ArticleBMC bioinformatics2016
PGen: large-scale genomic variations analysis workflow and browser in SoyKB.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Domain-specific Topic Model for Knowledge Discovery in Computational and Data-Intensive Scientific Communities.IEEE transactions on knowledge and data engineering · 2023Article
- Identification of loci associated with water use efficiency and symbiotic nitrogen fixation in soybean.Frontiers in plant science · 2023Article
- Genomic Variations Explorer (GenVarX): a toolset for annotating promoter and CNV regions using genotypic and phenotypic differences.Frontiers in genetics · 2023Article
- A novel Synthetic phenotype association study approach reveals the landscape of association for genomic variants and phenotypes.Journal of advanced research · 2022Article
- Linkage analysis and residual heterozygotes derived near isogenic lines reveals a novel protein quantitative trait loci from aFrontiers in plant science · 2022Article
- Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing.Scientific data · 2021Article
- Article
- Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries.BMC genomics · 2019Article
- Proceedings of the 2016 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.BMC bioinformatics · 2016Article
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Authors and funding
14 authors.
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Abstract
backgroundWith the advances in next-generation sequencing (NGS) technology and significant reductions in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations and to apply the knowledge towards improvements in traits. To efficiently facilitate large-scale NGS resequencing data analysis of genomic variations, we have developed "PGen", an integrated and optimized workflow using the Extreme Science and Engineering Discovery Environment (XSEDE) high-performance computing (HPC) virtual system, iPlant cloud data storage resources and Pegasus workflow management system (Pegasus-WMS). The workflow allows users to identify single nucleotide polymorphisms (SNPs) and insertion-deletions (indels), perform SNP annotations and conduct copy number variation analyses on multiple resequencing datasets in a user-friendly and seamless way.
resultsWe have developed both a Linux version in GitHub ( https://github.com/pegasus-isi/PGen-GenomicVariations-Workflow ) and a web-based implementation of the PGen workflow integrated within the Soybean Knowledge Base (SoyKB), ( http://soykb.org/Pegasus/index.php ). Using PGen, we identified 10,218,140 single-nucleotide polymorphisms (SNPs) and 1,398,982 indels from analysis of 106 soybean lines sequenced at 15X coverage. 297,245 non-synonymous SNPs and 3330 copy number variation (CNV) regions were identified from this analysis. SNPs identified using PGen from additional soybean resequencing projects adding to 500+ soybean germplasm lines in total have been integrated. These SNPs are being utilized for trait improvement using genotype to phenotype prediction approaches developed in-house. In order to browse and access NGS data easily, we have also developed an NGS resequencing data browser ( http://soykb.org/NGS_Resequence/NGS_index.php ) within SoyKB to provide easy access to SNP and downstream analysis results for soybean researchers.
conclusionPGen workflow has been optimized for the most efficient analysis of soybean data using thorough testing and validation. This research serves as an example of best practices for development of genomics data analysis workflows by integrating remote HPC resources and efficient data management with ease of use for biological users. PGen workflow can also be easily customized for analysis of data in other species.
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