Cinque Terre

Renesh Bedre, Ph.D.

Bioinformatics data scientist

Texas A&M University

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Research goals

Research interests

Appointments and education

Research areas

Biography

Dr. Renesh Bedre is a Postdoctoral Research Associate at Texas A&M AgriLife Research. He received his Ph.D. from Louisiana State University, where he studied and applied genomics and bioinformatics approaches to understand the genetics of model and non-model plants associated with diseases and abiotic stresses.

At Texas A&M AgriLife Research, he continued his work on high-throughput genomics analysis and developed new computational methods to understand the complex genetic traits, gene networks, pathways, and basal molecular mechanisms for developing next-generation crops.

In his research, he primarily employs bioinformatics, statistical, and machine learning methods to exploit the large-scale sequencing datasets and interpret meaningful biological information.

Besides, he developed scalable software/tools, automated analysis pipeline, statistical models, and integrated databases for effective analysis and visualization of massive omics datasets.

From his research output, Dr. Bedre bridges the gap between high-throughput plant genetics data and actionable biological insights for experimental biologists.

My research goal is to analyze high-throughput genetics data and develop new computational methods to understand the molecular mechanisms for crop improvement to increase yield and stress resistance. I accomplish these goals by:

  • Apply bioinformatics, statistical, and machine learning methods on big high-throughput omics datasets to exploit and interpret meaningful biological information
  • Develop scalable software/tool, automated analysis pipeline, statistical models, and integrated databases for effective analysis and visualization of big high-throughput omics datasets
  • Work closely with biologists and multi-disciplinary collaborators to help to quickly understand the biological data
  • Bioinformatics, genetics, genomics, and transcriptomics
  • Statistical modeling and machine learning
  • Standalone software programs, web servers, and databases development
  • Development of new computational methodologies and automated analysis pipelines for big data
  • High-performance computing (HPC)
  • Visualization
  • Python, R/Bioconductor, Perl, PostgreSQL, Linux, HPC, CyVerse, Docker

Postdoctoral Research Associate, Oct 2016-Present
Texas A&M AgriLife Research, TX, USA

Ph.D. in Computational Biology and Bioinformatics, Aug 2011- Aug 2016
Louisiana State University, baton Rouge, LA, USA

M.S. in Bioinformatics, 2009-2011
Indian Institute of Information Technology, Allahabad, UP, India

Computational biology, Bioinformatics, Genomics, Transcriptomics, Gene expression, GBS, GWAS, high-throughput data analysis and interpretation, Statistics, phylogenetics, genotyping, biomarkers, Functional annotation, Population genomics, Gene co-expression network, RNA-seq, RAD-seq, AgSeq, Genetics, Software and database development, data integration, Cloud-based computing, GitHub

Please, read CV for research experience, awards, and achievements