Syllabus
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Course Code: M-FSC 403 Course Name: Forensic Genetics, Serology and Bioinformatics |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Human Genetics: Human genetic variations. Mendelian Inheritance. Hardy-Weinberg Equilibrium. Mutation- their types and causes. Relevance of population genetics. Allele frequency, genotype frequency. Polymorphism and heterozygosity. Measures of genetic variations. |
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| 2 | Forensic Serology: Blood groups – history, biochemistry, biosynthesis of ABO antigen. Genetics of ABO, Rh, MN and other blood group systems. Secretors and non-secretors, rare alleles. Bombay blood group. Blood identification –presumptive and confirmatory assays. Methods of ABO blood grouping from dried blood stains and other body fluids (absorption elusion method, absorption inhibition method and mix-agglutination method), species identification from blood- Double Immunodiffusion Assays, Crossed-Over Electrophoresis. |
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| 3 | Forensic Protein Profiling: Erythrocyte Isoenzymes (PGM, GLO-I, ESD, EAP, AK, ADA etc), hemoglobin polymorphism. HLA typing. Role of sero-genetic markers in individualization, paternity disputes, and their limitations. Identification of Vaginal Secretions and Menstrual Blood: Identification of Vaginal Stratified squamous epithelial cells, vaginal acid phosphatase, and vaginal bacteria Semen- composition, spermatozoa morphology, presumptive and confirmatory tests for semen- acid phosphatase, prosthetic antigen test (P30), vesicle specific antigen test, RNA based assay. |
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| 4 | Bioinformatics: Introduction to bioinformatics and its application in Forensics Science. Integrated information retrieval. Major databases in bioinformatics. Sequence alignment, Phylogenetic analysis and related tools. Gene identification and prediction. Bioinformatics analysis of DNA Microarray, Bioinformatics tools of Forensic applications, Protein structure prediction and visualization tools. Tools used in proteomics, In-silico simulation for molecular biology experiments. Basic theory of probability and statistics. Bayesian analysis. Likelihood ratio. Statistical evaluation of DNA profiles using Bioinformatics tools. | |
