Syllabus
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Course Code: BCH-401 Course Name: Biostatistics and Bioinformatics |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Fundamentals of Statistics: Arithmetic mean, median, mode: measures of variation: standard deviation, variance, coefficient of variation; properties; correlation: types and methods; simple, multiple, linear and non linear correlation, spearman’s correlation, rank correlation; regression: linear and curvilinear regression (for X and Y only), regression lines by least square method, regression equations of X on Y and Y on X only; sample size; power of study. | |
| 2 | Tests of Significance: Null hypothesis; standard error; level of significance; degrees of freedom; significance of mean for large samples; significance in means for small samples (students t-test); significance in ratio of two samples; F test (for difference between variance of two samples); chi square test; analysis of variance (ANOVA) test for one and two way classification; applications of various online tools: SPSS, Minitab, XLSTAT etc. | |
| 3 | Fundamentals of Bioinformatics: Introduction to bioinformatics; concept of databases; biological databases; integration of databases; applications and problems in information retrieval from biological databases; Pairwise sequence comparisons by DOT-MATRIX and dynamic programming; Global (Needleman and Wunsch algorithm) and local (Smith and Waterman algorithm) alignments; Measures of sequence similarity (Alignment score, % sequence identity; percentage similarity; statistical scores–E, P and Z); Heuristic approaches for database searching; BLAST and FASTA; multiple sequence alignment; SP scoring; multidimensional dynamic programming; progressive sequence alignment approach. | |
| 4 | Applications of Bioinformatics: Gene, ORF of a gene, promoter and regulatory elements prediction; phylogenetic analysis (phylogeny, Phylogenetic tree, construction methods of Phylogenetic tree and Phylogenetic programs); protease digestion mapping; protein structure analysis; protein secondary structure prediction; Homology modelling (principles and procedures); docking; determination of metabolic pathways. | |
