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PGen: large-scale genomic variations analysis workflow and browser in SoyKB.

Yang Liu1,2, Saad M Khan1,2, Juexin Wang2,3

  • 1Informatics Institute, University of Missouri, Columbia, MO, USA.

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Summary

The PGen workflow efficiently analyzes next-generation sequencing data for crop germplasm, identifying millions of genetic variations like single nucleotide polymorphisms (SNPs) and indels to improve traits.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Advances in next-generation sequencing (NGS) and reduced costs enable large-scale crop germplasm analysis for genetic variation detection.
  • Efficient analysis of genomic variations is crucial for crop trait improvement.

Purpose of the Study:

  • To develop an integrated and optimized workflow, PGen, for large-scale NGS resequencing data analysis.
  • To facilitate user-friendly identification of single nucleotide polymorphisms (SNPs), insertion-deletions (indels), and copy number variations (CNVs).

Main Methods:

  • Developed PGen workflow integrating XSEDE HPC, iPlant cloud storage, and Pegasus workflow management system (Pegasus-WMS).
  • Implemented PGen as a Linux version on GitHub and a web-based tool within the Soybean Knowledge Base (SoyKB).

Main Results:

  • Identified over 10.2 million SNPs and 1.3 million indels in 106 soybean lines.
  • Discovered 297,245 non-synonymous SNPs and 3330 CNV regions.
  • Integrated SNP data from over 500 soybean germplasm lines for trait improvement via genotype-to-phenotype prediction.

Conclusions:

  • PGen workflow is optimized for efficient soybean data analysis, serving as a model for genomics data workflow development.
  • The workflow integrates remote HPC resources, data management, and user-friendliness.
  • PGen is adaptable for genomic variation analysis in other plant species.