/SNP_notes

Notes on SNP-related tools and genome variation analysis

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SNP-related notes

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SNP- and genome variation-related tools and genomics data analysis resources. Please, contribute and get in touch! See MDmisc notes for other programming and genomics-related notes.

Table of content

Variant calling pipelines

  • Koboldt, Daniel C. “Best Practices for Variant Calling in Clinical Sequencing.” Genome Medicine, (December 2020) - Introduction in genomic variant calling, panel/exome/whole genome sequencing technologies (Table 1), preprocessing, analysis (SNVs/indels, mutations, CNVs, SVs, gene fusions, Table 2), gold standard datasets (GIAB), best practices, filtering for each type of genomic variant.

  • MapCaller - map reads and identify genomic variants, indels, inversions, translocations. Based on KART method. Outperforms GATK, Freebayes, Mpileup. https://github.com/hsinnan75/MapCaller

    • Lin, Hsin-Nan, and Wen-Lian Hsu. “MapCaller - An Integrated and Efficient Tool for Short-Read Mapping and Variant Calling Using High-Throughput Sequenced Data.” Preprint. Bioinformatics, September 26, 2019. https://doi.org/10.1101/783605.

  • TOPMed Variant Calling Pipeline, https://github.com/statgen/topmed_variant_calling

  • DNAscan - a pipeline for DNA-seq analysis to call SNPs, indels, SVs, repeat expansions and viral genetic material. Four parts - alignment (HISAT2, BWA mem), analysis (Freebayes, GATC HC, Manta, Expansion Hunter), annotation (Annovar), report generation. https://github.com/KHP-Informatics/DNAscan

    • Iacoangeli, A., A. Al Khleifat, W. Sproviero, A. Shatunov, A. R. Jones, S. L. Morgan, A. Pittman, R. J. Dobson, S. J. Newhouse, and A. Al-Chalabi. “DNAscan: Personal Computer Compatible NGS Analysis, Annotation and Visualisation.” BMC Bioinformatics 20, no. 1 (December 2019): 213. https://doi.org/10.1186/s12859-019-2791-8.

Preprocessing tools

Depth

SNP callers

  • Varlociraptor - a unifying statistical model allowing for the detection of SNVs, MNVs, InDels, Inversions, Duplications, Breakends. Explicitly controls FDR. Defines variant calling grammar, allowing for defining all types of variants. Tumor-normal comparison allows for classifying variants into germline, somatic variants. Evaluated on simulated (artificial clones) and real (Venter's genome) data. Outperforms six other variant callers. Can call variants in single samples, in RNA-seq data, FFPE attifact detection. Tweet

  • Liu, Fenglin, Yuanyuan Zhang, Lei Zhang, Ziyi Li, Qiao Fang, Ranran Gao, and Zemin Zhang. "Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data." Genome Biology 20, no. 1 (2019): 1-15. - https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1863-4 - comparison of seven tools for SNP detection in scRNA-seq data. SAMtools, Strelka2, FreeBayes, and CTAT are best.

  • Xu, Chang. “A Review of Somatic Single Nucleotide Variant Calling Algorithms for Next-Generation Sequencing Data.” Computational and Structural Biotechnology Journal 16 (2018): 15–24. https://doi.org/10.1016/j.csbj.2018.01.003. - Overview of 46 somatic Single Nucleotide Variant (SNV) caller tools. Pre-processing, variant evaluation, and post-filtering steps. Four categories of algorithms, description of each, and the corresponding tools: matched tumor-normal (position-, haplotype-, mathine learning-based methods, Table 1), single-sample (Table 2, some offer somatic-germline classification), UMI-based (UMI technology, Figure 1, Table 3), and RNA-seq (Technology, issues, Table 4) variant calling. Benchmarking using tools for generating synthetic reads, spike-ins, GiAB, melanoma-normal samples, performance evaluation metrics. Issues in representing complex variants and tools for variant normalization. Deep neural network-based algorithms perform best.

