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Rao A.R.,Center for Agricultural Bioinformatics | Sharma A.P.,Central Inland Fisheries Research Institute
Indian Journal of Animal Sciences | Year: 2014

The demand for food proteins, including plant and animal proteins is increasing at an exponential rate. The demand for animal products will nearly be doubled by 2030. Thus, to improve livestock production and meet the animal protein demand, it is essential to go for application of interventions based on genomics, statistics and informatics. Such interventions are quite often used in the animal improvement programs to develop offspring with desirable traits. More recently, with the emergence of high throughput sequencing technologies, genomes of farm animals, fishes and model organisms were sequenced and the same are available in public domain. Also, with the advent of new silicon technologies, it has become possible to manage the generated data from genome sequencing projects. Now, the challenge lies with the analysis and interpretation of sequence data in a biologically meaningful manner, for which many algorithmic based analytical techniques and high performance computing methods were developed. Here, a brief review is presented on the application of various statistical and computational approaches used in genomic data analysis. Applications of the above mentioned approaches for health management and sustainable animal and fish production from the view point of vaccine and drug designing, disease risk management, epigenomics and whole genome level SNP/CNV associations with traits at are also discussed here. Besides, this paper allows the molecular biologists and other application scientists to analyze overwhelming amount of genomic data by different methods outlined here. Source


Chaturvedi K.K.,Center for Agricultural Bioinformatics | Bedi P.,University of Delhi | Misra S.,Covenant University | Singh V.B.,University of Delhi
Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013 | Year: 2013

With the increasing popularity of open source software, the changes in source code are inevitable. These changes in code are due to feature enhancement, new feature introduction and bug repair or fixed. It is important to note that these changes can be quantified by using entropy based measures. The pattern of bug fixing scenario with complexity of code change is responsible for the next release as these changes will cover the number of requirements and fixes. In this paper, we are proposing a method to predict the next release problem based on the complexity of code change and bugs fixed. We applied multiple linear regression to predict the time of the next release of the product and measured the performance using different residual statistics, goodness of fit curve and R2. We observed from the results of multiple linear regression that the predicted value of release time is fitting well with the observed value of number of months for the next release. © 2013 IEEE. Source


Mallikarjuna M.G.,Indian Agricultural Research Institute | Nepolean T.,Indian Agricultural Research Institute | Mittal S.,Indian Agricultural Research Institute | Hossain F.,Indian Agricultural Research Institute | And 10 more authors.
Indian Journal of Agricultural Sciences | Year: 2016

Iron (Fe) and Zinc (Zn) are the key elements required for many of the biological process in plants and animals. Transporter proteins are essential for uptake, transport and accumulation for Fe and Zn in plants. The present investigation was undertaken to study and compare the structural and functional diversity and evolutionary significance of the yellow stripe-like (YSL) transporters through in-silico tools in five species (barley, Brachypodium, foxtail millet, maize and rice) of Poaceae. One hundred and two YSL transporters collected from public databases were used in the analysis. All YSL transporters possessed PF03169 domain which belongs to the oligo peptide transporters (OPT) super family. Molecular weight of YSL proteins ranged from 11.10 to 84.70 kDa while pI values ranged from 4.99 to 11.64. Scondary structure analysis identified that, alpha helix and random coils were the most common structures of the YSL proteins. Phylogenetic analysis revealed that the YSL transporters are highly conserved in these five grass species. Comparative mapping of genes of YSL transporters showed maximum synteny between Brachypodium and barley (30%) followed by Brachypodium and rice (25%). Neutrality test has in fact revealed the positive or Darwinian selection on YSL transporters. The results of the present investigation provided a significant understanding of the structural and biological role of YSL transporters as well as the evolutionary pattern in Poaceae family. © 2016, Indian Council of Agricultural Research. All rights reserved. Source


Iquebal M.A.,Indian Agricultural Research Institute | Ghosh H.,Indian Agricultural Research Institute | Ghosh H.,Center for Agricultural Bioinformatics | Prajneshu,Indian Agricultural Research Institute
Indian Journal of Agricultural Sciences | Year: 2013

In this paper, utility of Genetic algorithm for fitting of SETAR three-regime model is highlighted. The proposed procedure is successfully applied for modelling and forecasting of Indian lac production data. It is hoped that, applied statisticians would also start employing Genetic algorithm for fitting other nonlinear time-series models. Source


Dash S.,Indian Agricultural Research Institute | Wahi S.D.,Indian Agricultural Research Institute | Rao A.R.,Indian Agricultural Research Institute | Rao A.R.,Center for Agricultural Bioinformatics
Indian Journal of Agricultural Sciences | Year: 2012

Seventy seven maize (Zea mays L.) genotypes collected from Annual progress report 2005-06 of All India Coordinated Maize Improvement Project, Directorate of Maize Research are classified by 6 different clustering methods including ANN and compared based on probability of misclassification. The percentage probability of misclassification for small, moderate and large sample sizes based on ANN method was 5.666, 5.417 and 4.534 respectively. The second best method for small sample size was Ward's method with 9.333 as percentage probability of misclassification. Whereas for moderate and large sample sizes K-means method was the second best method with 6.984 and 6.899 as percentage probability of misclassification. Hence, it can be concluded that the performance of ANN method is the best among the six methods of clustering irrespective of the sample size and dissimilarity measures used. Source

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