Bio Informatics (AL-803 (B)) - Important Questions

Explain dynamic programming algorithms used for global and local sequence alignment. Describe the Needleman–Wunsch and Smith–Waterman algorithms, including initialization, recurrence relations, scoring matrices and traceback procedure.

Derive the recurrence relation used in the Needleman–Wunsch global alignment algorithm and explain the role of substitution score $s(a,b)$ and gap penalty $d$.

Provide the recurrence explicitly.

Compare dynamic programming approaches and heuristic database-search methods for sequence alignment. Discuss differences in sensitivity, specificity, time and memory complexity, and typical use-cases (for example, Smith–Waterman vs BLAST).

Describe heuristic sequence-search tools such as BLAST and FASTA. Explain their core ideas (word seeding, extension), scoring and significance estimation, and how they achieve speed at the expense of exhaustive optimality.

Describe the organization, primary records and retrieval strategies of major nucleotide sequence databases: GenBank, EMBL and DDBJ. Explain accession numbers, submission and cross-references.

Describe the structure, annotation standards and access methods of major protein sequence databases, with emphasis on UniProt (Swiss-Prot and TrEMBL) and NCBI RefSeq.

Explain protein structure databases such as the Protein Data Bank (PDB). Describe common file formats (PDB, mmCIF), structural metadata stored, and common queries used to retrieve structural information.

Explain strategies for gene prediction in prokaryotic and eukaryotic genomes. Compare ab initio methods and evidence-based (homology/annotation-supported) approaches and list commonly used gene prediction tools (for example, AUGUSTUS, Glimmer).

Outline the computational workflow for mining gene expression data from microarray and RNA-seq experiments. Include steps for quality control, normalization, clustering, and differential expression analysis, and briefly mention common software/tools used at each stage.

Describe common proteome analysis workflows in bioinformatics, including protein identification from mass spectrometry data, database searching, quantification strategies and analysis of post-translational modifications.

Describe a computational pipeline for protein structure prediction covering homology modeling, threading and ab initio methods. For each stage discuss required inputs, typical algorithms and key challenges and limitations.