e l . v a r d i c t . v c f −−out CombineVariants . i n d e l . v c f
For model training: process and annotate the VCF files (union of call sets)
This step may be needed for model training. The workflow in SomaticSeq.Wrapper.sh allows for inclusion
and exclusion region. An inclusion region means we will only use calls inside these regions. An exclusion
region means we do not care about calls inside this region. DREAM Challenge had exclusion regions, e.g.,
blacklisted regions, etc.
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# In the DREAM_Stage_3 d i r e c t o r y , we have i n c l u d e d an e x c l u s i o n r e g i o n BED f i l e as an example
# This command us e s BEDtools to r i d o f a l l c a l l s i n the e x c l u s i o n r e g i o n
i n t e r s e c t B e d −header −a BINA_somatic . snp . v c f
−b i g n o r e . bed −v > somatic . snp . p r o c e s s e d . v c f
i n t e r s e c t B e d −header −a BINA_somatic . i n d e l . v c f −b i g n o r e . bed −v > somatic . i n d e l . p r o c e s s e d . v c f
# A l t e r n a t i v e l y ( or both ) , t h i s command us es BEDtools to keep only c a l l s i n the i n c l u s i o n r e g i o n
i n t e r s e c t B e d −header −a BINA_somatic . snp . v c f
−b i n c l u s i o n . bed > somatic . snp . p r o c e s s e d . v c f
i n t e r s e c t B e d −header −a BINA_somatic . i n d e l . v c f −b i n c l u s i o n . bed > somatic . i n d e l . p r o c e s s e d . v c f
3.3
Convert the VCF file, annotated or otherwise, into a tab separated file
This script works for all VCF files. It extracts information from BAM files as well as some VCF files created
by the individual callers. If the ground truth VCF file is included, a called variant will be annotated as a
true positive, and everything will be annotated as a false positive.
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# SNV
SSeq_merged . v c f 2 t s v . py −r e f genome .GRCh37. f a −myvcf somatic . snp . p r o c e s s e d . v c f −truth Ground . truth
. snp . v c f −mutect MuTect/ v a r i a n t s . snp . v c f . gz −varscan VarScan2/ v a r i a n t s . snp . v c f −jsm JSM2/
v a r i a n t s . v c f −s n i p e r SomaticSniper / v a r i a n t s . v c f −v a r d i c t VarDict/snp . v a r i a n t s . v c f −muse MuSE/
v a r i a n t s . v c f −l o f r e q LoFreq/ v a r i a n t s . snp . v c f −s t r e l k a S t r e l k a / v a r i a n t s . snp . v c f −dedup −tbam
tumor . bam −nbam normal . bam −o u t f i l e Ensemble . sSNV . t s v
That was for SNV, and indel is almost the same thing. After version 2.1, we have replaced all information
from SAMtools and HaplotypeCaller with information directly from the BAM files. The accuracy differences
are negligible with significant improvement in usability and resource requirement.
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# INDEL:
SSeq_merged . v c f 2 t s v . py −r e f genome .GRCh37. f a −myvcf somatic . i n d e l . p r o c e s s e d . v c f −truth Ground .
truth . i n d e l . v c f −varscan VarScan2/ v a r i a n t s . snp . v c f −v a r d i c t VarDict/ i n d e l . v a r i a n t s . v c f −
l o f r e q LoFreq/ v a r i a n t s . i n d e l . v c f −s c a l p e l S c a l p e l / v a r i a n t s . i n d e l . v c f −s t r e l k a S t r e l k a /
v a r i a n t s . i n d e l . v c f −tbam tumor . bam −nbam normal . bam −dedup −o u t f i l e Ensemble . sINDEL . t s v
At the end of this, Ensemble.sSNV.tsv and Ensemble.sINDEL.tsv are created.
All the options for SSeq_merged.vcf2tsv.py are listed here. They can also be displayed by running
SSeq_merged.vcf2tsv.py --help.
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−myvcf
−r e f
Input VCF f i l e o f the merged c a l l s [REQUIRED]
Genome r e f e r e n c e f a / f a s t a f i l e [REQUIRED]
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−nbam
BAM f i l e o f the matched normal sample [REQUIRED]
−tbam
BAM f i l e o f the tumor sample [REQUIRED]
−r e f
Genome r e f e r e n c e f a / f a s t q f i l e [REQUIRED]
−truth
Ground truth VCF f i l e . Every other p o s i t i o n i s a False P o s i t i v e .
