DNA-Seq Analysis Pipeline


The GDC DNA-Seq analysis pipeline identifies somatic variants within whole exome sequencing (WXS) and whole genome sequencing (WGS) data. Somatic variants are identified by comparing allele frequencies in normal and tumor sample alignments, annotating each mutation, and aggregating mutations from multiple cases into one project file.

The first pipeline starts with a reference alignment step followed by co-cleaning to increase the alignment quality. Four different variant calling pipelines are then implemented separately to identify somatic mutations. Somatic-caller-identified variants are then annotated. An aggregation pipeline incorporates variants from all cases in one project into a MAF file for each pipeline.

DNA-Seq analysis is implemented across six main procedures:

  • Genome Alignment
  • Alignment Co-Cleaning
  • Somatic Variant Calling
  • Variant Annotation
  • Mutation Aggregation
  • Aggregated Mutation Masking

Data Processing Steps


Prior to alignment, BAM files that were submitted to the GDC are split by read groups and converted to FASTQ format. Reads that failed the Illumina chastity test are removed. Note that this filtering step is distinct from trimming reads using base quality scores.

Alignment Workflow

DNA-Seq analysis begins with the Alignment Workflow. Read groups are aligned to the reference genome using one of two BWA algorithms [1]. BWA-MEM is used if mean read length is greater than or equal to 70 bp. Otherwise BWA-aln is used. Each read group is aligned to the reference genome separately and all read group alignments that belong to a single aliquot are merged using Picard Tools SortSam and MergeSamFiles. Duplicate reads, which may persist as PCR artifacts, are then flagged to prevent downstream variant call errors.

Reference Genome

All alignments are performed using the human reference genome GRCh38.d1.vd1. Decoy viral sequences are included in the reference genome to prevent reads from aligning erroneously and attract reads from viruses known to be present in human samples. Ten types of human viral genomes are included: human cytomegalovirus (CMV), Epstein-Barr virus (EBV), hepatitis B (HBV), hepatitis C (HCV), human immunodeficiency virus (HIV), human herpes virus 8 (HHV-8), human T-lymphotropic virus 1 (HTLV-1), Merkel cell polyomavirus (MCV), Simian vacuolating virus 40 (SV40), and human papillomavirus (HPV). Reference sequences used by the GDC can be downloaded here.

I/O Entity Format
Input Submitted Unaligned Reads or Submitted Aligned Reads FASTQ or BAM
Output Aligned Reads BAM

DNA-Seq Alignment Pipeline

DNA-Seq Alignment Command Line Parameters

Note that version numbers may vary in files downloaded from the GDC Portal due to ongoing pipeline development and improvement.

Step 1: Converting BAMs to FASTQs with Biobambam - biobambam2

bamtofastq \
collate=1 \
filename= <input.bam> \
gz=1 \
inputformat=bam \
level=5 \
outputdir= <output_path> \
outputperreadgroup=1 \
outputperreadgroupsuffixF=_1.fq.gz \
outputperreadgroupsuffixF2=_2.fq.gz \
outputperreadgroupsuffixO=_o1.fq.gz \
outputperreadgroupsuffixO2=_o2.fq.gz \
outputperreadgroupsuffixS=_s.fq.gz \
tryoq=1 \

Step 2: BWA Alignment - bwa - samtools

If mean read length is greater than or equal to 70bp:

bwa mem \
-t 8 \
-T 0 \
-R <read_group> \
<reference> \
<fastq_1.fq.gz> \
<fastq_2.fq.gz> |
samtools view \
-o <output.bam> -
If mean read length is less than 70bp:

bwa aln -t 8 <reference> <fastq_1.fq.gz> > <sai_1.sai> &&
bwa aln -t 8 <reference> <fastq_2.fq.gz> > <sai_2.sai> &&
bwa sampe -r <read_group> <reference> <sai_1.sai> <sai_2.sai> <fastq_1.fq.gz> <fastq_2.fq.gz> | samtools view -Shb -o <output.bam> -
If the quality scores are encoded as Illumina 1.3 or 1.5, use BWA aln with the "-l" flag.

