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/.     

multiSNV: a probabilistic approach for improving detection of somatic point mutations from multiple related tumour samples

Somatic variant analysis of a tumour sample and its matched normal has been widely used in cancer research to distinguish germline polymorphisms from somatic mutations. However, due to the extensive intratumour heterogeneity of cancer, sequencing data from a single tumour sample may greatly underestimate the overall mutational landscape. In recent studies, multiple spatially or temporally separated tumour samples from the same patient were sequenced to identify the regional distribution of somatic mutations and study intratumour heterogeneity. There are a number of tools to perform somatic variant calling from matched tumour-normal next-generation sequencing (NGS) data; however none of these allow joint analysis of multiple same-patient samples. We discuss the benefits and challenges of multisample somatic variant calling and present multiSNV, a software package for calling single nucleotide variants (SNVs) using NGS data from multiple same-patient samples. Instead of performing multiple pairwise analyses of a single tumour sample and a matched normal, multiSNV jointly considers all available samples under a Bayesian framework to increase sensitivity of calling shared SNVs. By leveraging information from all available samples, multiSNV is able to detect rare mutations with variant allele frequencies down to 3% from whole-exome sequencing experiments.     

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.     

MutAid: Sanger and NGS Based Integrated Pipeline for Mutation Identification,Validation and Annotation in Human Molecular Genetics

Traditional Sanger sequencing as well as Next-Generation Sequencing have been used for the identification of disease causing mutations in human molecular research. The majority of currently available tools are developed for research and explorative purposes and often do not provide a complete, efficient, one-stop solution. As the focus of currently developed tools is mainly on NGS data analysis, no integrative solution for the analysis of Sanger data is provided and consequently a one-stop solution to analyze reads from both sequencing platforms is not available. We have therefore developed a new pipeline called MutAid to analyze and interpret raw sequencing data produced by Sanger or several NGS sequencing platforms. It performs format conversion, base calling, quality trimming, filtering, read mapping, variant calling, variant annotation and analysis of Sanger and NGS data under a single platform. It is capable of analyzing reads from multiple patients in a single run to create a list of potential disease causing base substitutions as well as insertions and deletions. MutAid has been developed for expert and non-expert users and supports four sequencing platforms including Sanger, Illumina, 454 and Ion Torrent. Furthermore, for NGS data analysis, five read mappers including BWA, TMAP, Bowtie, Bowtie2 and GSNAP and four variant callers including GATK-HaplotypeCaller, SAMTOOLS, Freebayes and VarScan2 pipelines are supported. MutAid is freely available at https://sourceforge.net/projects/mutaid.     

VPOT:A Customizable Variant Prioritization Ordering Tool for Annotated Variants

Next-generation sequencing(NGS) technologies generate thousands to millions of genetic variants per sample.Identification of potential disease-causal variants is labor intensive as it relies on filtering using various annotation metrics and consideration of multiple pathogenicity prediction scores.We have developed VPOT(variant prioritization ordering tool),a python-based command line tool that allows researchers to create a single fully customizable pathogenicity ranking score from any number of annotation values,each with a user-defined weighting.The use of VPOT can be informative when analyzing entire cohorts,as variants in a cohort can be prioritized.VPOT also provides additional functions to allow variant filtering based on a candidate gene list or by affected status in a family pedigree.VPOT outperforms similar tools in terms of efficacy,flexibility,scalability,and computational performance.VPOT is freely available for public use at Git Hub(https://github.com/VCCRI/VPOT/).Documentation for installation along with a user tutorial,a default parameter file,and test data are provided.     

Validation and assessment of variant calling pipelines for next-generation sequencing

Background

The processing and analysis of the large scale data generated by next-generation sequencing (NGS) experiments is challenging and is a burgeoning area of new methods development. Several new bioinformatics tools have been developed for calling sequence variants from NGS data. Here, we validate the variant calling of these tools and compare their relative accuracy to determine which data processing pipeline is optimal.

