Charles Johnson, founder of the Texas A&M AgriLife Genomics and Bioinformatics Service, addressed the current status and future directions for massively high-throughput genomics for plant and animal breeding and research.
A major drawback to sequencing-based agriculture studies is the cost. Arrays and reduced representation sequencing methods are common alternatives for genotyping, but each of these methods has significant limitations associated with it.
In this webinar, Charles Johnson shared how his team developed AgSeq, to address these shortcomings.
AgSeq is a novel agriculture-focused genotyping pipeline that uses optimized laboratory processing, massive sample multiplexing, and machine learning to obtain highly accurate genotype information from low-coverage sequencing data. The reduced cost of whole-genome sequencing afforded by AgSeq allows for a substantial increase in individuals genotyped per study. AgSeq is powered by optimized library prep, automation, and high-throughput sequencing coupled with a reduction in the amount of data needed per individual. Data from individual samples is used to accurately impute gaps resulting from reduced coverage, allowing for accurate genotyping of large populations for plant and animal studies.
Charles Johnson is director and founder of the Texas A&M AgriLife Genomics and Bioinformatics Service (TxGen), a multimillion-dollar research unit within Texas A&M AgriLife Research, part of the Texas A&M University System. The center conducts next-generation sequencing and bioinformatics research with collaborators in 36 countries. Over the last 24 months, TxGen has worked with more than 300 different groups from 96 universities and companies, resulting in more than 200 different species sequenced (not counting metagenomic projects).
Dr. Johnson received his PhD from Texas A&M University and has worked for more than two decades in genomics and bioinformatics research, both in the biotech industry and academia, and is responsible for the creation of six successful and increasingly complex research organizations.