Step 3: BAM Sort - picard 2

java -jar picard.jar SortSam \
INPUT=<input.bam> \
OUTPUT=<output.bam> \
SORT_ORDER=coordinate \

Step 4: BAM Merge - picard 2

java -jar picard.jar MergeSamFiles \
CREATE_INDEX=true \                 
[INPUT= <input.bam>]  \
OUTPUT= <output_path> \
SORT_ORDER=coordinate \

Step 5: Mark Duplicates - picard 2

java -jar picard.jar MarkDuplicates \
INPUT=<input.bam> \

Co-cleaning Workflow

The alignment quality is further improved by the Co-cleaning workflow. Co-cleaning is performed as a separate pipeline as it uses multiple BAM files (i.e. the tumor BAM and normal tissue BAM) associated with the same patient. Both steps of this process are implemented using GATK.

Indel Local Realignment

Local realignment of insertions and deletions is performed using IndelRealigner. This step locates regions that contain misalignments across BAM files, which can often be caused by insertion-deletion (indel) mutations with respect to the reference genome. Misalignment of indel mutations, which can often be erroneously scored as substitutions, reduces the accuracy of downstream variant calling steps.

Base Quality Score Recalibration

A base quality score recalibration (BQSR) step is then performed using BaseRecalibrator. This step adjusts base quality scores based on detectable and systematic errors. This step also increases the accuracy of downstream variant calling algorithms. Note that the original quality scores are kept in the OQ field of co-cleaned BAM files. These scores should be used if conversion of BAM files to FASTQ format is desired.

I/O Entity Format
Input Aligned Reads BAM
Output Harmonized Aligned Reads BAM

DNA-Seq Co-Cleaning Command Line Parameters

Step 1: RealignTargetCreator

java -jar GenomeAnalysisTK.jar \
-T RealignerTargetCreator \
-R <reference>
-known <known_indels.vcf>
[ -I <input.bam> ]
-o <realign_target.intervals>

Step 2: IndelRealigner

java -jar GenomeAnalysisTK.jar \
-T IndelRealigner \
-R <reference> \
-known <known_indels.vcf> \
-targetIntervals <realign_target.intervals> \
--noOriginalAlignmentTags \
[ -I <input.bam> ] \
-nWayOut <output.map>

Step 3: BaseRecalibrator; dbSNP v.144

java -jar GenomeAnalysisTK.jar \
-T BaseRecalibrator \
-R <reference> \
-I <input.bam> \
-knownSites <dbsnp.vcf>
-o <bqsr.grp>

Step 4: PrintReads

java -jar GenomeAnalysisTK.jar \
-T PrintReads \
-R <reference> \
-I <input.bam> \
--BQSR <bqsr.grp> \
-o <output.bam>

Somatic Variant Calling Workflow

Aligned and co-cleaned BAM files are processed through the Somatic Mutation Calling Workflow as tumor-normal pairs. Variant calling is performed using four separate pipelines:

Note that SomaticSniper [5] was used and available on the GDC Data Portal prior to GDC Data Release 35.

Variant calls are reported by each pipeline in a VCF formatted file. See the GDC VCF Format documentation for details on each available field. At this point in the DNA-Seq pipeline, all downstream analyses are branched into four separate paths that correspond to their respective variant calling pipeline.

Pipeline Descriptions

Four separate variant calling pipelines are implemented for GDC data harmonization. There is currently no scientific consensus on the best variant calling pipeline so the investigator is responsible for choosing the pipeline(s) most appropriate for the data. Some details about the pipelines are indicated below.

The MuTect2 pipeline employs a "Panel of Normals" to identify additional germline mutations. This panel is generated using TCGA blood normal genomes from thousands of individuals that were curated and confidently assessed to be cancer-free. This method allows for a higher level of confidence to be assigned to somatic variants that were called by the MuTect2 pipeline.

Basic outlines for the other two pipelines can be found here:


Indel mutations that were generated with the MuTect2, Pindel, and VarScan pipelines are detected and reported in GDC VCF files.

Germline Variants

At this time, germline variants are deliberately excluded as harmonized data. The GDC does not recommend using germline variants that were previously detected and stored in the Legacy Archive as they do not meet the GDC criteria for high-quality data.

