Skip to content

Methods description

This page contains a description of all methods used in the pipeline, along with references for important tools.

Note that depending on the settings used, not all of these methods may be applicable, so please adapt this text appropriately for your application.

You can also download this text as a Word document (.docx) that contains an EndNote traveling library using the button below.

Download DOCX

Data preprocessing

Low-quality and adapters sequences are trimmed from the raw sequencing reads using Trimmomatic (v. 0.39)1. Trimmed reads are then aligned to the human hg38 reference genome using BWA mapping software (v. 0.7.17)2. Duplicate reads are marked using Samblaster (v. 0.1.25)3 and sorted using samtools (v. 1.8). Finally, base quality score recalibration is performed as indicated in the GATK4 (v. 4.2.2.0) best practices 4.

Germline variant calling

HaplotypeCaller from GATK4 (v. 4.2.2.0) is used to call germline variants, parallelized across chromosomes, and all samples in the cohort are joint genotyped together 4,5.

Somatic variant calling

Somatic variant calling (SNPs and Indels) is performed using Mutect (v. 1.1.7)6, Mutect2 (GATK v. 4.2.0)7, Strelka2 (v. 2.9.0)8, and VarDict (v. 1.4)9 in tumor-normal mode. Variants from all callers are merged using the CombineVariants tool from GATK version 3.8-1. Genomic, functional and consequence annotations are added using Variant Effect Predictor (VEP v. 99)10 and converted to Mutation Annotation Format (MAF) using the vcf2maf tool (v. 1.6.16)11.

For Copy Number Variants (CNVs), Control-Freec (v. 11.6)12 is used to generate pileups, which are used as input for the R package 'sequenza' (v. 3.0.0)13. The complete Control-Freec workflow is then re-run using ploidy and cellularity estimates from 'sequenza'.

FFPE Artifact filtering

SOBDetector is a tool that scores variants based on strand-orientation bias, which can be a sign of DNA damage caused by fixation of tissue. This pipeline runs SOBDetector in a two-pass method. The first pass uses parameters provided with the software (calculated from publicly available data from TCGA), then cohort-specific bias metrics are computed from those results, and SOBDetector is re-run using these cohort-specific values.

Quality and identity metrics

Ancestry and relatedness scores are generated using Somalier (v. 0.2.13)14. Contamination analyses are performed against viral and bacterial genomes from NCBI using Kraken2 (v. 2.1.2)15, as well as against mouse, human, and UniVec databases using FastQ Screen (v. 0.14.1)16. Sequence, mapping and variant statistics are computed using FastQC (v. 0.11.9), Qualimap (v. 2.2.1)17 and SNPeff (v. 4.3t)18. All of these metrics are combined into an interactive HTML report using MultiQC (v. 1.11)19.

Pipeline Orchestration

Job execution and management is done using Snakemake (v. 6.8.2)20 using custom-built Singularity (v. 3.8.5) containers for reproducibility.

References

  1. Bolger, A.M., M. Lohse, and B. Usadel, Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 2014. 30(15): p. 2114-20. ↩

  2. Li, H. and R. Durbin, Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009. 25(14): p. 1754-60. ↩

  3. Faust, G.G. and I.M. Hall, SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics, 2014. 30(17): p. 2503-5. ↩

  4. Van der Auwera, G.A. and B.D. O'Connor, Genomics in the cloud : using Docker, GATK, and WDL in Terra. First edition. ed. 2020, Sebastopol, CA: O'Reilly Media. ↩↩

  5. Poplin, R., et al., Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv, 2018: p. 201178. ↩

  6. Cibulskis, K., et al., Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol, 2013. 31(3): p. 213-9. ↩

  7. Benjamin, D., et al., Calling Somatic SNVs and Indels with Mutect2. bioRxiv, 2019: p. 861054. ↩

  8. Kim, S., et al., Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods, 2018. 15(8): p. 591-594. ↩

  9. Lai, Z., et al., VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res, 2016. 44(11): p. e108. ↩

  10. McLaren, W., et al., The Ensembl Variant Effect Predictor. Genome Biol, 2016. 17(1): p. 122. ↩

  11. Memorial Sloan Kettering Cancer Center. vcf2maf. 2013; Available from: https://github.com/mskcc/vcf2maf. ↩

  12. Boeva, V., et al., Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics, 2012. 28(3): p. 423-5. ↩

  13. Favero, F., et al., Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann Oncol, 2015. 26(1): p. 64-70. ↩

  14. Pedersen, B. somalier: extract informative sites, evaluate relatedness, and perform quality-control on BAM/CRAM/BCF/VCF/GVCF. 2018; Available from: https://github.com/brentp/somalier. ↩

  15. Wood, D.E., J. Lu, and B. Langmead, Improved metagenomic analysis with Kraken 2. Genome Biol, 2019. 20(1): p. 257. ↩

  16. Wingett, S.W. and S. Andrews, FastQ Screen: A tool for multi-genome mapping and quality control. F1000Res, 2018. 7: p. 1338. ↩

  17. Okonechnikov, K., A. Conesa, and F. Garcia-Alcalde, Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics, 2016. 32(2): p. 292-4. ↩

  18. Cingolani, P., et al., A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin), 2012. 6(2): p. 80-92. ↩

  19. Ewels, P., et al., MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 2016. 32(19): p. 3047-8. ↩

  20. Koster, J. and S. Rahmann, Snakemake-a scalable bioinformatics workflow engine. Bioinformatics, 2018. 34(20): p. 3600. ↩