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

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

  1. Unit 314 Marks High Priority

    Explain the dynamic programming approach to global pairwise sequence alignment. Derive the NeedlemanWunsch recurrence relations, describe the initialization, matrix filling and traceback steps, and discuss time and space complexity of the algorithm.

    Core derivation of dynamic programming for global pairwise alignment; high-frequency topic in Unit 3 (Dynamic programming).

  2. Unit 37 Marks High Priority

    Describe the SmithWaterman algorithm for local sequence alignment. Give the recurrence used, explain how the scoring and traceback produce the best local alignment, and discuss computational complexity and typical use-cases.

    Local alignment via dynamic programming; commonly asked in Unit 3.

  3. Unit 37 Marks High Priority

    Compare dynamic programming algorithms (NeedlemanWunsch/SmithWaterman) with heuristic database search methods such as BLAST. Discuss differences in accuracy, sensitivity, specificity, runtime, and typical scenarios where each approach is preferred.

    Direct comparison between exact dynamic programming methods and practical heuristics; reflects combined analytics topics (Methods of optimal alignment vs Heuristic methods).

  4. Unit 310 Marks High Priority

    Explain the BLAST algorithm (seed-and-extend heuristic) in detail. Describe word/seed generation, extension, evaluation of hits, and how statistical significance (E-value) is estimated in BLAST.

    Core heuristic method question; BLAST is a standard frequent topic in Unit 3 heuristics.

  5. Unit 37 Marks Medium Priority

    Explain the construction and use of evolutionary scoring matrices such as PAM and BLOSUM. Describe how each is derived, the biological interpretation of matrix entries, and guidelines for choosing an appropriate matrix (for example, $PAM_{250}$ vs $BLOSUM_{62}$).

    Scoring matrices are fundamental to alignment methods; medium-frequency topic allied to optimal alignment methods.

  6. Unit 37 Marks Medium Priority

    Discuss gap penalties used in sequence alignment. Define linear and affine gap penalties and give the recurrence relations for implementing an affine gap penalty in dynamic programming. Explain how gap opening and gap extension penalties affect alignments.

    Gap modeling and affine penalty recurrences are core to producing realistic alignments; moderately frequent in Unit 3.

  7. Unit 37 Marks Medium Priority

    Evaluate the trade-offs between sensitivity, specificity, and computational efficiency when using heuristic sequence-alignment methods versus exact dynamic programming algorithms. Provide examples and guidelines for choosing an approach for large-scale database searches.

    Evaluation of heuristic vs exact methods focusing on practical trade-offs; medium predicted importance.

  8. Unit 37 Marks Medium Priority

    Describe methods for multiple sequence alignment (MSA). Compare progressive alignment (e.g., Clustal) and iterative refinement approaches, discuss the limitations of exact dynamic programming for MSA, and outline common heuristics used to obtain practical MSAs.

    Multiple sequence alignment methods are related to pairwise alignment limitations and are commonly examined in Unit 3.

  9. Unit 37 Marks Medium Priority

    Explain how statistical significance of sequence alignments is assessed. Define E-value and p-value in the context of database search results and describe, at a high level, how these quantities are computed or estimated.

    Statistical assessment of alignment significance is an important applied topic; medium importance.

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