I/O Entity Format
Input Aligned Reads BAM
Output Raw Simple Somatic Mutation VCF

Variant Call Command-Line Parameters


MuSEv1.0; dbSNP v.144

Step 1: MuSE call

MuSE call \
-f <reference> \
-r <region> \                                   
<tumor.bam> \
<normal.bam> \
-O <intermediate_muse_call.txt>
Step 2: MuSE sump

MuSE sump \
-I <intermediate_muse_call.txt> \                           
-E \                                
-D <dbsnp_known_snp_sites.vcf> \
-O <muse_variants.vcf>  
Note: -E is used for WXS data and -G can be used for WGS data.


GATK; dbSNP v.144

java -jar GenomeAnalysisTK.jar \
-T MuTect2 \
-R <reference> \
-L <region> \
-I:tumor <tumor.bam> \
-I:normal <normal.bam> \
--normal_panel <pon.vcf> \                        
--cosmic <cosmic.vcf> \
--dbsnp <dbsnp.vcf> \
--contamination_fraction_to_filter 0.02 \                   
-o <mutect_variants.vcf> \
--output_mode EMIT_VARIANTS_ONLY \


Step 1: Mpileup; Samtools

samtools mpileup \
-f <reference> \
-q 1 \
-B \
<normal.bam> \
<tumor.bam> >
Step 2: Varscan Somatic; Varscan.v2

java -jar VarScan.jar somatic \
<intermediate_mpileup.pileup> \
<output_path> \
--mpileup      1 \
--min-coverage 8 \
--min-coverage-normal 8 \
--min-coverage-tumor 6 \
--min-var-freq 0.10 \
--min-freq-for-hom 0.75 \
--normal-purity 1.0 \
--tumor-purity 1.00 \
--p-value 0.99 \
--somatic-p-value 0.05 \
--strand-filter 0 \
Step 3: Varscan ProcessSomatic; Varscan.v2

java -jar VarScan.jar processSomatic \
<intermediate_varscan_somatic.vcf> \
--min-tumor-freq 0.10 \
--max-normal-freq 0.05 \
--p-value 0.07


Step 1: Filter Reads

Filter BAM reads that are not unmapped or duplicate or secondary_alignment or failed_quality_control or supplementary for both tumor and normal BAM files

Tool: sambamba

Sambamba view $(input.bam) --filter "not (unmapped or duplicate or secondary_alignment or failed_quality_control or supplementary)" --format bam --nthreads 1 --output-filename $(output.bam)
Step 2: Pindel

Pindel Repo

Step 2a.: Calculate mean insert size

cmd = "samtools view -f66 %s | head -n 1000000" % (bam)
output = do_shell_command(cmd)
lines = output.decode('utf-8').split('\n')
b_sum = 0
b_count = 0
numlines = 0
for line in lines:
    numlines += 1
    tmp = line.split("\t")
    if len(tmp) < 9:
    if abs(int(tmp[8])) < 10000:
        b_sum += abs(int(tmp[8]))
        b_count += 1
    mean = b_sum / b_count
Step 2b.: Write it to a config file

for inputBamFile, meanInsertSize, tag in zip(inputBamFiles, meanInsertSizes, tags):
        fil.write("%s\t%s\t%s\n" %(inputBamFile, meanInsertSize, tag))
Step 2c.: Run pindel

pindel \
-f GRCh38.d1.vd1.fa \
-i config_file \
-o $(output_prefix) \
--exclude GRCh38.d1.vd1.centromeres.telomeres.bed

Step 2d.: Merge DI and SI OUTPUT

with open(os.path.join(args.workdir, "pindel_somatic"), "w") as handle:
        for p in pindel_files:
            if p.endswith("_D"):
              with open(p) as ihandle:
                for line in ihandle:
                    if re.search("ChrID", line):
        for p in pindel_files:
            if p.endswith("_SI"):
              with open(p) as ihandle:
                for line in ihandle:
                  if re.search("ChrID", line):
Step 2e.: Create a config for pindel somatic filter

indel.filter.input = $(merged.pindel.output)
indel.filter.vaf = 0.08
indel.filter.cov = 20
indel.filter.hom = 6
indel.filter.pindel2vcf = "/path/to/pindel/pindel2vcf4tcga"
indel.filter.reference =  "GRCh38.d1.vd1.fa"
indel.filter.referencename = "GRCh38"
indel.filter.referencedate = datetime.datetime.now().strftime("%Y%m%d")
indel.filter.output = $(output.file.name.vcf)

