A Bayesian model integration for mutation calling through data partitioning

Abstract

MotivationDetection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled.ResultsWe proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets.Availability and implementation https://github.com/takumorizo/OHVarfinDer.Supplementary information Supplementary data are available at Bioinformatics online.

Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing

“…The latter group can be further segregated into those that generate models that estimate error rates by the analysis of data from a single sample (i.e., single sample/tumoronly mode) (14)(15)(16), data from a single control sample (16)(17)(18), or data from multiple control samples (e.g., cohort of healthy controls) (19)(20)(21). In the case of paired patient's tumor and matched normal sample, Bayesian statistics models are commonly used to identify tumorspecific somatic variants that are distinguishable from the background and the germline variants detected within the matched normal sample (22,23). Some techniques rely on a ploidy assumption to calculate genotype probabilities (24), while others have adapted statistical models to analyze allele frequencies directly (16), thus allowing the identification of rare subclones in existing, complex cancer genomes.…”

A Post-Method Condition Analysis of Using Ensemble Machine Learning for Cancer Prognosis and Diagnosis: a systematic review

“…It first generates two models include of the tumor model and error model by setting partition rules on paired-end reads and datasets, and then this framework integrates these models for mutation calling associated with breast cancer through input data partitioning. So, it is confirmed that the proposed method can improve performance using incorporating heterozygous single nucleotide polymorphisms (SNPs) and strand bias information comparison with other Bayesian network classifiers [107].…”

“…In another study, a novel Bayesian hierarchical model-based method has been proposed. This approach uses single-nucleotide variants (SNVs) and insertions and deletions (InDels) in whole genome sequence data as mutation data [107] obtained from sequencing of the breast cancer cell lines dataset that are available in TCGA [108] and data can be downloaded from https://gdc.cancer.gov/files/public/file/TCGA_mutation_calling_benchmark_files.zip. It first generates two models include of the tumor model and error model by setting partition rules on paired-end reads and datasets, and then this framework integrates these models for mutation calling associated with breast cancer through input data partitioning.…”

Accurate and Flexible Bayesian Mutation Call from Multi-regional Tumor Samples

“…Thus, mutation calling from sequence data sets has become a fundamental analysis in cancer therapy and research. An enormous number of studies [1][2][3][4][5][6][7][8] have been conducted to improve the performance of single-tumor-based mutation call, i.e., mutation call from a tumor and a matched normal sequence data set, and the performance of mutation call is updated annually by modeling properties of raw sequence data sets in more sophisticated manners. Strelka2 and OHVarfinDer construct Bayesian statistical models to utilize sequence data specific properties.…”