BU-401 Bioinformatics & Computational Biology

Unit · year 4

BU-401 · Bioinformatics & Computational Biology

Threads information · systems25 lectures5 concepts

Reading meaning from biological sequences and datasets.

PREREQUISITES

L01	Biological Data at Scale —
L02	Sequence Databases and Formats Database searching with BLAST
L03	Pairwise Sequence Alignment Sequence alignment
L04	Scoring Matrices: PAM and BLOSUM Sequence alignment
L05	Gap Penalties Sequence alignment
L06	Needleman–Wunsch: Global Alignment Sequence alignment
L07	Smith–Waterman: Local Alignment Sequence alignment
L08	Multiple Sequence Alignment Sequence alignment
L09	Heuristic Search and BLAST Database searching with BLAST
L10	Interpreting E-Values and Bit Scores Database searching with BLAST
L11	BLAST Variants and Choosing One Database searching with BLAST
L12	Distance-Based Tree Building Building phylogenetic trees
L13	Neighbour-Joining and UPGMA Building phylogenetic trees
L14	Maximum Likelihood Phylogenetics Building phylogenetic trees
L15	Bayesian Phylogenetics Building phylogenetic trees
L16	Bootstrapping and Tree Support Building phylogenetic trees
L17	Markov Chains in Sequence Analysis Hidden Markov models
L18	Hidden Markov Models Hidden Markov models
L19	The Viterbi and Forward Algorithms Hidden Markov models
L20	HMMs for Gene Finding and Profiles Hidden Markov models
L21	Gene Expression Data and Normalisation Clustering gene expression
L22	Clustering Gene Expression Clustering gene expression
L23	Hierarchical and k-Means Clustering Clustering gene expression
L24	Dimensionality Reduction: PCA and t-SNE Clustering gene expression
L25	Synthesis: Algorithms as Biological Instruments Sequence alignment, Hidden Markov models

Detecting homology by dynamic programming.

Fast approximate matching against large databases.

Distance and likelihood methods.

Finding genes and motifs probabilistically.

Finding structure in transcriptomic data.