Whole exome sequencing data analysis.pptx
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Whole exome sequencing (WES) selectively sequences the transcribed regions of the genome, providing a cost-effective alternative to whole genome sequencing. It produces a smaller, more manageable dataset for analysis. Key steps in WES data analysis include quality control of raw sequencing data, alignment of reads to a reference genome, variant calling, annotation of variants, and filtering of variants. Critical quality metrics are assessed at each step to ensure high quality data and accurate detection of variants.
Whole exome sequencing data analysis.pptx
- 1. Whole exome sequencing (WES|WXS) and its data analysis Feb 28, 2023 Haibo Liu Senior Bioinformatician UMass Medical School, Worcester, MA Email: haibol2017@gmail.com
- 2. Eukaryotic Exome The human exome contains about 180,000 exons. These constitute about 1% of the human genome (~40 Mb).
- 3. Exome sequencing • A NGS method that selectively sequences the transcribed regions of the genome. • Provides a cost-effective alternative to WGS • Produces a smaller, more manageable data set for faster, easier data analysis (4–5 Gb WES vs ~90 Gb WGS) • Identify both somatic and germline variants • Single Nucleotide Polymorphisms (SNPs) • Small Insertions-Deletions (indels) • Loss of Heterozygosity (LOH) • Copy Number Variants (CNVs), structural variants (SV) • Microsatellite stability
- 4. Performance of WES in clinical studies
- 6. Genotyping by Microarray, WES, and WGS (not updated, data analysis cost not included)
- 7. Experimental design of WES • Tissue sampling • Somatic mutations • Tumor (tumor purity and freshness are critical) • Normal tissue or blood sample • Germline mutations • Blood or any other tissue • Sample size and sample population • cohort (disease vs health) • Trio, related family (non-carrier, carrier, and patient) • Capture methods • Sequencing strategies • platform, PE|SE, UMI, read length, seq. depth
- 8. Rescue to DNA preparation from FFPE fixed samples
- 9. Exome capture: Target-enrichment strategies Array-based capture https://en.wikipedia.org/wiki/Exome_sequencing • Twist Exome 2.0 (Twist Bioscience) • Nextera Rapid Capture Exomes (Illumina) • xGen WES (IDT) • SureSelect (Agilent) • KAPA HyperExome (Roche) • SeqCap (NimblGen) • … Capture toolkits
- 10. UMI for detecting low frequency mutations for prenatal or cancer research The Cell3™ Target library preparation behind our whole exome enrichment incorporates error suppression technology. This includes unique molecular indexes (UMIs) and unique dual indexes (UDIs), to remove both PCR and sequencing errors and index hopping events. This error suppression technique, combined with our excellent uniformity of coverage, allows you to confidently and accurately call mutations down to 0.1% VAF and enables generation of sequencing libraries from as little as 1 ng cfDNA input.
