HapMuC: somatic mutation calling using heterozygous germ line variants near candidate mutations

Abstract

Motivation: Identifying somatic changes from tumor and matched normal sequences has become a standard approach in cancer research. More specifically, this requires accurate detection of somatic point mutations with low allele frequencies in impure and heterogeneous cancer samples. Although haplotype phasing information derived by using heterozygous germ line variants near candidate mutations would improve accuracy, no somatic mutation caller that uses such information is currently available.Results: We propose a Bayesian hierarchical method, termed HapMuC, in which power is increased by using available information on heterozygous germ line variants located near candidate mutations. We first constructed two generative models (the mutation model and the error model). In the generative models, we prepared candidate haplotypes, considering a heterozygous germ line variant if available, and the observed reads were realigned to the haplotypes. We then inferred the haplotype frequencies and computed the marginal likelihoods using a variational Bayesian algorithm. Finally, we derived a Bayes factor for evaluating the possibility of the existence of somatic mutations. We also demonstrated that our algorithm has superior specificity and sensitivity compared with existing methods, as determined based on a simulation, the TCGA Mutation Calling Benchmark 4 datasets and data from the COLO-829 cell line.Availability and implementation: The HapMuC source code is available from http://github.com/usuyama/hapmuc.Contact: imoto@ims.u-tokyo.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data

“…Haplotype-based strategy (as opposed to the mainstream position-based strategy) is widely adopted by structural variant callers in which reads need to be assembled to reconstruct long variants. It is also a powerful strategy for SNV detection and used by Platypus, HapMuC, LocHap, FreeBayes, and MuTect2 [35] , [41] , [42] , [43] , [44] . These algorithms locally assemble reads in a region and generate candidate haplotypes that may be represented by de Bruijn-like graphs.…”

Contralateral axillary metastasis: is surgical treatment the best option?

“…RAS proteins are small GTPases functioning as GDP-GTP-regulated binary switches that control many fundamental cellular processes. RAS proteins connect a great variety of upstream signals from activated Heuristic approaches qSNP [31] , RADIA [32] , Shimmer [33] , SOAPsnv [34] , VarDict [35] , VarScan2 [36] Joint genotype analysis CaVEMan [37] , FaSD-somatic [38] , JointSNVMix2 [39] , SAMtools [40] , Seurat* [41] , SNVSniffer [42] , SomaticSniper [43] , Virmid [44] Allele frequency deepSNV [45] , EBCall [46] , LoFreq [47] , LoLoPicker [48] , MuTect [49] , Strelka [50] Haplotype analysis FreeBayes [51] , HapMuC [52] , LocHap [53] , MuTect2 [49] , Platypus [54] Machine learning BAYSIC [55] , MutationSeq [56] , SNooPer [57] , SomaticSeq [58] Single-sample analysis GATKcan [59] , ISOWN [60] , OutLyzer [61] , Pisces [62] , SiNVICT [63] , SomVarIUS [64] Structural or copy number variation calling APOLOH [65] , BIC-Seq [66] , BreakDancer [67] , Break-Pointer [68] , CNVkit [69] , CoNIFER [70] , Delly [71] , HYDRA …”

Samovar: Single-sample mosaic SNV calling with linked reads

“…In this model, a paired-end read is assigned to a haplotype only if one of the ends overlaps a heterozygous variant phased by Long Ranger. Past work showed that even the phasing information from short fragments can improve mosaic variant calling accuracy [34]. We find while the precision is comparable to the Samovar full model, the number of variant calls is much lower, resulting in a genome-wide recall of 2.0% at 30X and 60X, because there are few sites for which adequate phasing information can be compiled from short reads alone ( Figure S4, Table S4).…”

“…The mosaic mutation will likely be tolerated by the haplotype assembler and the reads will still be assigned to H2 ( Figure 1b). The fact that all the mosaic-carrying reads fall on the same haplotype is a hallmark of post-zygotic mosaicism [11] and contrasts with sequencing error, which would tend to distribute the "mosaic" alleles evenly across haplotypes [34]. Reads with the mosaic allele are called haplotype-discordant reads, and these are the most reliable kind of evidence we can gather in support of mosaic variants.…”