The aim of this course is twofold: to provide an overview of the most common statistical methods for molecular genomics and transcriptomics data analysis, and to provide the necessary information for solving the complex biological problems and achieving the satisfactory score of sustainable development goal (SDG) index from the agriculture and health sectors.

The main objective of this course are

to understand statistical modeling for bioinformatics

to learn most common statistical methods for genome data analysis

to develop the capability of statistical model building strategies for genome assembling

hands-on training on genomics data analysis to understand how to provide the necessary information to solve the complex biological problems

After completion of this course successfully, the learners/students would be able

to analyze genome datasets to provide the necessary information to solve the complex biological problems that are associated with the genetic factors

to select appropriate statistical algorithms for genome assembling and their analysis

to contribute to the development of high yielding varieties and to achieve the satisfactory score of SDG index from the agricultural sector.

to contribute to the discovery of new drugs/vaccines for the complex diseases and to achieve the satisfactory score of SDG index from the health sector.

Introduction to Bioinformatics: Basic concept and importance, Molecular OMICS, Chromosome, Gene, Meiosis, Mitosis, Mandel’s Laws, Linkage and Mapping, Quantitative genetics, Molecular markers, Genotype and Phenotype, Genotyping technology,  DNA/RNA sequences, Central dogma, Amino acids with its structure and functions, Codons, Protein/Amino acid sequence.

Genetic Linkage Analysis: Introduction, Mendelian segregation, Segregation patterns in a full-sib family, Two-point analysis for backcross and F2-intercross, three-point analysis, Multilocus likelihood and locus ordering, Estimation with many loci, Mixture likelihoods and order probabilities, Map functions. Linkage analysis with controlled crosses and recombinant inbred lines.

Genome Sequencing: DNA sequencing, RNA sequencing, Whole genome sequencing, Basic methods of Sequencing, High-throughput sequencing (HTS) methods, Genome assembly, Next Generation Sequencing (NGS) and application areas.

Sequence analysis: Pairwise sequence alignment and protein structure/function prediction using online databases NCBI, GenBank, EMBL, UniPort, SWISSPROT, ExPASy, PDB and software BLAST, Pfam, Clustal-Omega/ ClustalW, SMART, SABLE and SWISS-MODEL.

1)

Lesk, A. (2013). Introduction to bioinformatics. Oxford University Press.

2)

Stuart M. Brown (2013). Next Generation DNA Sequencing Informatics. CSH press, N. Y. [Avadhanam, Mount]

3)

Wu, R., C. Ma & G. Casella (2007). Statistical genetics of quantitative traits: Linkage, maps and QTL. Springer.

4)

Baxevanis, A. D., & B. F. Ouellette (2004). Bioinformatics: a practical guide to the analysis of genes and proteins (Vol. 43). John Wiley & Sons.

5)

Davis, J. W. (2007). Bioinformatics and computational biology solutions using R and Bioconductor. [Gondro, Mount]

6)

Johnson, A. D. (2008). Bioinformatics for geneticists: a bioinformatics primer for the analysis of genetic data. [Barnes]

7)

Mathur, S. K. (2009). Statistical Bioinformatics with R. Academic Press.