title: PGen: large-scale genomic variations analysis workflow and browser in SoyKB.
abstract: BACKGROUND
  With 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.
  
  
  RESULTS
  We 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.
  
  
  CONCLUSIONS
  PGen 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.
authors: Informatics Institute, University of Missouri, Columbia, MO, USA.|Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
authors: Yang
authors: Y
authors: Liu
authors: Informatics Institute, University of Missouri, Columbia, MO, USA.|Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
authors: Saad M
authors: SM
authors: Khan
authors: Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Department of Computer Science, University of Missouri, Columbia, MO, USA.
authors: Juexin
authors: J
authors: Wang
authors: Information Sciences Institute, University of Southern California, Los Angeles, CA, USA.
authors: Mats
authors: M
authors: Rynge
authors: Department of Computer Science, University of Missouri, Columbia, MO, USA.
authors: Yuanxun
authors: Y
authors: Zhang
authors: Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Department of Computer Science, University of Missouri, Columbia, MO, USA.
authors: Shuai
authors: S
authors: Zeng
authors: Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Department of Computer Science, University of Missouri, Columbia, MO, USA.
authors: Shiyuan
authors: S
authors: Chen
authors: Division of Plant Sciences, University of Missouri, Columbia, MO, USA.
authors: Joao V
authors: JV
authors: Maldonado Dos Santos
authors: Division of Plant Sciences, University of Missouri, Columbia, MO, USA.|National Center of Soybean Biotechnology, Columbia, MO, USA.
authors: Babu
authors: B
authors: Valliyodan
authors: Department of Computer Science, University of Missouri, Columbia, MO, USA.
authors: Prasad P
authors: PP
authors: Calyam
authors: iPlant Collaborative, University of Arizona, Tucson, AZ, USA.
authors: Nirav
authors: N
authors: Merchant
authors: Division of Plant Sciences, University of Missouri, Columbia, MO, USA.|National Center of Soybean Biotechnology, Columbia, MO, USA.
authors: Henry T
authors: HT
authors: Nguyen
authors: Informatics Institute, University of Missouri, Columbia, MO, USA.|Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Department of Computer Science, University of Missouri, Columbia, MO, USA.
authors: Dong
authors: D
authors: Xu
authors: Informatics Institute, University of Missouri, Columbia, MO, USA. joshitr@missouri.edu.|Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA. joshitr@missouri.edu.|Department of Computer Science, University of Missouri, Columbia, MO, USA. joshitr@missouri.edu.|Department of Molecular Microbiology and Immunology and Office of Research, School of Medicine, University of Missouri, Columbia, MO, USA. joshitr@missouri.edu.
authors: Trupti
authors: T
authors: Joshi
mesh_terms: D018745:Genome, Plant; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D011110:Polymorphism, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D013025:Glycine max / Q000235:genetics*; D057188:Workflow