SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data

Abstract

We develop a statistical tool SNVer for calling common and rare variants in analysis of pooled or individual next-generation sequencing (NGS) data. We formulate variant calling as a hypothesis testing problem and employ a binomial–binomial model to test the significance of observed allele frequency against sequencing error. SNVer reports one single overall P-value for evaluating the significance of a candidate locus being a variant based on which multiplicity control can be obtained. This is particularly desirable because tens of thousands loci are simultaneously examined in typical NGS experiments. Each user can choose the false-positive error rate threshold he or she considers appropriate, instead of just the dichotomous decisions of whether to ‘accept or reject the candidates’ provided by most existing methods. We use both simulated data and real data to demonstrate the superior performance of our program in comparison with existing methods. SNVer runs very fast and can complete testing 300 K loci within an hour. This excellent scalability makes it feasible for analysis of whole-exome sequencing data, or even whole-genome sequencing data using high performance computing cluster. SNVer is freely available at http://snver.sourceforge.net/.

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

“…First, we conduct SNP calling using both our method EM-SNP and SNVer (parameter setting -bq 20 -a 0 -f 0 -p 1 -t 0) [11], a program that has been shown to outperform several other programs for SNP calling including CRISP, SAMtools, and GATK. Unlike many previous programs calling variants as SNPs or not, SNVer ranks variants according to their potential of being true SNPs using the p-value defined in the program.…”

“…Altmann et al [13] improved the computational speed of CRISP and identified SNPs as the variants with different minor allele frequencies across at least two pools. Wei et al [11] developed a statistical tool, called SNVer, for variant identification. For each pool, SNVer first defined a p-value by testing the hypothesis that the minor allele frequency is above a given threshold and then combined the p-values for individual pools to give an overall p-value using Simes methods as in [12].…”

Detection of somatic mutations in tumors using unaligned clonal sequencing data

“…This is especially true for longer reads, when the quality scores for the later positions are worse than those for the earlier positions. The minimum achievable detection frequency for a minor allele has been reported in the literature to be between 3% and 0.1% [22][23][24][25][26][27] and as our data are derived from FFPE-treated DNA samples it is not surprising that our minimum allele frequency cutoff is toward the upper end of this range.…”

The Flagellar Set Fla2 in Rhodobacter sphaeroides Is Controlled by the CckA Pathway and Is Repressed by Organic Acids and the Expression of Fla1

“…Single reads were mapped against the genome of R. sphaeroides WS8N (45) using bowtie2. The .SAM file was converted to .bam and visualized with Artemis and SNVER (46)(47)(48)(49)(50) to identify the differences between the genome sequence and the mapped reads. The changes that were consistently present in all the reads at a particular position were confirmed by PCR followed by Sanger sequencing.…”