Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies

Motivation

Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost.

Results

We describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study.

Conclusions

We ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging ‘big data’ problems in biomedical research brought on by the expansion of NGS technologies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0736-4) contains supplementary material, which is available to authorized users.     

Genotype and SNP calling from next-generation sequencing data

Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.     

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

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/.     

An integrative variant analysis suite for whole exome next-generation sequencing data

Background  

Whole exome capture sequencing allows researchers to cost-effectively sequence the coding regions of the genome. Although the exome capture sequencing methods have become routine and well established, there is currently a lack of tools specialized for variant calling in this type of data.     

Consensus rules in variant detection from next-generation sequencing data

A critical step in detecting variants from next-generation sequencing data is post hoc filtering of putative variants called or predicted by computational tools. Here, we highlight four critical parameters that could enhance the accuracy of called single nucleotide variants and insertions/deletions: quality and deepness, refinement and improvement of initial mapping, allele/strand balance, and examination of spurious genes. Use of these sequence features appropriately in variant filtering could greatly improve validation rates, thereby saving time and costs in next-generation sequencing projects.     

Comparison and evaluation of two exome capture kits and sequencing platforms for variant calling

Background

To promote the clinical application of next-generation sequencing, it is important to obtain accurate and consistent variants of target genomic regions at low cost. Ion Proton, the latest updated semiconductor-based sequencing instrument from Life Technologies, is designed to provide investigators with an inexpensive platform for human whole exome sequencing that achieves a rapid turnaround time. However, few studies have comprehensively compared and evaluated the accuracy of variant calling between Ion Proton and Illumina sequencing platforms such as HiSeq 2000, which is the most popular sequencing platform for the human genome. The Ion Proton sequencer combined with the Ion TargetSeq™ Exome Enrichment Kit together make up TargetSeq-Proton, whereas SureSelect-Hiseq is based on the Agilent SureSelect Human All Exon v4 Kit and the HiSeq 2000 sequencer.

Results

Here, we sequenced exonic DNA from four human blood samples using both TargetSeq-Proton and SureSelect-HiSeq. We then called variants in the exonic regions that overlapped between the two exome capture kits (33.6 Mb). The rates of shared variant loci called by two sequencing platforms were from 68.0 to 75.3 % in four samples, whereas the concordance of co-detected variant loci reached 99 %. Sanger sequencing validation revealed that the validated rate of concordant single nucleotide polymorphisms (SNPs) (91.5 %) was higher than the SNPs specific to TargetSeq-Proton (60.0 %) or specific to SureSelect-HiSeq (88.3 %). With regard to 1-bp small insertions and deletions (InDels), the Sanger sequencing validated rates of concordant variants (100.0 %) and SureSelect-HiSeq-specific (89.6 %) were higher than those of TargetSeq-Proton-specific (15.8 %).

Conclusions

In the sequencing of exonic regions, a combination of using of two sequencing strategies (SureSelect-HiSeq and TargetSeq-Proton) increased the variant calling specificity for concordant variant loci and the sensitivity for variant loci called by any one platform. However, for the sequencing of platform-specific variants, the accuracy of variant calling by HiSeq 2000 was higher than that of Ion Proton, specifically for the InDel detection. Moreover, the variant calling software also influences the detection of SNPs and, specifically, InDels in Ion Proton exome sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1796-6) contains supplementary material, which is available to authorized users.     

Replication strategies for rare variant complex trait association studies via next-generation sequencing

There is solid evidence that complex traits can be caused by rare variants. Next-generation sequencing technologies are powerful tools for mapping rare variants. Confirmation of significant findings in stage 1 through replication in an independent stage 2 sample is necessary for association studies. For gene-based mapping of rare variants, two replication strategies are possible: (1) variant-based replication, wherein only variants from nucleotide sites uncovered in stage 1 are genotyped and followed-up and (2) sequence-based replication, wherein the gene region is sequenced in the replication sample and both known and novel variants are tested. The efficiency of the two strategies is dependent on the proportions of causative variants discovered in stage 1 and sequencing/genotyping errors. With rigorous population genetic and phenotypic models, it is demonstrated that sequence-based replication is consistently more powerful. However, the power gain is small (1) for large-scale studies with thousands of individuals, because a large fraction of causative variant sites can be observed and (2) for small- to medium-scale studies with a few hundred samples, because a large proportion of the locus population attributable risk can be explained by the uncovered variants. Therefore, genotyping can be a temporal solution for replicating genetic studies if stage 1 and 2 samples are drawn from the same population. However, sequence-based replication is advantageous if the stage 1 sample is small or novel variants discovery is also of interest. It is shown that currently attainable levels of sequencing error only minimally affect the comparison, and the advantage of sequence-based replication remains.     

Generating amplicon reads for microbial community assessment with next-generation sequencing

Marker gene amplicon sequencing is often preferred over whole genome sequencing for microbial community characterization, due to its lower cost while still enabling assessment of uncultivable organisms. This technique involves many experimental steps, each of which can be a source of errors and bias. We present an up-to-date overview of the whole experimental pipeline, from sampling to sequencing reads, and give information allowing for informed choices at each step of both planning and execution of a microbial community assessment study. When applicable, we also suggest ways of avoiding inherent pitfalls in amplicon sequencing.     

