Bioinformatics is a new emerging area. It is an integration of Statistics, Computer applications and Biology.   The trained manpower in the area of Bioinformatics is required for meeting the new challenges in research in the discipline of Agricultural Statistics. This course is meant to train the students on concepts of basic biology, statistical techniques and computational techniques for understanding bioinformatics principals.


  • Basic Biology: Proteins and enzymes, genes, gene structures, gene expression and regulation, Molecular tools, nucleotides, nucleic acids, Markers, bioenergetics. Structural and functional genomics: Organization and structure of genomes, genome mapping, assembling of physical maps, strategies and techniques for genome sequencing and analysis. Single nucleotide polymorphism, expressed sequence tag.
  • Computing techniques: Languages useful for browsing biological databases on web; Computer networks – Internet, World wide web, Web browsers – EMBnet, NCBI: Databases pertaining to Nucleic acid sequences, protein sequences, Genome and Proteome: Searching sequence databases, Structural databases.
  • Statistical Techniques: MANOVA, Cluster analysis, Discriminant analysis, Principal component analysis, Principal coordinate analysis, Multidimensional scaling: Multiple regression analysis; Likelihood approach in estimation and testing: Resampling techniques – Bootstrapping and Jack-knifing: Markov Models. Hidden Markov Models, Bayesian estimation and Gibbs sampling.
  • Tools for Bioinformatics: DNA Sequence Analysis – Features of DNA sequence analysis, Approaches to EST analysis; Pairwise alignment techniques: Comparing two sequences, PAM and BLOSUM, Global alignment (The Needleman and Wunsch algorithm), Local Alignment (The Smith-Waterman algorithm), Dynamic programming, Pairwise database searching: Sequence analysis– BLAST and other related tools, Different methods of Multiple sequence alignment, Searching databases with multiple alignments; Alignment Scores, Design and Analysis of microarray experiments.


Gene expression and regulation, Markers, Genome mapping, Genome sequencing and analysis, Classification techniques, Cluster analysis, PCA and PCoA, Estimation and Testing of Hypothesis, Analysis of microarray data, Programming languages in Bioinformatics, Web browsers – NCBI & TIGR, Nucleotide and protein databases, BLAST, Protein structures.