Research Program Genetic Gains (RPGG) Review Meeting 2021: Computational Genomics By Mr Prasad Bajaj and Team

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Computational Genomics : From Genome to Function - Works under the umbrella of Sequencing and Informatics Unit (SISU) and CEGSB, Analysis of genomics data in collaboration with different scientists and researchers in RP-Genetic Gains, Provide computational and genomics data analysis support for day to day activities, Ensuring smooth functioning and maintenance of computational infrastructure

Government & Nonprofit

Computational
Genomics
Team Members
• Aamir W. Khan
• Vinod Kumar Valluri
Prasad Bajaj
Manager – Computational Biology

Computational Genomics : From Genome to Function
Wisdom
Knowledge
Information
Data
• Works under the umbrella of Sequencing and
Informatics Unit (SISU) and CEGSB
• Analysis of genomics data in collaboration with
different scientists and researchers in RP-
Genetic Gains
• Provide computational and genomics data
analysis support for day to day activities
• Ensuring smooth functioning and maintenance
of computational infrastructure
Applied
Context
Meaning
Raw

Genotypingandsequencingplatforms
WGRS GBS
RNA-Seq
Genome Assembly
Epigenomics
Microarrays
High Performance Computational Genome Analysis facility
 600 cores
 ~ 830 TB storage
 7.5 TB RAM
What we do : Omics Data Analysis

Genome analysis
• Genome assembly
• Variation/SNP discovery
• Functional annotations
• Population genomics
• Haplotype identification
• QTL-Seq
Chickpea 3000 Genome Sequencing
Initiative

RNA-Seq/Transcriptome Studies
• Expression atlas/profiles
• De novo transcript reconstruction
• Gene annotations
• Pathway mapping
• Spatiotemporal expression
• DEGs

Multiomics studies
• Proteome analysis
• Non-coding RNA
• Bisulphite sequencing
• Degradome sequencing
• Transcriptome & proteome correlation

Data Resources
A brief summary of research
data made available in public
domain like NCBI
“Open access to research information related to agriculture in the drylands is critical as it has the power to
promote greater distribution of knowledge and enhances potential for innovation with greater influence on
science”

• Friendly interface
• Detailed results
• Advance visualization
Data Resources
• Prevent data duplication
• For scientific community’s inspection
• Cross verification

Challenges
• Lack of funding and support
• No funds from CRP-GLDC and similar other initiatives though
we provide core services
• Resources are challenged
• Lack of financial support to maintain the infrastructure to carryout
analysis
• Employee turnover
• Employees and team members leaving because for better raise
and growth opportunities outside at other organisations

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