- 11. Comparison of different library preparation methods
- 12. Comparison of different library preparation methods
- 13. Sequencing depth
- 14. Quality control in WES Raw data QC BAM QC variant QC
- 15. Raw data QC • QC tools • FastQC/MultiQC • NGS QC toolkit (https://github.com/mjain-lab/NGSQCToolkit) • QC-chain (contamination detection) • PRINSEQ • QC3 • Important QC metrics • Base quality • Nucleotide distribution along cycles • GC content distribution • Duplication rate • Adaptor content QC3
- 16. Read trimming • Trimmomatic, cutadapt, fastp (auto adaptor detection), … • Quality/adaptor trimming • Don’t trim 5’ end (markduplicates)
- 17. From raw fastq to analysis-ready BAM
- 18. Aligner • BWA-mem • Bowtie2, Novoalign, GMAP
- 19. Selection of reference genomes • Completeness • Decoyed genome (1000 Genomes analysis pipeline) • EBV (herpesvirus 4 type 1, AC:NC_007605) and decoy sequences derived from HuRef, Human BAC and Fosmid clones and NA12878. (~36Mb) • T2T- CHM13v1.1, the latest, complete human reference genome
- 20. Quality control in WES Raw data QC BAM QC variant QC
- 21. BAM QC • Important QC metrics • % of reads that map to the reference • % of reads that map to the baits • Coverage depth distribution (target regions) • Coverage unevenness & Cohort Coverage Sparseness • Insert size distribution • Duplicate rate • Tools • Alfred • QC3 • Various picard CollectMetrics tools • covReport
- 22. Cohort Coverage Sparseness (CCS) and Unevenness (UE) Scores for a detailed assessment of the distribution of coverage of sequence reads https://www.nature.com/articles/s41598-017-01005-x
- 23. Local and global non-uniformity of different capture toolkits
- 24. Differences from Capture toolkits
- 25. Differences from Capture toolkits https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4092227/
- 26. Exome probe design is one of the major culprits • Most of the observed bias in modern WES stems from mappability limitations of short reads and exome probe design rather than sequence composition. https://www.nature.com/articles/s41598-020-59026-y
- 27. Alfred QC metrics https://academic.oup.com/bioinformatics/article/35/14/2489/5232224 Alignment Metric DNA-Seq (WGS) DNA-Seq (Capture) RNA-Seq ChIP-Seq/ATAC-Seq Chart Type Mapping Statistics ✔ ✔ ✔ ✔ Table Duplicate Statistics ✔ ✔ ✔ ✔ Table Sequencing Error Rates ✔ ✔ ✔ ✔ Table Base Content Distribution ✔ ✔ ✔ ✔ Grouped Line Chart Read Length Distribution ✔ ✔ ✔ ✔ Line Chart Base Quality Distribution ✔ ✔ ✔ ✔ Line Chart Coverage Histogram ✔ ✔ ✔ ✔ Line Chart Insert Size Distribution ✔ ✔ ✔ ✔ Grouped Line Chart InDel Size Distribution ✔ ✔ ✔ ✔ Grouped Line Chart InDel Context ✔ ✔ ✔ ✔ Bar Chart GC Content ✔ ✔ ✔ ✔ Grouped Line Chart On-Target Rate ✔ Line Chart Target Coverage Distribution ✔ Line Chart TSS Enrichment ✔ Table DNA pitch / Nucleosome pattern ✔ Grouped Line Chart https://www.gear-genomics.com/docs/alfred/webapp/#featuresty-control)
- 28. CovReport
- 29. From BAM to VCF GATK: Slop exon by 200 bp Analysis for each chromosome
- 30. Variant callers (Mutect2) (HaplotypeCaller) BreakSeq, LUMPY, Hydra,DELLY, CNVNator, Pindel FreeBayes/SAMtools, DeepVariant
- 31. GATK Best practices for population- based germline variant calling
- 32. GATK Mutect2 Best practices for population- based soMATIC variant calling
- 33. Discrepancy of variants called by different callers
- 34. Integrated variant calling • Integration of multiple tools’ results • Isma (integrative somatic mutation analysis) • Ensemble Machine learning method • BAYSIC • SomaticSeq • NeoMutate • SMuRF (Bartha and Gyorffy2019) (Nanni et al. 2019)
- 35. Quality control in WES Raw data QC BAM QC variant QC
- 36. Sample-level Variant QC • Tools • GATK CollectVariantCallingMetrics , VCFtools, PLINK/seq, QC3 • Important QC metrics • Ti/Tv ratio, nonsynonymous/synonymous, heterozygous/nonreference-homozygous (het/nonref-hom) ratio, mean depth, • Genotype missing rate • Genotype concordance to related data (different platforms) • Cross-sample DNA contamination (VerifyBamID) • Identity-by-descent (IBD) analysis (PLINK) • Related samples • PCA (EIGENSTRAT) • Population stratum (ethnicity) • Sex check (PLINK)
- 37. Ti/Tv ratio and het/nonref-hom ratio • The Ti/Tv ratio varies greatly by genome region and functionality, but not by ancestry. • The het/nonref-hom ratio varies greatly by ancestry, but not by genome regions and functionality. • extreme guanine + cytosine content (either high or low) is negatively associated with the Ti/Tv ratio magnitude. • when performing QC assessment using these two measures, care must be taken to apply the correct thresholds based on ancestry and genome region. https://academic.oup.com/bioinformatics/article/31/3/318/2366248 Too low ==> high false positive rate; too high ==> bias.
