IFPRI- NAIP - Computational Framework to Support Biotechnological Research

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National Agricultural Innovation Project (NAIP), ICAR and the International Food Policy Research Institute (IFPRI) organized a two day workshop on ‘Impact of capacity building programs under NAIP’ on June 6-7, 2014 at AP Shinde Auditorium, NASC Complex, Pusa, New Delhi. The main purpose of the workshop was to present and discuss the findings of the impact evaluation study on capacity building programs under NAIP by IFPRI. The scientists from ICAR and agricultural universities were sent abroad to receive training in specialized research techniques. Post-training, scientists were expected to work on collaborative projects within the ICAR, which would further enrich their knowledge and skills, expand their research network and stimulate them’ to improve their productivity, creativity and quality of their research. The ICAR commissioned with IFPRI (International Food Policy Research Institute) to undertake an evaluation of these capacity building programs under NAIP in July 2012. The workshop shared the findings on the impact of capacity building programs under NAIP and evolve strategies for future capacity building programs

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  • 1. Centre for Agricultural Bioinformatics Computational Framework to Support Biotechnological Research Indian Agricultural Statistics Research Institute New Delhi-110012.
  • 2. CABIN To undertake research, teaching and training in the field of Computational Biology and Agricultural Bioinformatics MANDATE
  • 3. MAJOR ACHIEVEMENTS Long Term Impact:  Started M.Sc. (Bioinformatics) since 2011 through P.G. School IARI New Delhi  Starting Ph.D. ( Bioinformatics) through P.G. School IARI New Delhi from 2014-15  Establishment of Centre for Agricultural Bioinformatics  National Facility of Computational Biology  Initiation of number of inter-institutional projects in agricultural bioinformatics/computational biology Short Term Impact  Research Studies undertaken - 32  Database/Tools developed - 31  Training/Workshops/meetings organized - 65  Publications -57
  • 4. International Consortia Training • Capacity building In important (Agricultural Bioinformatics and Computational Biology) • Provides international experience/exposure • May accelerate research in agricultural biotechnology • Motivates scientists/ researchers • Leads to understand the complex biological process in agriculture • Helps in development of superior varieties/breeds/product • Increases productivity and production Strength
  • 5. Strength • It provides opportunity to learn new things • Provides international outlook • Build international collaborations • Open new horizon of opportunities
  • 6. Weakness • Selection of training organization is based on personal approach • Training period is too short to learn new techniques/ tools • Many times training may not be very appropriate for the consortia research • Lack of interest of training supervisor. • Some times it deviates scientists/ researchers from their own research field. • Many times training areas are not absorbed appropriately in institutions after end of consortia.
  • 7. Need • All international collaborations including training should be governed by a national policy • Selection of training organization should be at the council level based on capability of training organization. • Identification of the field of training should be based on pre-set national priorities. • All trained manpower in a particular field should effectively used to solve national problem • Only scientists/researchers working in the relevant fields should be send for training
  • 8. CABIN IMPORTANT RESEARCH PUBLICATIONS • Sarika, Vasu Arora, M A. Iquebal, Anil Rai, and Dinesh Kumar (2013). In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB. BMC Genomics, 14, 43. • Iquebal MA, Sarika, Arora Vasu, Verma Nidhi, Rai Anil and Kumar Dinesh (2013): First whole genome based microsatellite DNA marker database of tomato for mapping and variety identification. BMC Plant Biology 2013, 13:197 (http://www.biomedcentral.com/1471-2229/13/197/abstract). • Iquebal MA, Sarika, Dhanda, SK, Arora V, Dixit, SP, Raghava GPS, Rai A and Kumar D (2013). Development of a model webserver for breed identification using microsatellite DNA marker. BMC Genetics 2013, 14:118. • Anu Sharma, Cini Varghese and Seema Jaggi (2013). WS-PBIBD - A Web Solution for Partially Balanced Incomplete Block Experimental Designs. Computer and Electronics in Agriculture, Volume 99, pp 132 – 134. • Bhati Jyotika, Chaduvula Pavan K, Kumar Sanjeev and Rai Anil (2013). Phylogenetic analysis and secondary structure prediction for drought tolerant Capbinding proteins of plant species. Indian Journal of Agricultural Sciences, 83 (1): 21–5, 2013. • Iquebal MA, Ghosh H and Prajneshu (2013). Fitting of SETAR Three-regime nonlinear time series model to Indian lac production data through genetic algorithm. Indian Journal of Agricultural Sciences, 83 (12), 130-132. • Avantika Singh, Garima Vats, Nidhi Chandra, Monendra Grover (2013). Sumoylation may play an important role in modification of large number of proteins associated with heat stress in plants. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences. DOI:10.1007/s40011-013-0249-8. Contd…..