  • MutationSeq - somatic SNV detection from tumor-normal pairs. http://compbio.bccrc.ca/software/mutationSeq/

  • Samovar - mosaic SNV calling from single samples sequenced using WGS 10X linked reads technology. Intro into mosaic SNVs, linked reads. Outperforms MuTect2, MosaicHunter. https://github.com/cdarby/samovar

    • Darby, Charlotte A, James R Fitch, Patrick J Brennan, Benjamin J Kelly, Natalie Bir, Vincent Magrini, Jeffrey Leonard, et al. “Samovar: Single-Sample Mosaic SNV Calling with Linked Reads.” Preprint. Genomics, February 25, 2019. https://doi.org/10.1101/560532.

  • RNA-MuTect method to detect somatic mutations from tumor DNA - matched normal RNA samples. Applied to TCGA, GTeX data. Most mutated are sun-exposed skin, esophagus mucosa, lung. Number of mutations in RNA is 5-fold larger than in DNA, mutations depend on coverage. Only half of DNA mutations have sufficient coverage in RNA. Filtering using 1) two aligners, 2) removal of errors based on a site-specific error model, 3) removal of RNA editing sites. https://zenodo.org/record/2620062#.XPz9e29KhQI

    • Yizhak, Keren, François Aguet, Jaegil Kim, Julian M Hess, Kirsten Kübler, Jonna Grimsby, Ruslana Frazer, et al. “RNA Sequence Analysis Reveals Macroscopic Somatic Clonal Expansion across Normal Tissues.” HUMAN GENETICS, 2019, 11.

Deep learning SNP callers

  • DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. CNN trained to detect SNPs. Inception v2 architecture. Outperforms GATK and others in FDA challenge, GIB data. Docker/Singularity CPU and GPU versions, local and cloud compatible. Tweet 1, Tweet 2, GitHub repo

  • NeuSomatic - Convolutional neural network (9 layers) for somatic mutations calling. Reads within 7bp window around candidate SNP are extracted, realigned, summarized into matrices for tumor-normal samples, used for classifying mutation type, length, position. Tested on GiB samples and on DREAM datasets. Comparison with other SNP callers. https://github.com/bioinform/neusomatic

    • Sahraeian, Sayed Mohammad Ebrahim, Ruolin Liu, Bayo Lau, Marghoob Mohiyuddin, and Hugo Y. K. Lam. “Deep Convolutional Neural Networks for Accurate Somatic Mutation Detection,” September 4, 2018. https://doi.org/10.1101/393801.

  • DeepSNV - an R package for detecting clonal and subclonal variants from deeply sequenced clonally heterogeneous cancer data. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC.

SNP annotations

Various genome annotations, Source: ConsHMM Data availability section: 25-state chromatin state annotations; CADD score v1.0; CADD score v1.4; CDTS score, another link; CNEEs; DANN score; EIGEN and Eigen-PC score; ENCODE DHS; FATHMM-XF score; FIRE score; fitCons score; FunSeq2 score; GENCODE v19; GERP++ scores and constrained element calls; GWAS catalog variants; LINSIGHT score; Motif instances and background; REMM score; Roadmap Epigenomics DHS; SiPhy-omega and SiPhy-pi constrained element calls (hg19 liftOver)

  • VCF-plotein - graphical, interactive interptetation of exome sequencing data in VCF format. Includes major databases, from gnomAD, COSMIC to Human Phenotype Ontology and GO terms. Shiny app, GitHub

  • The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package for functional annotation and translation of individual cancer genomes for precision cancer medicine. Currently, it interprets both somatic SNVs/InDels and copy number aberrations. The software extends basic gene and variant annotations from the Ensembl’s Variant Effect Predictor (VEP) with oncology-relevant, up-to-date annotations retrieved flexibly through vcfanno, and produces interactive HTML reports intended for clinical interpretation. NOTE: If you also want to interrogate the clinical impact of germline variants in the same individual, try the accompanying tool Cancer Predisposition Sequencing Reporter (CPSR).