Results

We developed a unified pipeline for processing NGS data that encompasses four modules: mapping, filtering, realignment and recalibration, and variant calling. We processed 130 subjects from an ongoing whole exome sequencing study through this pipeline. To evaluate the accuracy of each module, we conducted a series of comparisons between the single nucleotide variant (SNV) calls from the NGS data and either gold-standard Sanger sequencing on a total of 700 variants or array genotyping data on a total of 9,935 single-nucleotide polymorphisms. A head to head comparison showed that Genome Analysis Toolkit (GATK) provided more accurate calls than SAMtools (positive predictive value of 92.55% vs. 80.35%, respectively). Realignment of mapped reads and recalibration of base quality scores before SNV calling proved to be crucial to accurate variant calling. GATK HaplotypeCaller algorithm for variant calling outperformed the UnifiedGenotype algorithm. We also showed a relationship between mapping quality, read depth and allele balance, and SNV call accuracy. However, if best practices are used in data processing, then additional filtering based on these metrics provides little gains and accuracies of >99% are achievable.

Conclusions

Our findings will help to determine the best approach for processing NGS data to confidently call variants for downstream analyses. To enable others to implement and replicate our results, all of our codes are freely available at http://metamoodics.org/wes.

     

Comprehensive Analysis to Improve the Validation Rate for Single Nucleotide Variants Detected by Next-Generation Sequencing

Next-generation sequencing (NGS) has enabled the high-throughput discovery of germline and somatic mutations. However, NGS-based variant detection is still prone to errors, resulting in inaccurate variant calls. Here, we categorized the variants detected by NGS according to total read depth (TD) and SNP quality (SNPQ), and performed Sanger sequencing with 348 selected non-synonymous single nucleotide variants (SNVs) for validation. Using the SAMtools and GATK algorithms, the validation rate was positively correlated with SNPQ but showed no correlation with TD. In addition, common variants called by both programs had a higher validation rate than caller-specific variants. We further examined several parameters to improve the validation rate, and found that strand bias (SB) was a key parameter. SB in NGS data showed a strong difference between the variants passing validation and those that failed validation, showing a validation rate of more than 92% (filtering cutoff value: alternate allele forward [AF]≥20 and AF<80 in SAMtools, SB<–10 in GATK). Moreover, the validation rate increased significantly (up to 97–99%) when the variant was filtered together with the suggested values of mapping quality (MQ), SNPQ and SB. This detailed and systematic study provides comprehensive recommendations for improving validation rates, saving time and lowering cost in NGS analyses.     

An Integrated Approach for Analyzing Clinical Genomic Variant Data from Next-Generation Sequencing

Next-generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis for data interpretation. We have developed an integrated approach for end-to-end clinical NGS data analysis from variant detection to functional profiling. Robust bioinformatics pipelines were implemented for genome alignment, single nucleotide polymorphism (SNP), small insertion/deletion (InDel), and copy number variation (CNV) detection of whole exome sequencing (WES) data from the Illumina platform. Quality-control metrics were analyzed at each step of the pipeline by use of a validated training dataset to ensure data integrity for clinical applications. We annotate the variants with data regarding the disease population and variant impact. Custom algorithms were developed to filter variants based on criteria, such as quality of variant, inheritance pattern, and impact of variant on protein function. The developed clinical variant pipeline links the identified rare variants to Integrated Genome Viewer for visualization in a genomic context and to the Protein Information Resource’s iProXpress for rich protein and disease information. With the application of our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for downstream variant filtering that empowers clinicians and researchers to interpret more effectively the relevance of genomic alterations within a rare genetic disease.     