Step 2f.: Apply somatic filter on pindel output Tool: pindel2vcf4tcga

perl pindel/somatic_filter/somatic_indelfilter.pl $(somatic.indel.filter.config)
Step 3: Pindel Tool: Picard.jar 2

java \
-d64 \
-XX: +UseSerialGC \
-Xmx16G \
-jar picard.jar \
SortVcf \
I=$(pindel.somatic.vcf) \
Step 5: Vt Normalization Tool: GenomeAnalysisTK.jar nightly-2016-02-25-gf39d340

java \
-Xmx4G \
-jar \
/bin/GenomeAnalysisTK.jar \
-T VariantFiltration \
--disable_auto_index_creation_and_locking_when_reading_rods \
--variant $(vt.normal.output.vcf.gz) \
-R GRCh38.d1.vd1.fa \
--filterExpression vc.isBiallelic() && vc.getGenotype(\"TUMOR\").getAD().1 < 3" \
--filterName TALTDP \
-o $(output.vcf.gz)

Variant Call Annotation Workflow

Raw VCF files are then annotated in the Somatic Annotation Workflow with the Variant Effect Predictor (VEP) v84 [6] along with VEP GDC plugins.

The VEP uses the coordinates and alleles in the VCF file to infer biological context for each variant including the location of each mutation, its biological consequence (frameshift/ silent mutation), and the affected genes. See the documentation on the GDC VCF Format for more details. Variants in the VCF files are also matched to known variants from external mutation databases. The following databases are used for VCF annotation:

  • GENCODE v.22
  • sift v.5.2.2
  • ESP v.20141103
  • polyphen v.2.2.2
  • dbSNP v.146
  • Ensembl genebuild v.2014-07
  • Ensembl regbuild v.13.0
  • HGMD public v.20154
  • ClinVar v.201601

Due to licensing constraints COSMIC is not utilized for annotation in the GDC VEP workflow.

In addition to annotation, False Positive Filter is used to label low quality variants in VarScan.

I/O Entity Format
Input Simple Somatic Mutation VCF
Output Annotated Somatic Mutation VCF

Tumor-Only Variant Calling Workflow

Tumor only variant calling is performed on a tumor sample with no paired normal at the request of the research group. This method takes advantage of the normal cell contamination that is present in most tumor samples. These calls are made using the version of MuTect2 included in GATK4. Tumor-only variant call files can be found in the GDC Portal by filtering for "Workflow Type: GATK4 MuTect2".

Tumor-Only Variant Call Command-Line Parameters

GATK4 v4

## 1. Generate OXOG metrics:

java -d64 -XX:+UseSerialGC -Xmx3G -jar /gatk/gatk.jar \
CollectSequencingArtifactMetrics \
-I Tumor_Sample_Alignment.bam \
-O <job_identifier> \
-R GRCh38.d1.vd1.fa  ## Only chr1-22 + XYM

## 2. Generate pileup summaries on tumor sample:

java -d64 -XX:+UseSerialGC -Xmx3G -jar /gatk/gatk.jar \
-I Tumor_Sample_Alignment.bam \
-O <job_identifier>.targeted_sequencing.table \
-V af-only-gnomad-common-biallelic.grch38.main.vcf.gz \ # Germline reference from gnomad
-L intervals.bed \ ## Only chr1-22 + XYM
-R GRCh38.d1.vd1.fa

## 3. Calculate contamination on tumor sample

java -d64 -XX:+UseSerialGC -Xmx3G -jar /gatk/gatk.jar \
CalculateContamination \
-I <job_identifier>.targeted_sequencing.table \ # From step 2
-O <job_identifier>.targeted_sequencing.contamination.table

## 4. Find tumor sample name from BAM

java -d64 -XX:+UseSerialGC -Xmx3G -jar /gatk/gatk.jar \
GetSampleName \
-I Tumor_Sample_Alignment.bam \
-O <job_identifier>.targeted_sequencing.sample_name

## 5. Run MuTect2 using only tumor sample on chromosome level (25 commands with different intervals)

java -Djava.io.tmpdir=/tmp/job_tmp_3 -d64 -jar -Xmx3G -XX:+UseSerialGC \
/bin/gatk- \
Mutect2 \
-R GRCh38.d1.vd1.fa \
-L chr4:1-190214555 \ # Specify chromosome
-I Tumor_Sample_Alignment.bam \
-O 3.mt2.vcf \
-tumor <tumor_sample_name> \ # From step 4
--af-of-alleles-not-in-resource 2.5e-06 \
--germline-resource af-only-gnomad.hg38.vcf.gz \ # Germline reference from gnomad
-pon gatk4_mutect2_4136_pon.vcf.gz # New panel of normal created by 4136 TCGA curated normal samples, using GATK4