  • 9. CABIN• RK Sanjukta, Md. Samir Farooqi, Niyati Rai, Anil Rai, Naveen Sharma, Dwijesh C Mishra and Dhananjaya P Singh (2013). Expression analysis of genes responsible for amino acid biosynthesis in extremely halophilic bacterium, Salinibacter ruber, Indian Journal of Biochemistry and Biophysics Vol 50, June. 2013, pp. 177-185. • Satyavathi, C.T., Tiwari, S., Bharadwaj,C., Rao, A.R., Bhat, J. and Singh, S.P. (2013). Genetic Diversity Analysis in a Novel Set of Restorer Lines of Pearl Millet [Pennisetum glaucum (L.) R. Br] Using SSR Markers. Vegetos, 26(1), 72-82. • Iquebal, M. A. and Sarika (2013). Nonlinear growth models for describing country’s Lentil (Lens culinaris M.) production. Journal of Food Legumes, 26 (1&2), 79-82. • Goyal, P., Chattopadhyay, C., Mathur, A.P., Kumar, A., Meena, P.D., Datta, S. and Iquebal, M.A. (2013). Pathogenic and molecular variability among different oilseed Brassica isolates of Alternaria brassicae from India. Annals of Plant Protection Sciences, 21(2): 349-359. • Sundeep Kumar , Prerna Kumari , Uttam Kumar , Monendra Grover, Amit Kumar Singh , Rakesh Singh , R. S. Sengar (2013) Molecular approaches for designing heat tolerant wheat. J. Plant Biochem. Biotechnology, NAAS rating :6.41, DOI 10.1007/s13562-013-0229-3. • Goyal, P., Kumar, A., Chahar, M., Iquebal, M.A., Datta, S. and Chattopadhyay, C. (2013). Pathogenic and genetic variability among different oilseed Brassica isolates of Sclerotinia sclerotiorum from India and UK. Annals of Plant Protection Sciences, 21(2): 377-386. Contd…..
  • 10. CABIN• Sudhir Srivastava, Cini Varghese, Seema Jaggi and Eldho Varghese (2013). Diallel cross designs for test versus control comparisons. The Indian Journal of Genetics and Plant Breeding, 73(2), 186-193. • Nanda D.K., Singh R., Tomar S.K., Dash S.K., Jayakumar S., Arora D.K., Chaudhary R. & Kumar D. (2013). Indian Chilika curd- A potential dairy product for Geographical Indication registration. Indian Journal of Traditional Knowledge. Vol. 12 (4), October 2013. • Dubey P.P., Sharma A., Gour D.S., Prashant, Jain A., Mukhopadhyay C.S., Singh A. & Kumar D. (2013). Sequencing, single nucleotide polymorphisms identification and development of genotyping tests for leptin gene in zebu cattle (Bos indicus). Indian Journal of Animal Sciences 83 (6): 61–00, June 2013. • Kumar, V., Singh, K.H., Chaturvedi, K.K., Nanjundan, J. (2013). Enhancing access to information on rapeseed-mustard germplasm by implementing of Web-based database using LAMP Technology. African Journal of Agricultural Research. 8(11). pp. 2733-2743. DOI: 10.5897/AJAR2013.6945. • Eldho Varghese, Seema Jaggi and Sarika (2013). Response surface model with neighbor effects and correlated observations. Model Assisted Statistics and Applications, 8(1): 41-49. • Singh, V.B. and Chaturvedi, K.K. (2013). “Improving the Quality of Software by Quantifying the Code Change Metric and Predicting the Bugs”. Lecture Notes in Computer Science (LNCS). Vol. 7972, pp. 408–426, 2013. © Springer-Verlag, Berlin Heidelberg. • 'kf'k nfg;k] vkj-lh xks;y] lhek tXxh] ds-ds- prqosZnh] va'kq Hkkj}kt] fluh ojxhl ,oa Å"kk tSu ¼2012&13½. Ñf"k f'k{kk gsrq bZ&yfuZax Ik)fr dk fodklA Lkkaf[;dh&foe'kZ ¼63&66½ . • G. T. Patle, D. K. Singh, A. SarangiI, Anil Rai, Manoj Khanna and R N Sahoo (2013). Temporal variability of climatic parameters and potential evapotranspiration. Indian Journal of Agricultural Science, 83 (5): 518–24, May 2013. Contd…..