SESAME (SEquence Sorter & AMplicon Explorer): genotyping based on high-throughput multiplex amplicon sequencing

SUMMARY: Characterizing genetic diversity through genotyping short amplicons is central to evolutionary biology. Next-generation sequencing (NGS) technologies changed the scale at which these type of data are acquired. SESAME is a web application package that assists genotyping of multiplexed individuals for several markers based on NGS amplicon sequencing. It automatically assigns reads to loci and individuals, corrects reads if standard samples are available and provides an intuitive graphical user interface (GUI) for allele validation based on the sequences and associated decision-making tools. The aim of SESAME is to help allele identification among a large number of sequences. AVAILABILITY: SESAME and its documentation are freely available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported Licence for Windows and Linux from http://www1.montpellier.inra.fr/CBGP/NGS/ or http://tinyurl.com/ngs-sesame.     

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data

TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways. AVAILABILITY AND IMPLEMENTATION: TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization tool, or as a comprehensive workflow integrating sequencing alignment and variant calling. The executables, instructions and the Amazon Cloud Images of TREAT can be downloaded at the website: http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm.     

SeqSQC: A Bioconductor Package for Evaluating the Sample Quality of Next-generation Sequencing Data

As next-generation sequencing (NGS) technology has become widely used to identify genetic causal variants for various diseases and traits,a number of packages for checking NGS data quality have sprung up in public domains. In addition to the quality of sequencing data,sample quality issues,such as gender mismatch,abnormal inbreeding coefficient,cryptic relatedness,and population outliers,can also have fundamental impact on downstream analysis. However,there is a lack of tools specialized in identifying problematic samples from NGS data,often due to the limitation of sample size and variant counts. We developed SeqSQC,a Bioconductor package,to automate and accelerate sample cleaning in NGS data of any scale. SeqSQC is designed for efficient data storage and access,and equipped with interactive plots for intuitive data visualization to expedite the identification of problematic samples. SeqSQC is available at http://bioconductor. org/packages/SeqSQC.     

MAC: identifying and correcting annotation for multi-nucleotide variations

Background

Next-Generation Sequencing (NGS) technologies have rapidly advanced our understanding of human variation in cancer. To accurately translate the raw sequencing data into practical knowledge, annotation tools, algorithms and pipelines must be developed that keep pace with the rapidly evolving technology. Currently, a challenge exists in accurately annotating multi-nucleotide variants (MNVs). These tandem substitutions, when affecting multiple nucleotides within a single protein codon of a gene, result in a translated amino acid involving all nucleotides in that codon. Most existing variant callers report a MNV as individual single-nucleotide variants (SNVs), often resulting in multiple triplet codon sequences and incorrect amino acid predictions. To correct potentially misannotated MNVs among reported SNVs, a primary challenge resides in haplotype phasing which is to determine whether the neighboring SNVs are co-located on the same chromosome.

Results

Here we describe MAC (Multi-Nucleotide Variant Annotation Corrector), an integrative pipeline developed to correct potentially mis-annotated MNVs. MAC was designed as an application that only requires a SNV file and the matching BAM file as data inputs. Using an example data set containing 3024 SNVs and the corresponding whole-genome sequencing BAM files, we show that MAC identified eight potentially mis-annotated SNVs, and accurately updated the amino acid predictions for seven of the variant calls.

Conclusions

MAC can identify and correct amino acid predictions that result from MNVs affecting multiple nucleotides within a single protein codon, which cannot be handled by most existing SNV-based variant pipelines. The MAC software is freely available and represents a useful tool for the accurate translation of genomic sequence to protein function.     