−dbsnp
dbSNP VCF f i l e
−cosmic
COSMIC VCF f i l e
−mutect
VCF f i l e from e i t h e r MuTect2 , MuTect , or I n d e l o c a t o r
−s n i p e r
VCF f i l e from SomaticSniper
−varscan
VCF f i l e from VarScan2
−jsm
VCF f i l e from Bina ’ s workflow that c o n t a i n s JointSNVMix2
−v a r d i c t
VCF f i l e that c o n t a i n s only SNV or only INDEL from VarDict
−muse
VCF f i l e from MuSE
−l o f r e q
VCF f i l e from LoFreq
−s c a l p e l
VCF f i l e from S c a l p e l
−s t r e l k a
VCF f i l e from S t r e l k a
−dedup
A f l a g to c o n s i d e r only primary reads
−minMQ
Minimum mapping q u a l i t y f o r reads to be c o n s i d e r e d ( Default = 1)
−minBQ
Minimum base q u a l i t y f o r reads to be c o n s i d e r e d ( Default = 5)
−m i n c a l l e r
Minimum number o f c a l l e r c l a s s i f i c a t i o n f o r a c a l l to be c o n s i d e r e d ( Use 0 . 5 to
c o n s i d e r some LowQual c a l l s . Default = 0) .
−s c a l e
The o p t i o n s are phred , f r a c t i o n , or None , to convert numbers to Phred s c a l e or
f r a c t i o n a l s c a l e . ( d e f a u l t = None , i . e . , no c o n v e r s i o n )
−o u t f i l e
Output TSV f i l e name
Note: Do not worry if Python throws a warning like this.
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RuntimeWarning : i n v a l i d value encountered i n double_scalars
z = ( s − expected ) / np . s q r t ( n1∗n2∗ ( n1+n2+1) / 1 2 . 0 )
This is to tell you that scipy was attempting some statistical test with empty data. That’s usually due
to the fact that normal BAM file has no variant reads at that given position. That is why lots of values are
NaN for the normal.
3.4
Model Training or Mutation Prediction
You can use Ensemble.sSNV.tsv and Ensemble.sINDEL.tsv files either for model training (provided that their
mutation status is annotated with 0 or 1) or mutation prediction. This is done with stochastic boosting
algorithm we have implemented in R.
Model training:
2
# Training :
r _ s c r i p t /ada_model_builder .R Ensemble . sSNV . t s v
r _ s c r i p t /ada_model_builder .R Ensemble . sINDEL . t s v
Ensemble.sSNV.tsv.Classifier.RData and Ensemble.sINDEL.tsv.Classifier.RData will be created from
model training.
Mutation prediction:
1
3
# Mutation p r e d i c t i o n :
r _ s c r i p t /ada_model_predictor .R Ensemble . sSNV . t s v . C l a s s i f i e r . RData
Ensemble . sSNV . t s v
Trained .
sSNV . t s v
r _ s c r i p t /ada_model_predictor .R Ensemble . sINDEL . t s v . C l a s s i f i e r . RData Ensemble . sINDEL . t s v Trained .
sINDEL . t s v
After mutation prediction, if you feel like it, you may convert Trained.sSNV.tsv and Trained.sINDEL.tsv
into VCF files. Use -tools to list ONLY the individual tools used to have appropriately annotated VCF
files. Accepted tools are CGA (for MuTect/Indelocator), VarScan2, JointSNVMix2, SomaticSniper, VarDict,
7
MuSE, LoFreq, and/or Scalpel. To list a tool without having run it, the VCF will be annotated as if the
tool was run but did not identify that position as a somatic variant.
1
3
5
7
# P r o b a b i l i t y above 0 . 7 l a b e l e d PASS (−pass 0 . 7 ) , and between 0 . 1 and 0 . 7 l a b e l e d LowQual (−low
0.1) :
# Use −a l l to i n c l u d e REJECT c a l l s i n the VCF f i l e
# Use −phred to convert p r o b a b i l i t y v a l u e s ( between 0 to 1) i n t o Phred s c a l e i n the QUAL column
i n the VCF f i l e
SSeq_tsv2vcf . py −t s v Trained . sSNV . t s v
−v c f Trained . sSNV . v c f
−pass 0 . 7 −low 0 . 1 −t o o l s CGA
VarScan2 JointSNVMix2 SomaticSniper VarDict MuSE LoFreq S t r e l k a −a l l −phred
SSeq_tsv2vcf . py −t s v Trained . sINDEL . t s v −v c f Trained . sINDEL . v c f −pass 0 . 7 −low 0 . 1 −t o o l s CGA
VarScan2 VarDict LoFreq S c a l p e l S t r e l k a −a l l −phred
4
To run the dockerized somatic mutation callers
For your convenience, we have created a couple of scripts that can generate run script for the dockerized
somatic mutation callers.
4.1
Location
• somaticseq/utilities/dockered_pipelines/
4.2
Requirements
• Have internet connection, and able to pull and run docker images from docker.io
• Have cluster management system such as Sun Grid Engine, so that the ”qsub” command is valid
4.3
Example commands
For single-threaded jobs, best suited for whole exome sequencing or less.
1
3
5
7
9
# Example command to submit the run s c r i p t s f o r each o f the f o l l o w i n g somatic mutation c a l l e r s
u t i l i t i e s / d ockere d_pipe lines / singleThread / submit_callers_singleThread . sh \
−−normal−bam
/ABSOLUTE/PATH/TO/normal_sample . bam \
−−tumor−bam
/ABSOLUTE/PATH/TO/tumor_sample . bam \
−−human−r e f e r e n c e /ABSOLUTE/PATH/TO/GRCh38. f a \
−−output−d i r
/ABSOLUTE/PATH/TO/RESULTS \
−−s e l e c t o r
/ABSOLUTE/PATH/TO/Exome_Capture .GRCh38. bed \
−−dbsnp
/ABSOLUTE/PATH/TO/dbSNP .GRCh38. v c f \
−−a c t i o n
qsub \
−−mutect2 −−jointsnvmix2 −−s o m a t i c s n i p e r −−v a r d i c t −−muse −−l o f r e q −−s c a l p e l −−s t r e l k a −−
somaticseq
For multi-threaded jobs, best suited for whole genome sequencing.
2
4
6
8
10
# Submitting mutation c a l l e r j o b s by s p l i t t i n g each job i n t o 36 even r e g i o n s .
u t i l i t i e s / d ockere d_pipe lines / multiThreads / submit_callers_multiThreads . sh \
−−normal−bam
/ABSOLUTE/PATH/TO/normal_sample . bam \
−−tumor−bam
/ABSOLUTE/PATH/TO/tumor_sample . bam \
−−human−r e f e r e n c e /ABSOLUTE/PATH/TO/GRCh38. f a \
−−output−d i r
/ABSOLUTE/PATH/TO/RESULTS \
−−dbsnp
/ABSOLUTE/PATH/TO/dbSNP .GRCh38. v c f \
−−threads
36 \
−−a c t i o n
qsub \
−−mutect2 −−s o m a t i c s n i p e r −−v a r d i c t −−muse −−l o f r e q −−s c a l p e l −−s t r e l k a −−somaticseq
8
4.3.1
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
Parameters
−−normal−bam
/ABSOLUTE/PATH/TO/normal_sample . bam ( Required )
−−tumor−bam
/ABSOLUTE/PATH/TO/tumor_sample . bam ( Required )
−−human−r e f e r e n c e
/ABSOLUTE/PATH/TO/human_reference . f a ( Required )
−−dbsnp
/ABSOLUTE/PATH/TO/dbsnp . v c f ( Required f o r MuSE and LoFreq )
−−cosmic
/ABSOLUTE/PATH/TO/ cosmic . v c f ( Optional )
−−s e l e c t o r
/ABSOLUTE/PATH/TO/Capture_region . bed ( Optional . Will assume whole
genome from the . f a i f i l e without i t . )
−−exclude
/ABSOLUTE/PATH/TO/ B l a c k l i s t _ r e g i o n . bed ( Optional )
−−min−a f
( Optional . The minimum VAF c u t o f f f o r VarDict and VarScan2 .
D e f a u l t s are 0 . 1 0 f o r VarScan2 and 0 . 0 5 f o r VarDict ) .
−−a c t i o n
qsub ( Optional : the command preceding the . cmd s c r i p t s . Default i s
echo )
−−threads
36 ( Optional f o r multiThreads and i n v a l i d f o r singleThread : evenly
s p l i t the genome i n t o 36 BED f i l e s . Default = 12) .
−−mutect2
( Optional f l a g to invoke MuTect2)
−−varscan2
( Optional f l a g to invoke VarScan2 )
−−jointsnvmix2
( Optional f l a g to invoke JointSNVMix2 )
−−s o m a t i c s n i p e r
( Optional f l a g to invoke SomaticSniper )
−−v a r d i c t
( Optional f l a g to invoke VarDict )
−−muse
( Optional f l a g to invoke MuSE)
−−l o f r e q
( Optional f l a g to invoke LoFreq )
−−s c a l p e l
( Optional f l a g to invoke S c a l p e l )
−−s t r e l k a
( Optional f l a g to invoke S t r e l k a )
−−somaticseq
( Optional f l a g to invoke SomaticSeq . This s c r i p t always be echo ’ ed ,
as i t should not be submitted u n t i l a l l the c a l l e r s above complete ) .
−−output−d i r
/ABSOLUTE/PATH/TO/OUTPUT_DIRECTORY ( Required )
−−somaticseq−d i r
SomaticSeq_Output_Directory ( Optional . The d i r e c t o r y name o f the
SomaticSeq output . Default = SomaticSeq ) .
−−somaticseq−t r a i n
( Optional f l a g to invoke SomaticSeq to produce c l a s s i f i e r s i f
ground truth VCF f i l e s are provided . Only recommended i n singleThread mode , because o t h e r w i s e
i t ’ s b e t t e r to combine the output TSV f i l e s f i r s t , and then t r a i n c l a s s i f i e r s . )
−−somaticseq−a c t i o n
( Optional . What to do with the somaticseq . cmd . Default i s echo .
Only do ”qsub” i f you have a l r e a d y completed a l l the mutation c a l l e r s , but want to run
SomaticSeq at a d i f f e r e n t s e t t i n g . )
−−c l a s s i f i e r −snv
Trained_sSNV_Classifier . RData ( Optional i f t h e r e i s a c l a s s i f e r you
want to use )
−−c l a s s i f i e r −i n d e l
Trained_sINDEL_Classifier . RData ( Optional i f t h e r e i s a c l a s s i f e r
you want to use )
−−truth−snv
sSNV_ground_truth . v c f ( Optional i f t h e r e i s a ground truth , and
e v e ry t h i n g e l s e w i l l be l a b e l e d f a l s e p o s i t i v e )
−−truth−i n d e l
sINDEL_ground_truth . v c f ( Optional i f t h e r e i s a ground truth , and
e v e ry t h i n g e l s e w i l l be l a b e l e d f a l s e p o s i t i v e )
−−exome
( Optional f l a g f o r S t r e l k a )
−−s c a l p e l −two−pass
( Optional parameter f o r S c a l p e l . Default = f a l s e . )
−−mutect2−arguments
( Extra parameters to pass onto Mutect2 , e . g . , −−mutect2−arguments
’−−initial_tumor_lod 3 . 0 −−log_somatic_prior −5.0 −−min_base_quality_score 2 0 ’ )
−−mutect2−f i l t e r −arguments
( Extra parameters to pass onto F i l t e r M u t e c t C a l l s )
−−varscan−arguments
( Extra parameters to pass onto VarScan2 )
−−varscan−pileup−arguments
( Extra parameters to pass onto samtools mpileup that c r e a t e s p i l e u p
f i l e s f o r VarScan )
−−jsm−t r a i n −arguments
( Extra parameters to pass onto JointSNVMix2 ’ s t r a i n command)
−−jsm−c l a s s i f y −arguments
( Extra parameters to pass onto JointSNVMix2 ’ s c l a s s i f y command)
−−somaticsniper−arguments
( Extra parameters to pass onto SomaticSniper )
−−v a r d i c t−arguments
( Extra parameters to pass onto VarDict )
−−muse−arguments
( Extra parameters to pass onto MuSE)
−−l o f r e q −arguments
( Extra parameters to pass onto LoFreq )
−−s c a l p e l −discovery−arguments ( Extra parameters to pass onto S c a l p e l ’ s d i s c o v e r y command)
−−s c a l p e l −export−arguments
( Extra parameters to pass onto S c a l p e l ’ s export command)
−−s t r e l k a −c o n f i g −arguments
( Extra parameters to pass onto S t r e l k a ’ s c o n f i g command)
−−s t r e l k a −run−arguments
( Extra parameters to pass onto S t r e k l a ’ s run command)
−−somaticseq−arguments
( Extra parameters to pass onto SomaticSeq . Wrapper . sh )
9
4.3.2
What does the single-threaded command do
• For each flag such as --mutect2, --jointsnvmix2, ...., --strelka, a run script ending with .cmd will
be created in /ABSOLUTE/PATH/TO/RESULTS/logs. By default, these .cmd scripts will only be
created, and their file path will be printed on screen. However, if you do “--action qsub”, then these
scripts will be submitted via the qsub command. The default action is “echo.”