  • 11. CABIN • Priyamedha, Singh V. V., Chauhan J. S., Meena M. L., Mishra D. C. (2013). Correlation and path coefficient analysis for yield and yield components in early generation lines of Indian Mustard (Brassica juncea L.)”, Current Advances in Agricultural Sciences, 5(1): 37-40. • Chauhan J. S., Singh K. H., Mishra D. C. (2013). AMMI and bi-plot analyses to identify stable genotypes of Indian Mustard (Brassica juncea L.) for oil and seed meal quality characters, SABRAO Journal of Breeding and Genetics, 45 (2) 195-202, 2. • Sharma Anu, Lal S. B., Mishra D. C., Srivastava Sudhir and Rai Anil (2013): A web Based Software for Synonymous Codon Usage Indices. International Journal of Information and Computation Technology, Vol. 3 No. 3 pp 147-152. • Charanjit Kaur, Shweta Nagal, Jyoti Nishad, Ravinder Kumar and Sarika (2013). Evaluating eggplant (Solanum melongena L) genotypes for bioactive properties: A chemometric approach. Food Research International (In press; Available online at http://authors.elsevier.com/sd/article/S096399691300536X). • Iquebal, M.A, Sarika, Arora, Vasu, Rai, Anil, and Kumar, Dinesh (2013). In silico mining of microsatellite markers from goat whole genome and development of GOSATDB. Indian Journal of Animal Research.  Farooqi, M.S., Sanjukta, R.K., Sharma, N., Rai, A., Mishra, D.C., Singh, D.P., Chaturvedi, K.K., Kumar, A., Panwar, S., (2013). Statistical and computational methods for detection of synonymous codon usage patterns and gene expression. International Journal of Agricultural and Statistical Sciences, 9(1): 303-310.  Gautam, N. K., Iquebal, M. A., Singh, M., Akhtar, J., Khan, Z. and Ram, B. (2014). Genetic diversity analysis for quantitative traits in Lentil (Lens culinaris Medik.) germplasm. Legume Research-An International Journal, 37(2), 133-138. Contd…..
  • 12.  Iquebal MA, Sarika, Dhanda, SK, Arora V, Dixit, SP, Raghava GPS, Rai A and Kumar D (2013). Development of a model webserver for breed identification using microsatellite DNA marker. BMC Genetics 2013, 14:118. (http://www.biomedcentral.com/1471-2156/14/118/abstract).  Kumar, M., Ahmad, T., Rai, A. and Sahoo, P.M. (2013) Methodology for Construction of Composite Index. International Journal of Agricultural and Statistical Sciences, 9(2), 639-647.  V. Keshri, Dhananjaya P. Singh, R. Prabha, A. Rai, A. K. Sharma (2014) . Genome subtraction for the identification of potential antimicrobial targets in Xanthomonas oryzae pv. oryzae PXO99A pathogenic to rice, 3 Biotech, 4:91–95, DOI 10.1007/s13205-013-0131-7.  Meher, P.K., Sahu, T.K., Rao, A.R. and Wahi, S.D. (2014). Application of Gibbs sampling methodology for identification of transcription factor binding sites in MADS box family genes in Arabidopsis thaliana. Ind. J. Genet., 74(1), 73-80.  Sarika, Iquebal, M.A., Rai, Anil and Anshika (2013). Support vector machine for prediction of antimicrobial peptides in legumes. International Journal of Agricultural & Statistical Sciences, 9 (2), 717-728.  Sarika, Jaggi Seema and Sharma VK (2013). First order rotatable designs incorporating neighbor effects. ARS Combinatoria. 112, 145-159.
  • 13. THANKS