一种基于二代测序辨识短串联重复序列长度变异的新方法及其在人类遗传疾病研究中的应用

二代测序技术的涌现推动了基因组学研究,特别是在疾病相关的遗传变异研究中发挥了重要作用．虽然大多数遗传变异类型都可以借助于各种二代测序分析工具进行检测,但是仍然存在局限性,比如短串联重复序列的长度变异．许多遗传疾病是由短串联重复序列的长度扩张导致的,尤其是亨廷顿病等多种神经系统疾病．然而,现在几乎没有工具能够利用二代测序检测长度大于测序读长的短串联重复序列变异．为了突破这一限制,我们开发了一个全新的方法,该方法基于双末端二代测序辨识短串联重复序列长度变异,并可估计其扩张长度,将其应用于一项基于全外显子组测序的运动神经元疾病临床研究中,成功地鉴定出致病的短串联重复序列长度扩张．该方法首次原创性地利用测序读长覆盖深度特征来解决短串联重复序列变异检测问题,在人类遗传疾病研究中具有广泛的应用价值,并且对于其他二代测序分析方法的开发具有启发性意义．     

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.     

Development and Validation of a Next-Generation Sequencing Assay for BRCA1 and BRCA2 Variants for the Clinical Laboratory

The objective of this study was to design and validate a next-generation sequencing assay (NGS) to detect BRCA1 and BRCA2 mutations. We developed an assay using random shearing of genomic DNA followed by RNA bait tile hybridization and NGS sequencing on both the Illumina MiSeq and Ion Personal Gene Machine (PGM). We determined that the MiSeq Reporter software supplied with the instrument could not detect deletions greater than 9 base pairs. Therefore, we developed an alternative alignment and variant calling software, Quest Sequencing Analysis Pipeline (QSAP), that was capable of detecting large deletions and insertions. In validation studies, we used DNA from 27 stem cell lines, all with known deleterious BRCA1 or BRCA2 mutations, and DNA from 67 consented control individuals who had a total of 352 benign variants. Both the MiSeq/QSAP combination and PGM/Torrent Suite combination had 100% sensitivity for the 379 known variants in the validation series. However, the PGM/Torrent Suite combination had a lower intra- and inter-assay precision of 96.2% and 96.7%, respectively when compared to the MiSeq/QSAP combination of 100% and 99.4%, respectively. All PGM/Torrent Suite inconsistencies were false-positive variant assignments. We began commercial testing using both platforms and in the first 521 clinical samples MiSeq/QSAP had 100% sensitivity for BRCA1/2 variants, including a 64-bp deletion and a 10-bp insertion not identified by PGM/Torrent Suite, which also suffered from a high false-positive rate. Neither the MiSeq nor PGM platform with their supplied alignment and variant calling software are appropriate for a clinical laboratory BRCA sequencing test. We have developed an NGS BRCA1/2 sequencing assay, MiSeq/QSAP, with 100% analytic sensitivity and specificity in the validation set consisting of 379 variants. The MiSeq/QSAP combination has sufficient performance for use in a clinical laboratory.     

ASAP: an environment for automated preprocessing of sequencing data

Background

Next-generation sequencing (NGS) has yielded an unprecedented amount of data for genetics research. It is a daunting task to process the data from raw sequence reads to variant calls and manually processing this data can significantly delay downstream analysis and increase the possibility for human error. The research community has produced tools to properly prepare sequence data for analysis and established guidelines on how to apply those tools to achieve the best results, however, existing pipeline programs to automate the process through its entirety are either inaccessible to investigators, or web-based and require a certain amount of administrative expertise to set up.

Findings

Advanced Sequence Automated Pipeline (ASAP) was developed to provide a framework for automating the translation of sequencing data into annotated variant calls with the goal of minimizing user involvement without the need for dedicated hardware or administrative rights. ASAP works both on computer clusters and on standalone machines with minimal human involvement and maintains high data integrity, while allowing complete control over the configuration of its component programs. It offers an easy-to-use interface for submitting and tracking jobs as well as resuming failed jobs. It also provides tools for quality checking and for dividing jobs into pieces for maximum throughput.

Conclusions

ASAP provides an environment for building an automated pipeline for NGS data preprocessing. This environment is flexible for use and future development. It is freely available at http://biostat.mc.vanderbilt.edu/ASAP.     

Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.     

NGS QC Toolkit: a toolkit for quality control of next generation sequencing data

Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis.