An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data.

BackgroundThe popularity of massively parallel exome and transcriptome sequencing projects demands new data mining tools with a comprehensive set of features to support a wide range of analysis tasks.ResultsSeqGene, a new data mining tool, supports mutation detection and annotation, dbSNP and 1000 Genome data integration, RNA-Seq expression quantification, mutation and coverage visualization, allele specific expression (ASE), differentially expressed genes (DEGs) identification, copy number variation (CNV) analysis, and gene expression quantitative trait loci (eQTLs) detection. We also developed novel methods for testing the association between SNP and expression and identifying genotype-controlled DEGs. We showed that the results generated from SeqGene compares favourably to other existing methods in our case studies.ConclusionSeqGene is designed as a general-purpose software package. It supports both paired-end reads and single reads generated on most sequencing platforms; it runs on all major types of computers; it supports arbitrary genome assemblies for arbitrary organisms; and it scales well to support both large and small scale sequencing projects. The software homepage is http://seqgene.sourceforge.net.

Analysis of NGS (Next Generation Sequencing) data is a computationally demanding task requiring large amounts of CPU, memory, and disk space. There is also a requirement for high performance data storage systems, resilient to hardware failure, to be connected directly to the computing infrastructure (typically a multi-node cluster) to store large quantities of NGS data reliably. Traditional shared file systems such as NFS (Network File System) do not offer the performance, scalability or cache coherence required by modern NGS data analysis, so alternatives including GlusterFS, Ceph, and Lustre have been developed. However, there is a trade-off between data safety on replicated local storage and degradation of performance across distributed storage. Resilience to hardware failure is typically provided by RAID (Redundant Array of Independent Disks) and redundant storage nodes. Here we describe the evaluation of an alternative file system, RozoFS ( https://github.com/rozofs/rozofs ) for use with demanding NGS data analysis workloads. We used a synthetic data set (DREAM-TCGA data set 3) to run a complete tumor-normal analysis pipeline (“bcbio”, https://github.com/chapmanb/bcbio-nextgen ), including base quality recalibration, local indel realignment, somatic variant calling, and structural variants as a benchmark to compare RozoFS with a traditional shared file system (NFS) on two different HPC (High Performance Computing; Cloud4CaRE project) clusters. Our results show high reliability and good performance of RozoFS compared to NFS, in particular, during heavy I/O workloads. These findings indicate that the reliability and robustness of RozoFS's make it a good candidate for demanding NGS analysis workloads.

Major advances in genomics studies, particularly the introduction of high-throughput sequencing and the evolution of genotyping platforms have led to the emergence of big data in the biological sciences and a growing need to make sense of this data. This has largely fostered the evolution of methods and tools for genomic data analysis (especially of diseased conditions) with the aim of uncovering the genotype-phenotype relationships in such diseased conditions. Due to the growing complexity and volume of next-generation sequencing data available in biological sciences, there is a growing need to developed pipelines that can handle these data while automating most of the steps involved in these analyses. The aim of this study is to develop a computational pipeline for the analysis of next-generation sequencing data using Nextflow and Docker. Since different steps and tools are involved in the analysis of whole genome and whole exome sequencing data, the aim of the study was achieved by developing scripts for selected genome analysis tools, building a computational pipeline for the selected tools and performing unit and integration testing for the pipeline. The pipeline which was built on the framework of the well-established GATK best-practices workflow, integrated the following tools: FastQC, MultiQC, Jellyfish, genomeScope2.0, BWA, GATK and SnpEff. These tools were involved in performing the different steps of the NGS analyses which included quality control check, genome size heterozygosity, alignment or mapping, variant calling and annotation. Nextflow was employed in this pipeline as a workflow management system and Docker was used for containerising all the tools and their software dependencies. The developed pipeline was then tested to verify its utility in NGS data analysis. Pipeline development is very important in genomics research because, it could help improve the quality and reliability of research outcomes and facilitate the sharing and comparison of data across different studies and research groups. Having a pipeline that can effectively be used in quick and simple analysis of genomes will significantly help in uncovering biologically meaningful or clinically significant variants. It is expected that the outcome of this study will significantly impact studies into the genetic basis of human diseases and precision medicine.

