This course will enable students to learn to apply statistical methods to analyze biological data and draw meaningful conclusions & develop proficiency in experimental design, hypothesis testing, and data interpretation. The student will learn bioinformatics tools for sequence analysis, protein structure prediction, and database searching.
Overview of bioinformatics- introduction, advantages and limitations, Database types- Primary and secondary databases, sequence databases- nucleotide and protein sequence databases (NCBI, EMBL, DDBJ, UNIPORT)
Structure databases (PDB, MMDB), Concept of similarity searching, methods of similarity searching (BLAST, FASTA) and its use, multiple sequence alignment.
Sampling - Sampling procedure, types of sampling, Classification and tabulation of data, frequency distribution, probability, addition and multiplication theorem of probability, advantages and limitations of biostatistics.
Mean, median and mode, Measures of dispersion - Standard deviation, coefficient of variation, Skewness and Kurtosis.
Hypothesis testing, Nulls hypothesis and alternative hypothesis, Chi-square test, F-test, ANOVA- one way and two way classifications.