@article {890, title = {Accurate, scalable cohort variant calls using DeepVariant and GLnexus.}, journal = {Bioinformatics}, year = {2021}, month = {2021 Jan 05}, abstract = {

MOTIVATION: Population-scale sequenced cohorts are foundational resources for genetic analyses, but processing raw reads into analysis-ready cohort-level variants remains challenging.

RESULTS: We introduce an open-source cohort-calling method that uses the highly-accurate caller DeepVariant and scalable merging tool GLnexus. Using callset quality metrics based on variant recall and precision in benchmark samples and Mendelian consistency in father-mother-child trios, we optimized the method across a range of cohort sizes, sequencing methods, and sequencing depths. The resulting callsets show consistent quality improvements over those generated using existing best practices with reduced cost. We further evaluate our pipeline in the deeply sequenced 1000 Genomes Project (1KGP) samples and show superior callset quality metrics and imputation reference panel performance compared to an independently-generated GATK Best Practices pipeline.

AVAILABILITY AND IMPLEMENTATION: We publicly release the 1KGP individual-level variant calls and cohort callset (https://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP) to foster additional development and evaluation of cohort merging methods as well as broad studies of genetic variation. Both DeepVariant (https://github.com/google/deepvariant) and GLnexus (https://github.com/dnanexus-rnd/GLnexus) are open-sourced, and the optimized GLnexus setup discovered in this study is also integrated into GLnexus public releases v1.2.2 and later.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btaa1081}, author = {Yun, Taedong and Li, Helen and Chang, Pi-Chuan and Lin, Michael F and Carroll, Andrew and McLean, Cory Y} } @article {847, title = {Chromosome-scale, haplotype-resolved assembly of human genomes.}, journal = {Nat Biotechnol}, volume = {39}, year = {2021}, month = {2021 03}, pages = {309-312}, abstract = {

Haplotype-resolved or phased genome assembly provides a complete picture of genomes and their complex genetic variations. However, current algorithms for phased assembly either do not generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method named diploid assembly (DipAsm) that uses long, accurate reads and long-range conformation data for single individuals to generate a chromosome-scale phased assembly within 1 day. Applied to four public human genomes, PGP1, HG002, NA12878 and HG00733, DipAsm produced haplotype-resolved assemblies with minimum contig length needed to cover 50\% of the known genome (NG50) up to 25 Mb and phased ~99.5\% of heterozygous sites at 98-99\% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies for the discovery of structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as the human leukocyte antigen (HLA) and killer cell immunoglobulin-like receptor (KIR) regions. DipAsm will facilitate high-quality precision medicine and studies of individual haplotype variation and population diversity.

}, keywords = {Algorithms, Chromosomes, Human, Genome, Human, Haplotypes, Heterozygote, Humans, Polymorphism, Single Nucleotide}, issn = {1546-1696}, doi = {10.1038/s41587-020-0711-0}, author = {Garg, Shilpa and Fungtammasan, Arkarachai and Carroll, Andrew and Chou, Mike and Schmitt, Anthony and Zhou, Xiang and Mac, Stephen and Peluso, Paul and Hatas, Emily and Ghurye, Jay and Maguire, Jared and Mahmoud, Medhat and Cheng, Haoyu and Heller, David and Zook, Justin M and Moemke, Tobias and Marschall, Tobias and Sedlazeck, Fritz J and Aach, John and Chin, Chen-Shan and Church, George M and Li, Heng} } @article {867, title = {Parliament2: Accurate structural variant calling at scale.}, journal = {Gigascience}, volume = {9}, year = {2020}, month = {2020 12 21}, abstract = {

BACKGROUND: Structural variants (SVs) are critical contributors to genetic diversity and genomic disease. To predict the phenotypic impact of SVs, there is a need for better estimates of both the occurrence and frequency of SVs, preferably from large, ethnically diverse cohorts. Thus, the current standard approach requires the use of short paired-end reads, which remain challenging to detect, especially at the scale of hundreds to thousands of samples.

FINDINGS: We present Parliament2, a consensus SV framework that leverages multiple best-in-class methods to identify high-quality SVs from short-read DNA sequence data at scale. Parliament2 incorporates pre-installed SV callers that are optimized for efficient execution in parallel to reduce the overall runtime and costs. We demonstrate the accuracy of Parliament2 when applied to data from NovaSeq and HiSeq X platforms with the Genome in a Bottle (GIAB) SV call set across all size classes. The reported quality score per SV is calibrated across different SV types and size classes. Parliament2 has the highest F1 score (74.27\%) measured across the independent gold standard from GIAB. We illustrate the compute performance by processing all 1000 Genomes samples (2,691 samples) in <1 day on GRCH38. Parliament2 improves the runtime performance of individual methods and is open source (https://github.com/slzarate/parliament2), and a Docker image, as well as a WDL implementation, is available.

CONCLUSION: Parliament2 provides both a highly accurate single-sample SV call set from short-read DNA sequence data and enables cost-efficient application over cloud or cluster environments, processing thousands of samples.

}, issn = {2047-217X}, doi = {10.1093/gigascience/giaa145}, author = {Zarate, Samantha and Carroll, Andrew and Mahmoud, Medhat and Krasheninina, Olga and Jun, Goo and Salerno, William J and Schatz, Michael C and Boerwinkle, Eric and Gibbs, Richard A and Sedlazeck, Fritz J} } @article {177, title = {Assessing structural variation in a personal genome-towards a human reference diploid genome.}, journal = {BMC Genomics}, volume = {16}, year = {2015}, month = {2015 Apr 11}, pages = {286}, abstract = {

BACKGROUND: Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods.

RESULTS: We demonstrate Parliament{\textquoteright}s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8\%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus.

CONCLUSIONS: HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

}, keywords = {Computational Biology, Databases, Genetic, Diploidy, Genome, Human, Genomic Structural Variation, Humans, Sequence Analysis, DNA, Software}, issn = {1471-2164}, doi = {10.1186/s12864-015-1479-3}, author = {English, Adam C and Salerno, William J and Hampton, Oliver A and Gonzaga-Jauregui, Claudia and Ambreth, Shruthi and Ritter, Deborah I and Beck, Christine R and Davis, Caleb F and Dahdouli, Mahmoud and Ma, Singer and Carroll, Andrew and Veeraraghavan, Narayanan and Bruestle, Jeremy and Drees, Becky and Hastie, Alex and Lam, Ernest T and White, Simon and Mishra, Pamela and Wang, Min and Han, Yi and Zhang, Feng and Stankiewicz, Pawel and Wheeler, David A and Reid, Jeffrey G and Muzny, Donna M and Rogers, Jeffrey and Sabo, Aniko and Worley, Kim C and Lupski, James R and Boerwinkle, Eric and Gibbs, Richard A} } @article {270, title = {Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline.}, journal = {BMC Bioinformatics}, volume = {15}, year = {2014}, month = {2014 Jan 29}, pages = {30}, abstract = {

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.

}, keywords = {Genome, Genomics, High-Throughput Nucleotide Sequencing, Humans, Internet, Software}, issn = {1471-2105}, doi = {10.1186/1471-2105-15-30}, author = {Reid, Jeffrey G and Carroll, Andrew and Veeraraghavan, Narayanan and Dahdouli, Mahmoud and Sundquist, Andreas and English, Adam and Bainbridge, Matthew and White, Simon and Salerno, William and Buhay, Christian and Yu, Fuli and Muzny, Donna and Daly, Richard and Duyk, Geoff and Gibbs, Richard A and Boerwinkle, Eric} }