Statistical and bio-computational applications in animal sciences

A R RAO; MANOSWINI DASH; TANMAYA KUMAR SAHU; S D WAHI; B K BEHERA; A P SHARMA; V K BHATIA

doi:10.56093/ijans.v84i5.40641

Authors

A R RAO Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
MANOSWINI DASH Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
TANMAYA KUMAR SAHU Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
S D WAHI Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
B K BEHERA Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
A P SHARMA Central Inland Fisheries Research Institute, Barrackpore, West Bengal
V K BHATIA Indian Agricultural Statistics Research Institute, New Delhi 110 012 India

https://doi.org/10.56093/ijans.v84i5.40641

Keywords:

Allele mining, CNV, Drug discovery, GWAS, Machine learning, SNP, Transcriptomics, Vaccine designing

Abstract

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.

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