The ADAMTS7 locus has been identified in multiple recent genome-wide association studies as being associated with coronary artery disease (CAD) in humans. Based on these associations, we hypothesized that functional rare variants in the gene may be causal risk alleles for CAD in humans. In order to identify rare variants in the human ADAMTS7 gene, we used data from the NHLBI Exome Sequencing Project (ESP) of 6503 participants, which, as per Exome Variant Server (data release ESP6500SI-V2), identified 404 variants in the entire genic sequence of ADAMTS7, of which 169 were missense variants and 2 were nonsense variants. We acquired BAM files for the ADAMTS7 region in 5438 ESP samples and performed in-house realignment, filtering and variant calling for the ADAMTS7 gene using the following criteria; sequences needed ≥90% alignment to the reference sequence, as well as ≥2% better alignment to the actual ADAMTS7 gene vs. pseudogenes or else they were excluded from analysis. In these custom analyses we identified 103 missense and 2 nonsense variants. In addition, we performed Sanger sequencing of 200 PennCath patients (100 MI cases, 100 controls) using primer design that accounted for pseudogenes. For variant calling we only used data with 2X coverage in >50% of samples tested (19/28 amplicons). The Sanger sequence data revealed 27 missense and zero nonsense variants. Comparison of the custom analysis of ESP data and independent Sanger sequencing provided high-confidence calls for 12 coding variants (including the S214P variant, a lead SNP in the GWAS association) in the ADAMTS7 gene. In summary, several variants in the ADAMTS7 gene included in the Exome Variant Server appear to arise from pseudogenes, while a number of high-confidence ADAMTS7 variants are not included on the exome chip. By combining multiple sequencing technologies from multiple studies, we identified 12 variants located in the actual coding sequence of ADAMTS7 for future functional studies. In summary, pseuodogenes present a unique challenge to identifying coding variants in the age of exome and whole genome sequencing and require a customized strategy for sequence alignment and variant calling. ADAMTS7 provides a cautionary example and we present one approach to overcome these challenges.

At the First (1996) and Second (1997) International Strategy Meetings on Human DNA Sequencing, the attendees discussed and strongly endorsed the concept that human genomic DNA sequence produced by large-scale DNA sequencing centers funded by the Human Genome Project should release data as rapidly as possible. In the case of unfinished data, it was suggested that sequence assemblies of 1–2 kb in size should be released within 24 hr of generation. Finished data should be submitted to the public sequence databases on a similarly rapid time scale. Most of the funding agencies engaged in supporting the Human Genome Project have adopted policies that reflect the importance of rapid dissemination of genomic sequence data. Similarly the nucleotide sequence databases have made provision for the acceptance of the unfinished data as well as, of course, finished sequence data. At the 1998 meeting, the question was raised about similar data dissemination practices for genomic sequence data from other organisms. The attendees unanimously agreed that this was also critical and adopted the following statement as individual scientists. ‘‘As extensive determination of the genomic DNA sequence of several organisms proceeds, it is increasingly clear that sequence information has enormous and immediate scientific value, even prior to its final assembly and completion. Delaying the release of either unfinished or finished genomic DNA sequence data serves no useful purpose and actually has the effect of slowing the progress of research. Therefore, the attendees at the Third International Strategy Meeting on Human Genome Sequencing (Bermuda, Feb. 27–28, 1998) agreed unanimously to support, as individual scientists, the view that all publicly funded large-scale DNA sequencing projects, regardless of the organism, should deposit data immediately into the public domain, following the same guidelines that have previously been adopted by this group for human genomic sequence [see D.R. Bentley, Science 274: 533–534 (1996); http:// hugo.gdb.org/bermuda.htm]. The scientists attending this meeting will continue to adhere to these principles and urge all other scientists and policymaking groups involved in large-scale sequencing to adopt them as well.’’ This statement does not yet reflect, nor is it binding on, the policy of any funding agency. The attendees at the Third International Strategy Meeting on Human Genome Sequencing were Mark Adams, David Bentley, Bruce Birren, Martin Bobrow, Elbert Branscomb, Graham Cameron, Anthony V. Carrano, Ellson Chen, Francis Collins, David Cox, Mike Davies, Pieter de Jong, Richard Durbin, Glen Evans, Asao Fujiyama, Richard Gibbs, Eric Green, Phil Green, Mark Guyer, LaDeana Hillier, Ursula Hurtenbach, Elke Jordan, Jill Kent, Fumihiko Kikuchi, Steven Koonin, Jane Lamerdin, Eric Lander, Chris Martin, Richard McCombie, John McPherson, Michael Morgan, Richard Myers, James Ostell, Ari Patrinos, Jane Peterson, Bruce Roe, Jane Rogers, Andre Rosenthal, Yoshiyuki Sakaki, Barbara Skene, Edwin Southern, Jilly Steward, John Sulston, Mark Vaudin, Jeffrey Wadsworth, Robert Waterson, James Watson, Jean Weissenbach, Richard Wilson, and MarieLaure Yaspo.

The EMBL Nucleotide Sequence Database () at the EMBL European Bioinformatics Institute, UK, offers a large and freely accessible collection of nucleotide sequences and accompanying annotation. The database is maintained in collaboration with DDBJ and GenBank. Data are exchanged between the collaborating databases on a daily basis to achieve optimal synchrony. Webin is the preferred tool for individual submissions of nucleotide sequences, including Third Party Annotation, alignments and bulk data. Automated procedures are provided for submissions from large-scale sequencing projects and data from the European Patent Office. In 2006, the volume of data has continued to grow exponentially. Access to the data is provided via SRS, ftp and variety of other methods. Extensive external and internal cross-references enable users to search for related information across other databases and within the database. All available resources can be accessed via the EBI home page at . Changes over the past year include changes to the file format, further development of the EMBLCDS dataset and developments to the XML format.

ABO is a blood group system of high clinical significance due to the prevalence of ABO variation that can cause major, potentially life-threatening, transfusion reactions. Using multiple large-scale next-generation sequence data sets, we demonstrate the application of read-depth approaches to discover previously unsuspected structural variation (SV) in the ABO gene in individuals of African ancestry. Our analysis of SV in the ABO gene across 6432 exomes reveals a partial deletion in the ABO gene in 32 individuals of African ancestry that predicts a novel O allele. Our study demonstrates the power that analyses of large-scale sequencing data, particularly data sets containing underrepresented populations, can provide in identifying novel SVs.

The Familial Alzheimer Sequencing (FASe) project aims to identify rare and high penetrant variants that have strong effect in the etiology of Alzheimer Disease (AD) by using sequencing data from families densely affected by late onset AD (fLOAD). We have generated whole genome sequence (WGS) data for 952 samples (758 cases, 194 controls) from the Knight-ADRC at Washington University (WASHU), the NIALOAD and NCRAD repositories. These samples are being added to our current dataset of whole exome (WES) and WGS from 1,235 non-Hispanic white participants (824 cases, 411 controls) across 285 fLOAD families. These samples have no or minimum overlap with the families sequenced by the ADSP consortia which will also be incorporated to our dataset; a total of 440 families and 3,187 samples (average of 5 cases and 2 controls per family) will be analyzed. We are processing all the data using the same bioinformatics pipeline. Briefly, sequence reads are aligned against reference build GRCh38 using BWA; variant calling is restricted to exonic regions following GATK v4.1.2 best practices. Data analysis includes single variant association, segregation, gene-based and pathway analysis. We have detected a genetic cross-over between AD, Frontotemporal Dementia and Parkinson disease, and we also identified rare variants in novel candidate genes for AD (PLD3, UNC5C, CPAMD8) highlighting the power of our dataset and the feasibility of our approach. We hope to identify novel variants and pathways implicated on AD, which will be followed-up in the case-control ADSP.