  • FUMA - Functional Mapping and Annotation of GWAS using 43 scRNA-seq datasets (human and mouse). MAGMA Cell type-specific enrichment. Applied to 26 GWAS disorders,  https://fuma.ctglab.nl/. Processed data and instructions for self-download: https://github.com/Kyoko-wtnb/FUMA_scRNA_data

    • Watanabe, Kyoko, Maša Umićević Mirkov, Christiaan A. de Leeuw, Martijn P. van den Heuvel, and Danielle Posthuma. “Genetic Mapping of Cell Type Specificity for Complex Traits.” Nature Communications 10, no. 1 (December 2019): 3222. https://doi.org/10.1038/s41467-019-11181-1.

  • XGBoost to predict the impact of non-coding variants. Uses functional essentiality features, 3D genome organization, enhancer reporter data, existing deleteriousness metrics (CADD, ncEigen, FATHMM, FunSeq2, LINSIGHT, ORION, ReMM, ncRVIS). Methods how to train/optimize XGBoost. Provides a score, ncER (non-coding essential regulation) nucleotide-resolution and average over 10bp bins.  https://github.com/TelentiLab/ncER_datasets,  https://www.ai-omni.com/

    • Wells, Alex, David Heckerman, Ali Torkamani, Li Yin, Jonathan Sebat, Bing Ren, Amalio Telenti, and Julia di Iulio. “Ranking of Non-Coding Pathogenic Variants and Putative Essential Regions of the Human Genome.” Nature Communications 10, no. 1 (December 2019): 5241. https://doi.org/10.1038/s41467-019-13212-3.

  • ConsHMM - genome segmentation into 100 conservation states based on a 100 species DNA sequence alignment. Hidden Markov Model, extension of ChromHMM. Compared with 12 other scores (CADD, CDTS, DANN, Eigen, Eigen-PC, FATHMM-XF, FIRE, fitCons, GERP++, PhastCons, PhyloP, REMM, also LINSIGHT, FunSeq2). Clustering, GO enrichment reveals distinct functionality. hg19 single nucleotide scores https://github.com/ernstlab/ConsHMM,  https://figshare.com/articles/ConsHMM_100-state_Segmentation_of_hg19_Human_Genome/8162036/1

    • Arneson, Adriana, and Jason Ernst. “Systematic Discovery of Conservation States for Single-Nucleotide Annotation of the Human Genome.” Communications Biology 2, no. 1 (December 2019): 248. https://doi.org/10.1038/s42003-019-0488-1.

  • Predicting pathogenic vs. non-pathogenic SNPs and then regulatory status of each base using 38 functional and structural features. XGBoost model, parameter tuning. All predictions are at https://omni-variants.herokuapp.com/. Existing noncoding deleteriousness metrics: CADD, ncEigen, FATHMM, FunSeq2, LINSIGHT, ORION, ReMM, ncRVIS

    • telenti, amalio, Alexander C Wells, David Heckerman, Ali Torkamani, Bing Ren, and Julia di Iulio. “Identification of Essential Regulatory Elements in the Human Genome.” Preprint. Genomics, October 16, 2018. https://doi.org/10.1101/444562.

  • Seattle - The SeattleSeq Annotation server provides annotation of SNVs (single-nucleotide variations) and small indels, both known and novel. This annotation includes dbSNP rs IDs, gene names and accession numbers, variation functions (e.g. missense), protein positions and amino-acid changes, conservation scores, HapMap frequencies, PolyPhen predictions, and clinical association

  • snpEff - a variant annotation and effect prediction tool

  • slivar - variant expressions, annotation, and filtering, by Brent Pedersen, https://github.com/brentp/slivar

  • The Variant Interpretation for Cancer Consortium Meta-Knowledgebase. Aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. Validation using GENIE database. https://search.cancervariants.org

    • Wagner, Alex Handler, Brian Walsh, Georgia Mayfield, David Tamborero, Dmitriy Sonkin, Kilannin Krysiak, Jordi Deu Pons, et al. “A Harmonized Meta-Knowledgebase of Clinical Interpretations of Cancer Genomic Variants,” July 11, 2018. https://doi.org/10.1101/366856.

