LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

A fully automated pipeline for quantitative genotype calling from next generation sequencing data in autopolyploids

Photo from wikipedia

BackgroundGenotyping-by-sequencing (GBS) has been used broadly in genetic studies for several species, especially those with agricultural importance. However, its use is still limited in autopolyploid species because genotype calling software… Click to show full abstract

BackgroundGenotyping-by-sequencing (GBS) has been used broadly in genetic studies for several species, especially those with agricultural importance. However, its use is still limited in autopolyploid species because genotype calling software generally fails to properly distinguish heterozygous classes based on allele dosage.ResultsVCF2SM is a Python script that integrates sequencing depth information of polymorphisms in variant call format (VCF) files and SuperMASSA software for quantitative genotype calling. VCFs can be obtained from any variant discovery software that outputs exact allele sequencing depth, such as a modified version of the Tassel-GBS pipeline provided here. VCF2SM was successfully applied in analyzing GBS data from diverse panels (alfalfa and potato) and full-sib mapping populations (alfalfa and switchgrass) of polyploid species.ConclusionsWe demonstrate that our approach can help plant geneticists working with autopolyploid species to advance their studies by distinguishing allele dosage from GBS data.

Keywords: genotype calling; automated pipeline; fully automated; quantitative genotype

Journal Title: BMC Bioinformatics
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.