Next-generation sequencing (NGS) techniques produce high quantities of DNA and RNA sequencing data, enabling variation analysis at a whole genome level. Due to the massive availability of NGS data, the processing and analysis now constitute a serious challenge for research in life sciences. This necessitates the development of novel, user-friendly and scalable software tools, which are able to jointly handle large NGS data sets in order to break the bottleneck that has already moved from data generation to data analysis in high-throughput biology. Here we introduce a highly efficient NGS analysis and visualization software tool (Rikurator), which is designed for researchers who are struggling with NGS data analysis and may not have access to a dedicated bioinformatics team. Rikurator can be applied to a multitude of genomic analysis tasks, including discovery of pathogenic mutations in familial or sporadic sample sets, analysis of structural variants and somatic characterization of cancer genomes. To these ends the software implements interactive variant filtering and annotation methods based on sample comparison, control data (e.g. ExAC), amino acid changes and quality scores, which provide an intuitive view at the studied samples. Shared candidate genes in a set of dozens of individuals can be discovered in a matter of minutes with an ordinary laptop computer. Importantly, the software is able to analyze hundreds of whole-genome sequenced samples using only a modest amount of RAM. The tool also visualizes and integrates genes, variants (VCF file format), sequence reads (BAM) and user defined features (BED/GFF). In addition, Rikurator is not limited to human studies, supporting reference genomes and genome annotations in standard formats. The software supports targeted (e.g. exome), whole-genome, RNA, Oxford Nanopore and PacBio sequencing data. Rikurator can be run on Windows, Linux and Mac, and it will be freely available after publication. Citation Format: Riku Katainen, Veli Mäkinen, Lauri A. Aaltonen, Esa Pitkänen. Fast and scalable software for comparative variant analysis and visualization of massive next-generation sequencing data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5281.

A central challenge in interpreting personal genomes is determining which mutations most likely influence disease. Although progress has been made in scoring the functional impact of individual mutations, the characteristics of the genes in which those mutations are found remain largely unexplored. For example, genes known to carry few common functional variants in healthy individuals may be judged more likely to cause certain kinds of disease than genes known to carry many such variants. Until now, however, it has not been possible to develop a quantitative assessment of how well genes tolerate functional genetic variation on a genome-wide scale. Here we describe an effort that uses sequence data from 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project (ESP). Specifically, we develop an intolerance scoring system that assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene. To illustrate the utility of this intolerance score, we show that genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease, but with striking variation in intolerance among genes causing different classes of genetic disease. We conclude by showing that use of an intolerance ranking system can aid in interpreting personal genomes and identifying pathogenic mutations.

ColoSeq Provides Comprehensive Lynch and Polyposis Syndrome Mutational Analysis Using Massively Parallel Sequencing

The human reference genome lays the basis for genomic analyses by enabling alignment of sequenced reads. However, the linear human reference genome released by the Genome Reference Consortium only represents a single consensus haplotype, and therefore constitutes a suboptimal representation of human genetic variation. Directed acyclic graph reference genome representations that incorporate information on genetic variation have been shown to improve the accuracy of read alignment, variant calling and other subsequent genomic analyses. A set of bioinformatics tools utilizing graph genomes (Seven Bridges Graph Toolkit; SBGT) for the analysis of next-generation sequencing (NGS) data has recently been published by Seven Bridges in preprint*. In this study, we benchmark the SBGT pipeline on germline variant calling and compare it against other state-of-the-art whole genome pipelines, namely BWA-MEM + GATK4 by the Broad Institute. The analysis includes five whole-genome samples (HG001-HG005) with the truth data established by the Genome in a Bottle Consortium. The results show that the SBGT pipeline has the highest accuracy in small (<50bp) INDEL calling with an F1-score of 0.25% above the average, while scoring 0.06% above the average in SNP calling. Moreover, we show that, unlike the other pipelines, the SBGT pipeline is capable of finding INDELs as long as several kilobases without any additional processing steps. The Mendelian inheritance discordance measurements on CEPH/CEU and Ashkenazim trios demonstrate that the SBGT pipeline offers the most consistent germline variant calling with a score that is 1-2 percentage points better than the other pipelines. Finally, we present an optimized version of the SBGT pipeline that can process a whole-genome sample with 30x coverage on the cloud in 2 hours and under $5.

