Reflections of Researchers on Capacity Building Programs

International Open Training

A. R. Rao

Centre for Agricultural Bioinformatics

Indian Agricultural Statistics Research Institute,

New Delhi

Workshop on Impact of Capacity Building Programs under NAIP

NASC, Pusa, New Delhi

June 6-7, 2014

General Information

• Name : A.R.Rao

• Organization : IASRI

• Duration : 24-02-11 to 23-05-11

• Area of training : Bioinformatics

• Place of training : Univ. of Washington, Seattle

• Expenditure : Rs.5,01,836

Skill Gaps

• Institutional: bridge the gap between genomic information

and knowledge by utilizing statistical and computational

sciences (Bioinformatics)

• Individual: Infer knowledge from quantitative analysis of

genome sequence data by application of modern and

algorithmic approaches

Capacity building, Work Culture & Attitudinal Changes

• Genome Prediction and its application in (i) Disease risk

prediction (ii) prediction of breeding values or trait performance

from SNP genotyping data

• Methodology: Least Absolute Shrinkage Selection Operator

(LASSO) – Penalized Regression Method; K-fold cross validation

• Advanced methods – Machine Learning Approaches: Random

Forest

Capacity building, Work Culture & Attitudinal Changes

• Share resources and data (avoid undertakings)

• Interact more with biologists and application scientists

• Listen to webinars and understand language of others

• Give importance to others’ time

• Increase transparency in work

• Constant monitoring of self and other staff engaged in

projects

Capacity building, Work Culture & Attitudinal Changes

• listen carefully, ask to repeat if not followed

• think and talk precise, speak with clarity and facts

• Commit only to the extent which can be delivered in time

• Plan before start of work

• Do not hesitate to ask questions like “why? and what?”

Planning, Selection & Training duration

• Well planned by the funding agency in terms of

announcement, format of application and timely processing

of proposals

• Highly satisfied with the selection process

• For effective training – duration would have been 6 months

(duration depends on the requirement of the host institute,

sponsoring institute and interest of the candidate)

Resource Institution and Resource Person

• University of Washington, Seattle

• Bruce Weir, Professor & Chair, BiostatisticsDepartment, Adjunct Professor, GenomeSciences, University of Washington, Seattle, USA

• Research Interests: Statistical methodology forgenetic data, population structure, diseaseassociations and relationships, use of geneticdata for human identification

Training Implementation and Reporting

• Introduction and topic assignment

• Review of literature and understanding of topic

• Understanding UW’s High Performance Computing

Environment

• Getting access to the system, raw data and refined data

• Understanding data kept in Network Common Data Form

(NetCDF) – Reading through notes and manuals – discussion

through emails – rare in-person discussions

Training Implementation and Reporting

• Samples genotyped on the Illumina 1M beadchip

• Understanding of methodology

• Writing perl and R scripts for parsing and analysis of data,debugging and final execution of programmes

• Submission of jobs in queue using HPC cluster

• Processing of results and drawing inference

• Discussion with fellow colleagues

• Attending seminars, webinars, meetings; Participation in audio

and video conferencing

• Reading training manuals of Summer Institutes; Report Writing

Post-Training Utilization

Research

• Developed a new approach for mini-core identification andillustrated it by using data generated under Bioprospecting ofgenes and allele mining for abiotic stress tolerance project

• Compared the performance of LASSO with other methodsavailable for classification and prediction purposes

• Guided 1 Ph.D. and 1 M.Sc. Students in the trained area

• Tried on SNP-genotyping data of maize generated under BAMproject

• Applied on data, where the number of variables (p) are largerthan number of observations (n), particularly in case-controlstudies

Post-Training Utilization

Training

• Recent advances in statistical and computational genomicsdata analysis (19th - 28th March, 2012)

• ICAR-Winter School on Recent advances in quantitativegenetics and statistical genomics during (06-26th November,2012)

• Advanced Analytical Techniques in Bioinformatics (10-19th

March, 2014)

• Several lectures were delivered on Genome Prediction invarious trainings organized under NARS.

Future Plan

• Interactions will be held with molecular biologists, plant andanimal breeders interested in Genomic Prediction. To play asignificant role in the upcoming network platforms.

• Application of penalized regression methods for prediction ofeconomically important traits like milk yield, body mass index,lactation length, calving interval, etc.

• Application of Lasso and elasticnet methods in disease riskprediction in plant, animal and fish species.

• Application of Random Forest methodology and algorithmicbased machine learning approaches for genome prediction

• High Performance Cluster management in NationalAgricultural Bioinformatics Grid

Acknowledgements

• Director, IASRI

• ICAR, NAIP, World Bank, ND, NCs and Staff at NAIP Office

• Prof. Bruce S. Weir, Prof. & Chair, Biostatistics Department,University of Washington, Seattle

• David Levein, Stephine Groton, Sarah Nelson, David Crosslin,Rui Zhang, Rohit Swarnkar at GCC

• Director, NAARM