– Each of these .cmd script correspond to a mutation caller you specified. They all use docker
images.
– We may improve their functionalities in the future to allow more tunable parameters. For the
initial releases, POC and reproducibility take precedence.
• If you do “--somaticseq,” the somaticseq script will be created in /ABSOLUTE/PATH/TO/RESULTS/SomaticSeq/logs. However, it will not be submitted until you manually do so after each of these
mutation callers is finished running.
– In the future, we may create more sophisticated solution that will automatically solves these
dependencies. For the initial release, we’ll focus on stability and reproducibility.
• Due to the way those run scripts are written, the Sun Grid Engine’s standard error log will record the
time the task completes (i.e., Done at 2017/10/30 29:03:02), and it will only do so when the task is
completed with an exit code of 0. It can be a quick way to check if a task is done, by looking at the
final line of the standard error log file.
4.3.3
What does the multi-threaded command do
It’s very similar to the single-threaded WES solution, except the job will be split evenly based on genomic
lengths.
• If you specified “--threads 36,” then 36 BED files will be created. Each BED file represents 1/36
of the total base pairs in the human genome (obtained from the .fa.fai file, but only including 1,
2, 3, ..., MT, or chr1, chr2, ..., chrM contigs). They are named 1.bed, 2.bed, ..., 36.bed, and will
be created into /ABSOLUTE/PATH/TO/RESULTS/1, /ABSOLUTE/PATH/TO/RESULTS/2, ...,
/ABSOLUTE/PATH/TO/RESULTS/36. You may, of course, specify any number. The default is 12.
• For each mutation callers you specify (with the exception of SomaticSniper), a script will be created
into /ABSOLUTE/PATH/TO/RESULTS/1/logs, /ABSOLUTE/PATH/TO/RESULTS/2/logs, etc.,
with partial BAM input. Again, they will be automatically submitted if you do “--action qsub.”
• Because SomaticSniper does not support partial BAM input (one would have to manually split the
BAMs in order to parallelize SomaticSniper this way), the above mentioned procedure is not applied
to SomaticSniper. Instead, a single-threaded script will be created (and potentially qsub’ed) into
/ABSOLUTE/PATH/TO/RESULTS/logs.
– However, because SomaticSniper is by far the fastest tool there, single-thread is doable even for
WGS. Even single-threaded SomaticSniper will likely finish before parallelized Scalpel. When I
benchmarked the DREAM Challenge Stage 3 by splitting it into 120 regions, Scalpel took 10
hours and 10 minutes to complete 1/120 of the data. SomaticSniper took a little under 5 hours
for the whole thing.
– After SomaticSniper finishes, the result VCF files will be split into each of the /ABSOLUTE/PATH/TO/RESULTS/1, /ABSOLUTE/PATH/TO/RESULTS/2, etc.
• JointSNVMix2 also does not support partial BAM input. Unlike SomaticSniper, it’s slow and takes
massive amount of memory. It’s not a good idea to run JointSNVMix2 on a WGS data. The only way
to do so is to manually split the BAM files and run each separately. We may do so in the future, but
JointSNVMix2 is a 5-year old that’s no longer being supported, so we probably won’t bother.
10
• Like the single-threaded case, a SomaticSeq run script will also be created for each partition like
/ABSOLUTE/PATH/TO/RESULTS/1/SomaticSeq/logs, but will not be submitted until you do so
manually.
– For simplicity, you may wait until all the mutation calling is done, then run a command like
1
5
f i n d /ABSOLUTE/PATH/TO/RESULTS −name ’ somaticseq ∗ . cmd’ −exec qsub {} \ ;
Use BAMSurgeon to create training set
For your convenience, we have created a couple of wrapper scripts that can generate the run script to create
training data using BAMSurgon at somaticseq/utilities/dockered_pipelines/bamSimulator. Descriptions
and example commands can be found in the README there.
5.1
Requirements
• Have internet connection, and able to pull and run docker images from docker.io
• Have cluster management system such as Sun Grid Engine, so that the ”qsub” command is valid
6
Release Notes
Make sure training and prediction use the same version. Otherwise the prediction is not valid.
6.1
Version 1.0
Version used to generate data in the manuscript and Stage 5 of the ICGC-TCGA DREAM Somatic Mutation
Challenge, where SomaticSeq’s results were #1 for INDEL and #2 for SNV.
In the original manuscript, VarDict’s var2vcf_somatic.pl script was used to generate VarDict VCFs,
and subsequently “-filter somatic” was used for SSeq_merged.vcf2tsv.py. Since then (including DREAM
Challenge Stage 5), VarDict recommends var2vcf_paired.pl over var2vcf_somatic.pl, and subsequently “filter paired” was used for SSeq_merged.vcf2tsv.py. The difference in SomaticSeq results, however, is pretty
much negligible.
6.2
Version 1.1
Automated the SomaticSeq.Wrapper.sh script for both training and prediction mode. No change to any
algorithm.
6.3
Version 1.2
Have implemented the following improvement, mostly for indels:
• SSeq_merged.vcf2tsv.py can now accept pileup files to extract read depth and DP4 (reference forward,
reference reverse, alternate forward, and alternate reverse) information (mainly for indels). Previously,
that information can only be extracted from SAMtools VCF. Since the SAMtools or HaplotypeCaller
generated VCFs hardly contain any indel information, this option improves the indel model. The
SomaticSeq.Wrapper.sh script is modified accordingly.
• Extract mapping quality (MQ) from VarDict output if this information cannot be found in SAMtools
VCF (also mostly benefits the indel model).
• Indel length now positive for insertions and negative for deletions, instead of using the absolute value
previously.
11
6.4
Version 2.0
• Removed dependencies for SAMtools and HaplotypeCaller during feature extraction.
SSeq_merged.vcf2tsv.py extracts those information (plus more) directly from BAM files.
• Allow not only VCF file, but also BED file or a list of chromosome coordinate as input format for
SSeq_merged.vcf2tsv.py, i.e., use -mybed or -mypos instead of -myvcf.
• Instead of a separate step to annotate ground truth, that can be done directly by
SSeq_merged.vcf2tsv.py by supplying the ground truth VCF via -truth.
• SSeq_merged.vcf2tsv.py can annotate dbSNP and COSMIC information directly if BED file or a list
of chromosome coordinates are used as input in lieu of an annotated VCF file.
• Consolidated feature sets, e.g., removed some redunda Fixed a bug: if JointSNVMix2 is not included,
the values should be “NaN” instead of 0’s. This is to keep consistency with how we handle all other
callersnt feature sets coming from different resources.
6.5
Version 2.0.2
• Incorporated LoFreq.
• Used getopt to replace getopts in the SomaticSeq.Wrapper.sh script to allow long options.
6.6
Version 2.1.2
• Properly handle cases when multiple ALT’s are calls in the same position. The VCF files can either
contain multiple calls in the ALT column (i.e., A,G), or have multiple lines corresponding to the same
position (one line for each variant call). Some functions were significantly re-written to allow this.
• Incorporated Scalpel.
• Deprecated HaplotypeCaller and SAMTools dependencies completely as far as feature generation is
concerned.
• The Wrapper script removed SnpSift/SnpEff dependencies. Those information can be directly obtained
during the SSeq_merged.vcf2tsv.py step. Also removed some additional legacy steps that has become
useless since v2 (i.e., score_Somatic.Variants.py). Added a step to check the correctness of the input.