The reconstruction of individual haplotypes can facilitate the interpretation of disease risks; however, high costs and technical challenges still hinder their assessment in clinical settings. Second-generation sequencing is the gold standard for variant discovery but, due to the production of short reads covering small genomic regions, allows only indirect haplotyping based on statistical methods. In contrast, third-generation methods such as the nanopore sequencing platform developed by Oxford Nanopore Technologies (ONT) generate long reads that can be used for direct haplotyping, with fewer drawbacks. However, robust standards for variant phasing in ONT-based target resequencing efforts are not yet available. In this study, we presented a streamlined proof-of-concept workflow for variant calling and phasing based on ONT data in a clinically relevant 12-kb region of the APOE locus, a hotspot for variants and haplotypes associated with aging-related diseases and longevity. Starting with sequencing data from simple amplicons of the target locus, we demonstrated that ONT data allow for reliable single-nucleotide variant (SNV) calling and phasing from as little as 60 reads, although the recognition of indels is less efficient. Even so, we identified the best combination of ONT read sets (600) and software (BWA/Minimap2 and HapCUT2) that enables full haplotype reconstruction when both SNVs and indels have been identified previously using a highly-accurate sequencing platform. In conclusion, we established a rapid and inexpensive workflow for variant phasing based on ONT long reads. This allowed for the analysis of multiple samples in parallel and can easily be implemented in routine clinical practice, including diagnostic testing.

A sound analysis of DNA sequencing data is important to extract meaningful information and infer quantities of interest. Sequencing and mapping errors coupled with low and variable coverage hamper the identification of genotypes and variants and the estimation of population genetic parameters. Methods and implementations to estimate population genetic parameters from sequencing data available nowadays either are suitable for the analysis of genomes from model organisms only, require moderate sequencing coverage, or are not easily adaptable to specific applications. To address these issues, we introduce ngsJulia, a collection of templates and functions in Julia language to process short-read sequencing data for population genetic analysis. We further describe two implementations, ngsPool and ngsPloidy, for the analysis of pooled sequencing data and polyploid genomes, respectively. Through simulations, we illustrate the performance of estimating various population genetic parameters using these implementations, using both established and novel statistical methods. These results inform on optimal experimental design and demonstrate the applicability of methods in ngsJulia to estimate parameters of interest even from low coverage sequencing data. ngsJulia provide users with a flexible and efficient framework for ad hoc analysis of sequencing data.ngsJulia is available from: https://github.com/mfumagalli/ngsJulia

A sound analysis of DNA sequencing data is important to extract meaningful information and infer quantities of interest. Sequencing and mapping errors coupled with low and variable coverage hamper the identification of genotypes and variants and the estimation of population genetic parameters. Methods and implementations to estimate population genetic parameters from sequencing data available nowadays either are suitable for the analysis of genomes from model organisms only, require moderate sequencing coverage, or are not easily adaptable to specific applications. To address these issues, we introduce ngsJulia, a collection of templates and functions in Julia language to process short-read sequencing data for population genetic analysis. We further describe two implementations, ngsPool and ngsPloidy, for the analysis of pooled sequencing data and polyploid genomes, respectively. Through simulations, we illustrate the performance of estimating various population genetic parameters using these implementations, using both established and novel statistical methods. These results inform on optimal experimental design and demonstrate the applicabil- ity of methods in ngsJulia to estimate parameters of interest even from low coverage sequencing data. ngsJulia provide users with a flexible and efficient framework for ad hoc analysis of sequencing data.ngsJulia is available from: https://github.com/mfumagalli/ngsJulia