## After this step, all chromosome level VCFs are merged into one.

## 6. Sort VCF with Picard

java -d64 -XX:+UseSerialGC -Xmx16G -jar /usr/local/bin/picard.jar \
SortVcf \
OUTPUT=<job_identifier>.targeted_sequencing.mutect2.tumor_only.sorted.vcf.gz \
I=merged_multi_gatk4_mutect2_tumor_only_calling.vcf \ # From step 5

## 7. Filter variant calls from MuTect
java -d64 -XX:+UseSerialGC -Xmx3G -jar /gatk/gatk.jar \
FilterMutectCalls \
-O <job_identifier>.targeted_sequencing.mutect2.tumor_only.contFiltered.vcf.gz \
-V <job_identifier>.targeted_sequencing.mutect2.tumor_only.sorted.vcf.gz \ # From step 6
--contamination-table <job_identifier>.targeted_sequencing.contamination.table \ # From step 3
-L intervals.bed

## 8. Filter variants by orientation bias
java -d64 -XX:+UseSerialGC -Xmx3G -jar /gatk/gatk.jar \
FilterByOrientationBias \
-O <job_identifier>.targeted_sequencing.tumor_only.gatk4_mutect2.raw_somatic_mutation.vcf.gz \ # final output
-P <job_identifier>.pre_adapter_detail_metrics.txt \ # From step 1
-V <job_identifier>.targeted_sequencing.mutect2.tumor_only.contFiltered.vcf.gz \ # From step 7
-L intervals.bed \
-R GRCh38.d1.vd1.fa \
-AM G/T \

Tumor-Only Variant Annotation Workflow

After single-tumor variant calling is performed with MuTect2, a series of filters are applied to minimize the release of germline variants in downloadable VCFs. In all cases, the GDC applies a set of custom filters based on allele frequency, mapping quality, somatic/germline probability, and copy number. In some cases an additional variant classification step is applied before the GDC filters.

The PureCN R-package [7] [8] is used to classify the variants by somatic/germline status and clonality based on tumor purity, ploidy, contamination, copy number, and loss of heterozygosity. The following steps are performed with this package:

  • Interval Capture : Generates an interval file using a FASTA and BED file coordinates.
  • GC-Normalization : Calculates GC-normalized tumor/normal coverage data.
  • Normal DB Creation : Generates a normal database using the normalized coverage file and panel-of-normals VCF
  • Somatic Variant Calling : Classifies each of the previously called variants

Note that PureCN will not be performed if there is insufficient data to produce a target capture kit specific normal database. In rare occasions, PureCN may not find a numeric solution. If PureCN is not performed or does not find a solution, this is indicated in the VCF header. VCF files that were annotated with these pipelines can be found in the GDC Portal by filtering for "Workflow Type: GATK4 MuTect2 Annotation".

Somatic Aggregation Workflow

The Somatic Aggregation Workflow generates one MAF file from multiple VCF files; see the GDC MAF Format guide for details on file structure. In this step, one MAF file is generated per variant calling pipeline for each project and contains all available cases within this project.

I/O Entity Format
Input Multiple Annotated Somatic Mutation VCF
Output Aggregated Somatic Mutation MAF

Masked Somatic Aggregation Workflow

The MAF files generated by Somatic Aggregation Workflow are controlled-access due to the presence of germline mutations. Open-access MAF files are modified for public release by removing columns and variants that could potentially contain germline mutation information. See the GDC MAF Format for details about the criteria used to remove variants.

While these criteria cause the pipeline to over-filter some of the true positive somatic variants in open-access MAF files, they prevent personally identifiable germline mutation information from becoming publicly available. The GDC recommends that investigators explore both controlled and open-access MAF files if omission of certain somatic mutations is a concern.