- 38. Example report
- 39. Potential error sources in next-generation sequencing workflow https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1659-6
- 40. Origin of variant artifacts • Artifacts introduced by sample/library preparation • low-quality base calls (Read-end artifacts and other low Qual bases) • Alignment artifacts • Local misalignment near indels, • Erroneous alignments in low-complexity regions • Paralogous alignments of reads not well represented in the reference • Strand orientation bias artifacts (Strand Orientation Bias Detector (SOBDetector), Fisher score)-- https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-13-666 •
- 41. Artifacts (https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-020-00791-w) Low base qual Read end Strand bias Low complexity misalignment Paralog misalgnment
- 42. Variant-level QC • Important QC metrics • Genotype missing rate • Hardy-Weinberg Equilibrium (caution) p-value • Mendelian error rate • Allele balance of heterozygous calls • Variant quality score (GATK): filtering SNP and INDELS separately(https://gatk.broadinstitute.org/hc/en-us/articles/360035890471-Hard- filtering-germline-short-variants) • Hard filter • QualByDepth (QD) • FisherStrand (FS) • StrandOddsRatio (SOR) • RMSMappingQuality (MQ) • MappingQualityRankSumTest (MQRankSum) • ReadPosRankSumTest (ReadPosRankSum) • Machine learning-based filtering: Variant Quality Score Recalibration --filterExpression "QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" --filterName "my_snp_filter"
- 43. Variant-level filtering • Tools • GATK, VCFtools, PLINK/Seq • Sequencing data-based filtering • Exclude potential artifacts • Database-based filtering: • Exclude known variants which are present in public SNP databases, published studies or in-house databases as it is assumed that common variants represent harmless variations • Pedigree-based filtering • Each generation introduces up to 4.5 deleterious mutations, it might be as well that a de novo mutation is causing the disease. • Function-based filtering • Caution: risk removing the pathogenic variant
- 45. Allelic balance • SLIVAR: genotype quality, sequencing depth, allele balance, and population allele frequency : https://github.com/brentp/slivar https://onlinelibrary.wiley.com/doi/full/10.1002/humu.23674
- 46. Variant annotation tools VAT Annotation of variants by functionality in a cloud computing environment.
- 48. Functional predictors/Prioritization tools snpSift http://pcingola.github.io/SnpEff/ss_introduction/ (Hintzsche et al., 2016) VAAST https://github.com/Yandell-Lab/VVP-pub VarSifter, VarSight gNome, KGGseq
- 49. (Cheng et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. SCIENCE, 19 Sep 2023Vol 381, Issue 6664,DOI: 10.1126/science.adg7492) Latest, advanced AI tool for infer effect of missense mutations: AlphaMissense (Hintzsche et al., 2016)
- 50. Tools and resources for linking variants to therapeutics
- 51. Variant visualization tools VIVA, vcfR oncoprint
- 52. Oncoprint for visualizing cohort variants
- 54. Summary
- 55. WES and its data analysis
- 57. WES data analysis pipelines • DRAGEN (Illumina) • https://www.illumina.com/products/by-type/informatics- products/basespace-sequence-hub/apps/dragen-enrichment.html • JWES
- 58. • A high-performance commercial solution (https://www.sentieon.com/products/) • improves upon BWA, STAR, Minimap2, GATK, HaplotypeCaller, Mutect, and Mutect2 based pipelines and is deployable on any generic-CPU-based computing system WES data analysis pipelines