SUMMARY: Variant Call Format (VCF), the prevailing representation for germline genotypes in population sequencing, suffers rapid size growth as larger cohorts are sequenced and more rare variants are discovered. We present Sparse Project VCF (spVCF), an evolution of VCF with judicious entropy reduction and run-length encoding, delivering >10× size reduction for modern studies with practically minimal information loss. spVCF interoperates with VCF efficiently, including tabix-based random access. We demonstrate its effectiveness with the DiscovEHR and UK Biobank whole-exome sequencing cohorts. AVAILABILITY AND IMPLEMENTATION: Apache-licensed reference implementation: github.com/mlin/spVCF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics, https://doi.org/10.1093/bioinformatics/bty894 In the supplementary material, the phrase “The pairwise variant disconcordance rate” has been changed to “The pairwise variant concordance rate”. The updated supplementary file is now available online.

SUMMARY: We report VCPA, our SNP/Indel Variant Calling Pipeline and data management tool used for the analysis of whole genome and exome sequencing (WGS/WES) for the Alzheimer's Disease Sequencing Project. VCPA consists of two independent but linkable components: pipeline and tracking database. The pipeline, implemented using the Workflow Description Language and fully optimized for the Amazon elastic compute cloud environment, includes steps from aligning raw sequence reads to variant calling using GATK. The tracking database allows users to view job running status in real time and visualize >100 quality metrics per genome. VCPA is functionally equivalent to the CCDG/TOPMed pipeline. Users can use the pipeline and the dockerized database to process large WGS/WES datasets on Amazon cloud with minimal configuration. AVAILABILITY AND IMPLEMENTATION: VCPA is released under the MIT license and is available for academic and nonprofit use for free. The pipeline source code and step-by-step instructions are available from the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (http://www.niagads.org/VCPA). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: As resequencing projects become more prevalent across a larger number of species, accurate variant identification will further elucidate the nature of genetic diversity and become increasingly relevant in genomic studies. However, the identification of larger genomic variants via DNA sequencing is limited by both the incomplete information provided by sequencing reads and the nature of the genome itself. Long-read sequencing technologies provide high-resolution access to structural variants often inaccessible to shorter reads. RESULTS: We present PBHoney, software that considers both intra-read discordance and soft-clipped tails of long reads (>10,000 bp) to identify structural variants. As a proof of concept, we identify four structural variants and two genomic features in a strain of Escherichia coli with PBHoney and validate them via de novo assembly. PBHoney is available for download at http://sourceforge.net/projects/pb-jelly/. CONCLUSIONS: Implementing two variant-identification approaches that exploit the high mappability of long reads, PBHoney is demonstrated as being effective at detecting larger structural variants using whole-genome Pacific Biosciences RS II Continuous Long Reads. Furthermore, PBHoney is able to discover two genomic features: the existence of Rac-Phage in isolate; evidence of E. coli's circular genome.

BACKGROUND: Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results. RESULTS: To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts. CONCLUSIONS: By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples.