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ICAR AICE-JRF/SRF Bioinformatics & Computational Biology: practice questions

ICAR AICE-JRF/SRF - Agricultural Statistics, Computer Application, Bioinformatics (PhD admission and fellowship) · India · National Testing Agency for ICAR

Original practice questions written to the published syllabus · not past papers
10worked here, free
95written to this exam in this subject
10syllabus units

What this subject covers

The third sub-subject of the ICAR Statistical Sciences group: molecular-biology basics, sequence databases, pairwise and multiple alignment, genome assembly, phylogenetics, and the statistics behind scoring and tree reliability. Taught from the statistician's side of the bench.

In the exams: AICE-JRF subject 69 Bioinformatics Units 1-5 (heavy: the whole paper); AIEEA-PG Group 07 sub-subject 7.3 (heavy); AIEEA-PG Group 07 bioinformatics databases (present)

Where the 95 questions fall

How our questions for ICAR AICE-JRF/SRF spread across the units of Bioinformatics & Computational Biology. Difficulty: 12 easy, 69 medium, 14 hard.

BLAST and FASTA; scoring matrices PAM and BLOSUM13
Phylogenetic trees: parsimony, distance (UPGMA, neighbour-joining) and maximum likelihood12
Pairwise alignment: Needleman-Wunsch and Smith-Waterman11
Assembly quality, read coverage and sequencing error11
Tree reliability: bootstrap and jackknife10
Protein structure and functional annotation (introduction)10
Multiple sequence alignment8
Sequencing technologies and genome assembly: de novo and comparative8
Genomic resources and databases: NCBI, EBI, ExPASy, GenBank, EMBL, DDBJ7
Molecular biology basics: DNA, RNA, gene expression and recombinant DNA5

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Genomic resources and databases: NCBI, EBI, ExPASy, GenBank, EMBL, DDBJ · medium

What distinguishes Swiss-Prot from TrEMBL within UniProtKB?

  1. Swiss-Prot holds DNA sequences, while TrEMBL holds the proteins they encode
  2. Swiss-Prot covers only plants, while TrEMBL covers animals and microbes
  3. Swiss-Prot stores 3-D structures and TrEMBL stores the sequences
  4. Swiss-Prot is manually reviewed; TrEMBL is automatically annotated
Answer and working

D. Swiss-Prot is manually reviewed; TrEMBL is automatically annotated

TrEMBL entries are computer-annotated translations awaiting curation; Swiss-Prot entries are reviewed by curators.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

2 · Pairwise alignment: Needleman-Wunsch and Smith-Waterman · medium

Why are affine gap penalties preferred to linear ones for biological sequences?

  1. Affine penalties make every gap free of cost
  2. One indel event spans many residues
  3. Linear penalties cannot be computed by dynamic programming
  4. Affine penalties remove the need for a substitution matrix
Answer and working

B. One indel event spans many residues

A gap-opening cost plus a smaller extension cost favours one long gap over many short ones, matching how indels arise.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

3 · BLAST and FASTA; scoring matrices PAM and BLOSUM · medium

What does BLOSUM62 mean?

  1. Built from proteins that are exactly 62% identical
  2. Corresponding to 62 accepted mutations per 100 residues
  3. Blocks clustered at 62% identity
  4. Using a word size of 62 residues in search
Answer and working

C. Blocks clustered at 62% identity

Clustering at 62% down-weights very similar sequences, so the matrix reflects substitutions among sequences below that identity.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

4 · Multiple sequence alignment · medium

What are the main steps of progressive multiple sequence alignment?

  1. Random alignment followed by deletion of all gapped columns
  2. Pairwise distances, a guide tree, then alignment in tree order
  3. Exact dynamic programming over all sequences at once
  4. Assembly of the sequences into one contig
Answer and working

B. Pairwise distances, a guide tree, then alignment in tree order

Most similar sequences are aligned first and others added as profiles following the guide tree.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

5 · Sequencing technologies and genome assembly: de novo and comparative · medium

What is the main type of error in raw reads from single-molecule long-read technologies, compared with short-read sequencing by synthesis?

  1. Substitutions, versus mainly insertions and deletions
  2. Insertions and deletions, versus mainly substitutions
  3. Chimeric reads, versus mainly adapter dimers
  4. No errors at all, versus mainly substitutions
Answer and working

B. Insertions and deletions, versus mainly substitutions

Long-read raw errors are dominated by indels (though accuracy has improved greatly); short-read errors are mostly substitutions rising towards read ends.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

6 · Sequencing technologies and genome assembly: de novo and comparative · medium

What do paired-end or mate-pair reads add to genome assembly?

  1. Higher base quality at every position
  2. Links to order and orient contigs
  3. Longer individual reads
  4. Removal of all repeats from the genome
Answer and working

B. Links to order and orient contigs

Two reads from the same fragment at a known approximate distance can bridge gaps between contigs.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

7 · Sequencing technologies and genome assembly: de novo and comparative · medium

What is a risk of reference-guided (comparative) assembly?

  1. It cannot use short reads
  2. It always produces more contigs than de novo assembly
  3. It needs no sequencing at all
  4. Sequence absent from the reference is missed
Answer and working

D. Sequence absent from the reference is missed

Mapping to a related genome is efficient but biased towards the reference; presence-absence variation is common among varieties of a crop.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

8 · Assembly quality, read coverage and sequencing error · medium

Why are PCR duplicate reads marked or removed before variant calling?

  1. They always map to the wrong chromosome
  2. They have higher base quality than other reads
  3. They are copies of one molecule
  4. They cannot be aligned
Answer and working

C. They are copies of one molecule

Duplicates do not provide independent evidence; leaving them in inflates confidence in amplification errors.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

9 · Tree reliability: bootstrap and jackknife · easy

How is a bootstrap replicate made in phylogenetics?

  1. By sampling alignment columns with replacement
  2. By sampling taxa without replacement
  3. By shuffling bases within each sequence
  4. By removing half the taxa
Answer and working

A. By sampling alignment columns with replacement

Felsenstein's (1985) bootstrap resamples sites to the original length, rebuilds the tree, and counts how often each clade recurs.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

10 · Protein structure and functional annotation (introduction) · hard

A list of 200 differentially expressed genes is tested for enrichment in each of 5,000 GO terms. What two precautions are essential?

  1. Use all genes in all species as background and test only the top term
  2. Remove all genes without GO terms from the list and skip correction
  3. Test only cellular component terms and use raw p-values
  4. Use the correct background gene set and correct for multiple testing
Answer and working

D. Use the correct background gene set and correct for multiple testing

Enrichment depends on the universe of genes that could have been detected, and thousands of terms are tested, so FDR control is needed.

Further reading: Durbin et al., Biological Sequence Analysis; Mount, Bioinformatics; Pevsner, Bioinformatics and Functional Genomics; Felsenstein, Inferring Phylogenies

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Bioinformatics & Computational BiologyThe full study manual, free ICAR AICE-JRF/SRFFormat, sections and how it has changed

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