I/O Entity Format
Input Aggregated Somatic Mutation Protected MAF
Output Masked Somatic Mutation Somatic MAF

Whole Genome Sequencing Variant Calling

Variant calls are generated from WGS data using a different pipeline than WXS and Targeted Sequencing samples. This pipeline, based on a workflow generated by the Sanger Institute, generates multiple downstream data types using the following software packages:

  • CaVEMan: Single nucleotide variants, which are available in VCF format.
  • Pindel: Small indel variants, which are available in VCF format.
  • BRASS: Structural variants, which are available in BEDPE format.
  • AscatNGS: Copy number variants, which are available as copy number estimates or copy number segment files, data may be available in tab separated values (.TSV) or plain text file (.TXT)

BEDPE File Format

BEDPE file format, (browser extensible data paired-end) is designed to concisely describe disjoint genome features, such as structural variations or paired-end sequence alignments. It's an enhanced version of the BED format, as BED does not allow inter-chromosomal feature definitions. In addition, BED only has one strand field, which is insufficient for paired-end sequence alignments, especially when studying structural variation. The BEDPE format is described below.

  • chrx (required): The name of the chromosome on which the xth end of the feature exists. (x is 1 or 2). Any string can be used. For example, "chr1", "III", "myChrom", "contig1112.23" (use "." for unknown).
  • startx (required): The zero-based starting position of the first end of the feature on chrx. The first base in a chromosome is numbered 0. The start position in each BEDPE feature is therefore interpreted to be 1 greater than the start position listed in the feature (use -1 for unknown).
  • endx (required): The one-based ending position of the first end of the feature on chrx. The end position in each BEDPE feature is one-based (use -1 for unknown).
  • name (optional): Defines the name of the BEDPE feature. Any string can be used.
  • score (optional): A score between 0 and 1000. If the track line useScore attribute is set to 1 for this annotation data set, the score value will determine the level of gray in which this feature is displayed (higher numbers = darker gray). Any string can be used.
  • strandx (optional): Defines the strand for the xth end of the feature. Either "." (unknown), "+", or "-".

In addition to the above fields, bedtools allows for the addition of user-defined fields to the normal, 10-column BEDPE format as necessary. These columns are merely "passed through" pairToBed and pairToPair and are not part of any analysis. One would use these additional columns to add extra information (e.g., edit distance for each end of an alignment, or "deletion", "inversion", etc.) to each BEDPE feature.

CNV from WGS File Format

AscatNGS, originally developed by Raine et al (2016) (GitHub page), indicates the DNA copy number changes affecting a tumor genome when comparing to a matched normal sample. See below for a description of the copy number segment and copy number estimation files produced by AscatNGS:

  • GDC Aliquot: The GDC ID for the aliquot collected from the sample (copy number segment files only).
  • Gene ID: The gene ENSMBL ID (copy number variant only).
  • Gene Name: The gene symbol (copy number variant only).
  • Chromosome: The name of the chromosome on which the copy number change exists.
  • Start: The starting position of the copy.
  • End: The ending position of the copy.
  • Copy Number: The weighted median of the strand copy numbers [9].
  • Major Copy Number: The greater strand copy number of the two strands of the DNA (copy number segment files only).
  • Minor Copy number: The smaller strand copy number of the two strands of the DNA (copy number segment files only).
  • Max. Copy number: The highest copy number for overlapped segment (copy number variant only).
  • Min. Copy number: The lowest copy number for overlapped segment (copy number variant only).

Harmonization for GENIE variants

Variants reported from the AACR Project GENIE are available from the GDC Data Portal in MAF format. These variants were produced using an abridged pipeline in which the Genomic Data Commons received the variants directly instead of calling them from aligned reads. For an outline of the harmonization process, see the steps below:

  1. Variants are submitted directly to the GDC as a "Genomic Profile."
  2. GENIE variants are lifted over to GRCh38 coordinates.
  3. Variants are annotated using VEP and made available via the GDC Data Portal.

Microsatellite Instability Detection

The GDC adopts MSIsensor2 to derive Microsatellite Instability (MSI) information from tumor DNA-Seq data. The MSIsensor2 software uses only the tumor BAM as input, and calculates the numeric MSI score (number of msi sites / all valid sites). The MSI status of MSI (Microsatellite Instable) or MSS (Microsatellite Stable) is then determined using a MSI score cutoff value of 20%.

The output msi_score and msi_status values are stored directly as properties of the aligned_reads (BAM files), and can be accessible via API. In addition, the portal/API can be filtered using these proprties by choosing "Add a File Filter" in the Repository Page and selecting msi_score or msi_status.

Please note:

  1. MSI status generated from DNA-Seq by the GDC is considered bioinformatics-derived information, and is not considered clinical data. If performed by the clinical lab, the clinical MSI test result would be stored as a laboratory_test in the molecular_test entity.
  2. MSIsensor2 can theoretically be applied to WGS, WXS, or Targeted Sequencing data. Given the number of MSI sites available in some Targeted Sequencing data, please consider the results carefully.
  3. It is possible that multiple MSI statuses exist within the same sample/case if more than one DNA-Seq BAM was generated. It is the users' responsibility to check for their consistency, especially when the MSI scores are close to 20%.

File Access and Availability

Files from the GDC DNA-Seq analysis pipeline are available in the GDC Data Portal in BAM, VCF, and MAF formats. Descriptions are listed below for all available data types and their respective file formats.

Data Type Description File Format
Aligned Reads Reads that have been aligned to the GRCh38 reference and co-cleaned. Unaligned reads and reads that map to decoy sequences are also included in the BAM files. BAM
Raw Simple Somatic Mutation A tab-delimited file with genotypic information related to genomic positions. Genomic variants are first identified here. VCF
Annotated Somatic Mutation An annotated version of a raw simple somatic mutation file. Annotated files include biological context about each observed mutation. VCF
Aggregated Somatic Mutation A tab-delimited file derived from multiple VCF files. Contains information from all available cases in a project. MAF
Masked Somatic Mutation A modified version of the Aggregated Somatic Mutation MAF file with sensitive or potentially erroneous data removed. MAF

[1]. Li, Heng, and Richard Durbin. "Fast and accurate short read alignment with Burrows-Wheeler transform." Bioinformatics 25, no. 14 (2009): 1754-1760.

[2]. Fan, Yu, Liu Xi, Daniel ST Hughes, Jianjun Zhang, Jianhua Zhang, P. Andrew Futreal, David A. Wheeler, and Wenyi Wang. "Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data." bioRxiv (2016): 055467.

[3]. Cibulskis, Kristian, Michael S. Lawrence, Scott L. Carter, Andrey Sivachenko, David Jaffe, Carrie Sougnez, Stacey Gabriel, Matthew Meyerson, Eric S. Lander, and Gad Getz. "Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples." Nature biotechnology 31, no. 3 (2013): 213-219.

[4]. Koboldt, Daniel C., Qunyuan Zhang, David E. Larson, Dong Shen, Michael D. McLellan, Ling Lin, Christopher A. Miller, Elaine R. Mardis, Li Ding, and Richard K. Wilson. "VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing." Genome research 22, no. 3 (2012): 568-576.

[5]. Larson, David E., Christopher C. Harris, Ken Chen, Daniel C. Koboldt, Travis E. Abbott, David J. Dooling, Timothy J. Ley, Elaine R. Mardis, Richard K. Wilson, and Li Ding. "SomaticSniper: identification of somatic point mutations in whole genome sequencing data." Bioinformatics 28, no. 3 (2012): 311-317.

[6]. McLaren, William, Bethan Pritchard, Daniel Rios, Yuan Chen, Paul Flicek, and Fiona Cunningham. "Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor." Bioinformatics 26, no. 16 (2010): 2069-2070.

[7]. Riester, Markus, Angad P. Singh, A. Rose Brannon, Kun Yu, Catarina D. Campbell, Derek Y. Chiang, and Michael P. Morrissey. "PureCN: copy number calling and SNV classification using targeted short read sequencing." Source code for biology and medicine 11, no. 1 (2016): 13.

[8]. Oh, Sehyun, Ludwig Geistlinger, Marcel Ramos, Martin Morgan, Levi Waldron, and Markus Riester. "Reliable analysis of clinical tumor-only whole exome sequencing data" bioRxiv 552711 (2019);

[9]. Gene-level copy number data is generated by intersection of copy number segment and gene ranges. It is possible for one gene to overlap with multiple segments, and in this case, copy_number, min_copy_number and max_copy_number could take different values. In particular, the copy_number value is calculated as the median, weighted on length of overlapped bases, of segment copy numbers from all overlapped segments.