BIGpre: a quality assessment package for next-generation sequencing data

The emergence of next-generation sequencing (NGS) technologies has significantly improved sequencing throughput and reduced costs. However, the short read length, duplicate reads and massive volume of data make the data processing much more difficult and complicated than the first-generation sequencing technology. Although there are some software packages developed to assess the data quality, those packages either are not easily available to users or require bioinformatics skills and computer resources. Moreover, almost all the quality assessment software currently available didn’t taken into account the sequencing errors when dealing with the duplicate assessment in NGS data. Here, we present a new user-friendly quality assessment software package called BIGpre, which works for both Illumina and 454 platforms. BIGpre contains all the functions of other quality assessment software, such as the correlation between forward and reverse reads, read GC-content distribution, and base Ns quality. More importantly, BIGpre incorporates associated programs to detect and remove duplicate reads after taking sequencing errors into account and trimming low quality reads from raw data as well. BIGpre is primarily written in Perl and integrates graphical capability from the statistics package R. This package produces both tabular and graphical summaries of data quality for sequencing datasets from Illumina and 454 platforms. Processing hundreds of millions reads within minutes, this package provides immediate diagnostic information for user to manipulate sequencing data for downstream analyses. BIGpre is freely available at http://bigpre.sourceforge.net/.     

An efficient and tunable parameter to improve variant calling for whole genome and exome sequencing data

Next generation sequencing (NGS) has traditionally been performed in various fields including agricultural to clinical and there are so many sequencing platforms available in order to obtain accurate and consistent results. However, these platforms showed amplification bias when facilitating variant calls in personal genomes. Here, we sequenced whole genomes and whole exomes from ten Korean individuals using Illumina and Ion Proton, respectively to find the vulnerability and accuracy of NGS platform in the GC rich/poor area. Overall, a total of 1013 Gb reads from Illumina and ~39.1 Gb reads from Ion Proton were analyzed using BWA-GATK variant calling pipeline. Furthermore, conjunction with the VQSR tool and detailed filtering strategies, we achieved high-quality variants. Finally, each of the ten variants from Illumina only, Ion Proton only, and intersection was selected for Sanger validation. The validation results revealed that Illumina platform showed higher accuracy than Ion Proton. The described filtering methods are advantageous for large population-based whole genome studies designed to identify common and rare variations associated with complex diseases.     

Base-calling for next-generation sequencing platforms

Next-generation sequencing platforms are dramatically reducing the cost of DNA sequencing. With these technologies, bases are inferred from light intensity signals, a process commonly referred to as base-calling. Thus, understanding and improving the quality of sequence data generated using these approaches are of high interest. Recently, a number of papers have characterized the biases associated with base-calling and proposed methodological improvements. In this review, we summarize recent development of base-calling approaches for the Illumina and Roche 454 sequencing platforms.     

Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA

A primary component of next-generation sequencing analysis is to align short reads to a reference genome, with each read aligned independently. However, reads that observe the same non-reference DNA sequence are highly correlated and can be used to better model the true variation in the target genome. A novel short-read micro re-aligner, SRMA, that leverages this correlation to better resolve a consensus of the underlying DNA sequence of the targeted genome is described here.     

Family-based association studies for next-generation sequencing

An individual's disease risk is determined by the compounded action of both common variants, inherited from remote ancestors, that segregated within the population and rare variants, inherited from recent ancestors, that segregated mainly within pedigrees. Next-generation sequencing (NGS) technologies generate high-dimensional data that allow a nearly complete evaluation of genetic variation. Despite their promise, NGS technologies also suffer from remarkable limitations: high error rates, enrichment of rare variants, and a large proportion of missing values, as well as the fact that most current analytical methods are designed for population-based association studies. To meet the analytical challenges raised by NGS, we propose a general framework for sequence-based association studies that can use various types of family and unrelated-individual data sampled from any population structure and a universal procedure that can transform any population-based association test statistic for use in family-based association tests. We develop family-based functional principal-component analysis (FPCA) with or without smoothing, a generalized T(2), combined multivariate and collapsing (CMC) method, and single-marker association test statistics. Through intensive simulations, we demonstrate that the family-based smoothed FPCA (SFPCA) has the correct type I error rates and much more power to detect association of (1) common variants, (2) rare variants, (3) both common and rare variants, and (4) variants with opposite directions of effect from other population-based or family-based association analysis methods. The proposed statistics are applied to two data sets with pedigree structures. The results show that the smoothed FPCA has a much smaller p value than other statistics.     

PipeIT2: A tumor-only somatic variant calling workflow for molecular diagnostic Ion Torrent sequencing data

Precision oncology relies on the accurate identification of somatic mutations in cancer patients. While the sequencing of the tumoral tissue is frequently part of routine clinical care, the healthy counterparts are rarely sequenced. We previously published PipeIT, a somatic variant calling workflow specific for Ion Torrent sequencing data enclosed in a Singularity container. PipeIT combines user-friendly execution, reproducibility and reliable mutation identification, but relies on matched germline sequencing data to exclude germline variants. Expanding on the original PipeIT, here we describe PipeIT2 to address the clinical need to define somatic mutations in the absence of germline control. We show that PipeIT2 achieves a > 95% recall for variants with variant allele fraction >10%, reliably detects driver and actionable mutations and filters out most of the germline mutations and sequencing artifacts. With its performance, reproducibility, and ease of execution, PipeIT2 is a valuable addition to molecular diagnostics laboratories.     

Bambino: a variant detector and alignment viewer for next-generation sequencing data in the SAM/BAM format

SUMMARY: Bambino is a variant detector and graphical alignment viewer for next-generation sequencing data in the SAM/BAM format, which is capable of pooling data from multiple source files. The variant detector takes advantage of SAM-specific annotations, and produces detailed output suitable for genotyping and identification of somatic mutations. The assembly viewer can display reads in the context of either a user-provided or automatically generated reference sequence, retrieve genome annotation features from a UCSC genome annotation database, display histograms of non-reference allele frequencies, and predict protein-coding changes caused by SNPs. AVAILABILITY: Bambino is written in platform-independent Java and available from https://cgwb.nci.nih.gov/goldenPath/bamview/documentation/index.html, along with documentation and example data. Bambino may be launched online via Java Web Start or downloaded and run locally.