The v2.1 and 2.1.1 had some typos in the wrapper script, so only describing v2.1.2 here.
6.7
Version 2.2
• Added MuTect2 support.
6.8
Version 2.2.1
• InDel_3bp now stands for indel counts within 3 bps of the variant site, instead of exactly 3 bps from
the variant site as it was previously (likewise for InDel_2bp).
• Collapse MQ0 (mapping quality of 0) reads supporting reference/variant reads into a single metric of
MQ0 reads (i.e., tBAM_MQ0 and nBAM_MQ0). From experience, the number of MQ0 reads is at
least equally predictive of false positive calls, rather than distinguishing if those MQ0 reads support
reference or variant.
• Obtain SOR (Somatic Odds Ratio) from BAM files instead of VarDict’s VCF file.
• Fixed a typo in the SomaticSeq.Wrapper.sh script that did not handle inclusion region correctly.
12
6.9
Version 2.2.2
• Got around an occasional unexplained issue in then ada package were the SOR is sometimes categorized
as type, by forcing it to be numeric.
• Defaults PASS score from 0.7 to 0.5, and make them tunable in the SomaticSeq.Wrapper.sh script
(--pass-threshold and --lowqual-threshold).
6.10
Version 2.2.3
• Incorporated Strelka2 since it’s now GPLv3.
• Added another R script (ada_model_builder_ntChange.R) that uses nucleotide substitution pattern
as a feature. Limited experiences have shown us that it improves the accuracy, but it’s not heavily
tested yet.
• If a COSMIC site is labeled SNP in the COSMIC VCF file, if_cosmic and CNT will be labeled as 0.
The COSMIC ID will still appear in the ID column. This will not change any results because both of
those features are turned off in the training R script.
• Fixed a bug: if JointSNVMix2 is not included, the values should be “NaN” instead of 0’s. This is to
keep consistency with how we handle all other callers.
6.11
Version 2.2.4
• Resolved a bug in v2.2.3 where the VCF files of Strelka INDEL and Scalpel clash on GATK CombineVariants, by outputting a temporary VCF file for Strelka INDEL without the sample columns.
• Caller classification: consider if_Scalpel = 1 only if there is a SOMATIC flag in its INFO.
6.12
Version 2.2.5
• Added a dockerfile. Docker repo at https://hub.docker.com/r/lethalfang/somaticseq/.
• Ability to use vcfsort.pl instead of GATK CombineVariants to merge VCF files.
6.13
Version 2.3.0
• Moved some scripts to the utilities directory to clean up the clutter.
• Added the split_Bed_into_equal_regions.py to utilities, which will split a input BED file into multiple
BED files of equal size. This is to be used to parallelize large WGS jobs.
• Made compatible with MuTect2 from GATK4.
• Removed long options for the SomaticSeq.Wrapper.sh script because it’s more readable this way.
• Added a script to add “GT” field to Strelka’s VCF output before merging it with other VCF files.
That was what caused GATK CombineVariants errors mentioned in v2.2.4’s release notes.
• Added a bunch of scripts at utilities/dockered_pipelines that can be used to submit (requiring Sun
Grid Engine or equivalent) dockerized pipeline to a computing cluster.
6.14
Version 2.3.1
• Improve the automated run script generator at utilities/dockered_pipelines.
• No change to SomaticSeq algorithm
13
6.15
Version 2.3.2
• Added run script generators
ered_pipelines/bamSurgeon
for
dockerized
BAMSurgeon
pipelines
at
utilities/dock-
• Added an error message to r_scripts/ada_model_builder_ntChange.R when TrueVariants_or_False
don’t have both 0’s and 1’s. Other than this warning message change, no other change to SomaticSeq
algorithm.
6.16
Version 2.4.0
• Restructured the utilities scripts.
• Added the utilities/filter_SomaticSeq_VCF.py script that “demotes” PASS calls to LowQual based
on a set of tunable hard filters.
• BamSurgeon scripts invokes modified BamSurgeon script that splits a BAM file without the need to
sort by read name. This works if the BAM files have proper read names, i.e., 2 and only 2 identical
read names for each paired-end reads.