  • vcfanno - annotate a VCF with other VCFs/BEDs/tabixed files. https://github.com/brentp/vcfanno

    • Pedersen, Brent S., Ryan M. Layer, and Aaron R. Quinlan. “Vcfanno: Fast, Flexible Annotation of Genetic Variants.” Genome Biology 17, no. 1 (December 2016). https://doi.org/10.1186/s13059-016-0973-5.

  • atSNP - search for effects of SNPs on transcription factor binding. DB of 37 billion variant-motif pairs. Search by SNP IDs, window of SNPs, genomic location, gene, transcription factor. http://atsnp.biostat.wisc.edu/

    • Shin, Sunyoung, Rebecca Hudson, Christopher Harrison, Mark Craven, and Sündüz Keleş. “AtSNP Search: A Web Resource for Statistically Evaluating Influence of Human Genetic Variation on Transcription Factor Binding.” Edited by John Hancock. Bioinformatics, December 8, 2018. https://doi.org/10.1093/bioinformatics/bty1010.

SNP signatures

SNP pathogenicity scores

  • SEMpl - predict the impact of SNPs on TF binding. Uses ChIP-seq data, DNAse-seq, and PWMs. Simulate all possible SNPs in PWMs, estimate effect on ChIP signal. https://github.com/Boyle-Lab/SEM_CPP

    • Nishizaki, Sierra S, Natalie Ng, Shengcheng Dong, Robert S Porter, Cody Morterud, Colten Williams, Courtney Asman, Jessica A Switzenberg, and Alan P Boyle. “Predicting the Effects of SNPs on Transcription Factor Binding Affinity.” Edited by John Hancock. Bioinformatics, August 2, 2019, btz612. https://doi.org/10.1093/bioinformatics/btz612.

  • regBase - Prediction of regulatory impact of variants outside of protein-coding regions, human. Trained on prediction scores from 23 tools, Gradient Tree Boosting, thorough training and evaluation. hg19 predictions are available for download. Python implementation https://github.com/mulinlab/regBase

    • Zhang, Shijie, Yukun He, Huanhuan Liu, Haoyu Zhai, Dandan Huang, Xianfu Yi, Xiaobao Dong, et al. “RegBase: Whole Genome Base-Wise Aggregation and Functional Prediction for Human Non-Coding Regulatory Variants.” Nucleic Acids Research, September 12, 2019, gkz774. https://doi.org/10.1093/nar/gkz774.

  • ClinPred - pathogenicity prediction for all nonsynonymous SNPs. Trained on ClinVar, validated on nine other databases. Random forest and gradient boosted decision tree, comparison with other machine learning algorithms. Downloadable scores for all nonsynonymous SNPs, https://sites.google.com/site/clinpred/home

    • Alirezaie, Najmeh, Kristin D. Kernohan, Taila Hartley, Jacek Majewski, and Toby Dylan Hocking. “ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants.” The American Journal of Human Genetics 103, no. 4 (October 2018): 474–83. https://doi.org/10.1016/j.ajhg.2018.08.005.

SNP visualization, clustering

SNP, GWAS databases

InDels

CNV, SV

  • Awesome papers and projects about CNV and SV using NGS data - Relevant studies with Structual Variants and Copy Number Variants in NGS (Genome, Exome and Amplicon Sequencing) pipelines

  • Benchmarking of 15 WGS-based structural variant callers (focusing on deletions), selected out of 55 (Supplementary Table 1). Gold standard - homozygous deletions in inbred mouse chromosomes. Lumpy, Manta give most optimal performance under various test strategies. GitHub, Tweet

  • Benchmark of 10 CNV callers. LUMPY performs best overall, Canvas is good for high specificity, CNVnator and RDXplorer are good for high sensitivity. Table 1 summarizes functionality of each caller. Used the Database of Genomic Variants as a gold standard, call CNVs from NA12878 genome

  • Review of structural variant callers. De novo-based approaches (graph- or scaffold-based), short-read DNA-seq and RNA-seq (gene fusion) mapping, long-read (PacBio, Oxford Nanopore) mapping, multimethods approaches. SV calling from newer technologies, such as optical mapping, strand-seq, 10X Genomics linked reads, Hi-C. Brief description of tools, their performance, references to reviews. Table 1 - categorized list of tools, brief description and links.

  • Benchmarking of seven SV callers (BreakDancer, Pindel, Delly, Lumpy, Manta, GRIDSS, SvABA) to detect different SV types, sizes, the effect of SV abundance and sequencing coverage, sequence similarity, biases (GC content and homopolymers), and mapping quality. Overview of read-pair, read-depth, split-read, and local assembly. SV types and definitions. Manta, Lumpy, of GRIDSS perform well. Supplementary material - code examples

  • CNVpytor - CNV/CNA detection from read depth and SNPs (allelic imbalance). Python implementation of CNVnator, faster parsing (pysam) smaller .pytor files (H5). Works on a cloud, in Jupyter notebooks. Visualization using JBrowse

  • HATCHet (Holistic Allele-specific Tumor Copy-number Heterogeneity) is an algorithm that infers allele and clone-specific CNAs and WGDs jointly across multiple tumor samples from the same patient, and that leverages the relationships between clones in these samples.

  • SV-Bay - structural variant detection, Bayesian, corrects for GC-content and mappability. Uses both normal and abnormal reads, paired-end and depth information. Somatic variants if a normal sample is available. Detailed methods, stats. Compared with GASVPro, Lumpy, BreakDancer, DELLY on simulated (TGsim) and experimental neuroblastoma datasets. Improve sensitivity and specificity of SV detection, less false positives. Reasonably fast.

  • CNVkit - capturing CNVs in on-target and off-target genomic regions. Existing tools (CNVer, ExomeCNV, exomeCopy, CONTRA, CoNIFER, ExomeDepth, VarScan2, XHMM, ngCGH, EXCAVATOR, CANOES, PatternCNV, CODEX, Control-FREEC, cn.MOPS, cnvOffSeq, CopyWriteR). Account for GC content, mappability. Python 2.7 implementation. Examples

  • Manta - SV detection in single- and tumor-normal samples. parallelized for within-sample performance. Fast, detects more variants of different types.

  • LUMPY-SV - a general probabilistic framework for structural variant discovery. Integrates multiple signals - read-pair, split-read, read-depth and prior knowledge. Operates on multiple samples.

  • DELLY - detection of structural variants, such as CNVs, duplications, inversions, translocations. Paired-end and split-read analysis

  • Control-FREEC - assess copy number and genotype information in whole genome and exome sequencing data. Corrects for contamination by normal cells and variable sample ploidy. With a matched normal sample, distinguishes somatic from germline events.

  • CNVnator - CNV using read depth. Bin the genome, mean-shift technique to quantify CNVs. Poor agreement in methods detection. Misses retrotransposon-CNVs but detects >50% other regions missed by other methods. https://github.com/abyzovlab/CNVnator

  • CREST - mapping somatic structural variations in cancer genomes ad base-pair resolution. realignment of soft-clipped subsequences.Compared with BreakDancer, Pindel. Tested on experimental and simulated data, hg18, some SVs experimentally validated.

  • DNAcopy - an R package for DNA copy number data analysis using Circular Binary Segmentation (CBS) algorithm. Input - CHG-like data, regions with comparative copy number values. Smoothing, outlier detection, segmentation using changepoint detection, visualization

  • copynumber - Segmentation of single- and multi-track copy number data by penalized least squares regression. Similar functionality to DNAcopy

  • instasv - Integrative analysis of structural variations called by different tools.

  • TITAN - a tool for predicting subclonal copy number alterations (CNA) and loss of heterozygosity (LOH) from tumour whole genome sequencing data.

  • QDNASeq - Quantitative DNA sequencing for chromosomal aberrations.

  • samplot - Plot structural variant signals from many BAMs and CRAMs.

  • smoove - structural variant calling and genotyping with existing tools, but, smoothly.

  • gnomAD-SV - structural variants from deep WGS, added to gnomAD browser. 14,891 genomes, average statistics of SVs in general population.

Power

Miscellaneous