This twenty-weeks long course is designed to introduce students of Biosciences to the Computational Biology- using computer for biological data analysis.
PowerPoint slides of lectures will be available on the presentation part of the course website. The lectures build on each other, so you should view them in the order listed. The few lectures listed as "Honors" or "Optional" will not be covered on the quizzes but are provided in case you want to go into more depth.
Prescribed textbook for this course is Baxevanis, A.D. and Ouellette, B.F.F. (2004). Bioinformatics: A Practical guide to the Analysis of Genes and Proteins. Wiley-Interscience, USA. E-Book
Short ungraded exercises will occasionally be embedded into lectures. Each lecture will be followed by ungraded exercises. Answers to these exercises will be given and explained after you reply to the questions. These exercises do not introduce new material, but they enable you to check your understanding of the lectures. If you miss too many of the exercises after a lecture, then you might want to listen to that lecture again.
You should expect to spend on about 2 hours per week watching the lectures, another 2 hours per week doing the exercises, and about 1 hour on each quiz. You might want to spend additional time doing the reading and participating in the discussion forums.
Syllabus with Course Materials
Unit 1: FB
Biological Databases: Nucleotide Sequence Databases, GenBank, DDBJ, EMBL, Sequence Flatfile and submission process, Protein sequence databases, UniProt in detail, Mapping databases, Genomic databases, Data mining.
The Bioinformatics Gold Rush Scientific American 283.1 (2000): 58-63.
Unit 2: MK
Analysis for nucleotide sequences: Gene Prediction methods and programs, Markov and Hidden Markov models in gene prediction, Promoter analysis, RNA secondary structure thermodynamics, Dynamic programming and genetic algorithms for secondary structure prediction, refining multiple sequence alignment based on RNA secondary structure predictions, Vienna RNAfold, Evolution and origins of sequence polymorphisms, SNP discovery methods and databases, Genotyping, International haplotype map project, 1000 genomes project.
Unit 3: MK
Analysis for protein sequences: Predicting features of individual residues, Predicting function, Neural Networks, Protein structure prediction, Protein structure databases, PDB in detail, 3D visualization softwares, Pathway and molecular interaction databases, Prediction algorithms for pathways and Molecular Interactions, Integrating gene expression data with pathway information.
Inferring relationships: Global Vs. local sequence alignments, Dotplots, Scoring matrices, Pairwise sequence alignment, BLAST, Position-Specific scoring and PSI-BLAST, MegaBLAST, BL2SEQ, BLAT, FASTA Vs BLAST, Protein multiple sequence alignments, Multiple structural alignments, Shotgun sequencing, Sequence assembly and finishing.
Unit 5: FB
Phylogenetic Analysis: Basics of phylogenetics, Nucleotide substitution models and selection, Distance-matrix-based methods, Neighbor-Joining, Fitch-Margoliash, Outgroups, UPGMA, Minimum Evolution, Maximum Parsimony, Maximum Likelihood, Bayesian Inference, Searching for trees, Rooting trees, Bootstrapping, Likelihood ratio tests.
BAST, F. 2015. Tutorial on Phylogenetic Inference Part-2. Resonance 20 (5) 445-457 PDF
BAST, F. 2015. Tutorial on Phylogenetic Inference Part-1. Resonance 20 (4) 360-367 PDF
Unit 6: MK
Genomics: Comparative Genomics, Genomic alignments, Gene predictions in genomic alignments, Genome-wide association study, Phylogenetic footprinting, Gene annotation, Gene expression analysis using DNA Microarray, Annotation of array probes, Image processing, Normalizing expression measurements.
Unit 7: MK
Proteomics: Major proteomic approaches, Protein analysis by MALDI and SELDI methods, Time of Flight MS in protein analysis, Protein Identification by Mascot, Peptide Mass Fingerprinting, Comparative proteomics, Two-Dimensional Polyacrylamide Gel Electrophoresis.
1. Databases-Problem set
(Pages 52 -64)
3.Finding best evolutionary models (Goodness of fit/ML ModelTest) Problem Set
4. Calculating evolutionary distances- Problem Set
5. Distance methods-Problem set
6. Cladistic Methods- Problem set