• No change to SomaticSeq algorithm
6.17
Version 2.4.1
• Updated some docker job scripts.
• Added a script that converts some items in the VCF’s INFO field into the sample field, to precipitate the need to merge multiple VCF files into a single multi-sample VCF, i.e., utilities/reformat_VCF2SEQC2.py.
• No change to SomaticSeq algorithm
6.18
Version 2.5.0
• In modify_VJSD.py, get rid of VarDict’s END tag (in single sample mode) because it causes problem
with GATK CombineVariants.
• Added limited single-sample support, i.e., ssSomaticSeq.Wrapper.sh is the wrapper script. singleSample_callers_singleThread.sh is the wrapper script to submit single-sample mutation caller scripts.
• Added run scripts for read alignments and post-alignment processing, i.e,. FASTQ → BAM, at utilities/dockered_pipelines/alignments.
• Fixed a bug where the last two CD4 numbers were both alternate concordant reads in the output VCF
file, when the last number should’ve been alternate discordant reads.
• Changed the output file names from Trained.s(SNV|INDEL).vcf and Untrained.s(SNV|INDEL).vcf to
SSeq.Classified.s(SNV|INDE).vcf and Consensus.s(SNV|INDEL).vcf. No change to the actual tumornormal SomaticSeq algorithm.
• Added utilities/modify_VarDict.py to VarDict’s “complex” variant calls (e.g., GCA>TAC) into SNVs
when possible.
• Modified r_scripts/ada_model_builder_ntChange.R to allow you to ignore certain features, e.g.,
r_scripts/ada_model_builder_ntChange.R Training_Data.tsv nBAM_REF_BQ tBAM_REF_BQ
SiteHomopolymer_Length ...
Everything after the input file are features to be ignored during training.
Also added r_scripts/ada_cross_validation.R.
14
6.19
Version 2.5.1
• Additional passable parameters options to pass extra parameters to somatic mutation callers. Fixed a
bug where the “two-pass” parameter is not passed onto Scalpel in multiThreads scripts.
• Ignore Strelka_QSS and Strelka_TQSS for indel training in the SomaticSeq.Wrapper.sh script.
6.20
Version 2.5.2
• Ported some pipeline scripts to singularities at utilities/singularities.
6.21
Version 2.6.0
• VarScan2_Score is no longer extracted from VarScan’s output. Rather, it’s now calculated directly
using Fisher’s Exact Test, which reproduces VarScan’s output, but will have a real value when VarScan2
does not output a particular variant.
• Incorporate TNscope’s output VCF into SomaticSeq, but did not incorporate TNscope caller into the
dockerized workflow because we don’t have distribution license.
6.22
Version 2.6.1
• Optimized memory for singularity scripts.
• Updated utilities/bamQC.py and added utilities/trimSoftClippedReads.py (removed soft-clipped bases
on soft-clipped reads)
• Added some docker scripts at utilities/dockered_pipelines/QC
6.23
Version 2.7.0
• Added another feature: consistent/inconsistent calls for paired reads if the position is covered by both
forward and reverse reads. However, they’re excluded as training features in SomaticSeq.Wrapper.sh
script for the time being.
• Change non-GCTA characters to N in VarDict.vcf file to make it conform to VCF file specifications.
6.24
Version 2.7.1
• Without –gatk $PATH/TO/GenomeAnalysisTK.jar in the SomaticSeq.Wrapper.sh script, it will use
utilities/getUniqueVcfPositions.py and utilities/vcfsorter.pl to (in lieu of GATK3 CombineVariants) to
combine all the VCF files.
• Fixed bugs in the docker/singularities scripts where extra arguments for the callers are not correctly
passed onto the callers.
6.25
Version 2.7.2
• Make compatible with .cram format
• Fixed a bug where Strelka-only calls are not considered by SomaticSeq.
6.26
Version 2.8.0
• The program will now throw an exception and crash if the VCF file(s) are not sorted according to the
.fasta reference file.
15
7
Contact Us
For
suggestions,
bug
reports,
or
technical
support,
please
post
in
https://github.com/bioinform/somaticseq/issues. The developers are alerted when issues are created
there. Alternatively, you may also email li_tai.fang@roche.com.
16
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