BIF401 — Bioinformatics I: Midterm & Finalterm Solved MCQs and Subjective
This solved paper covers BIF401 — Bioinformatics I for Virtual University of Pakistan students, with midterm (Lectures 1–22) and finalterm (Lectures 23–45) questions solved with answers. It includes 10 midterm MCQs, 10 finalterm MCQs, 5 midterm subjective questions and 5 finalterm subjective questions — 30 questions in total — covering bioinformatics, biological databases, BLAST and FASTA, sequence alignment and phylogenetics. Every answer was solved independently and cross-checked against the VU handout, with the handout lecture cited under each question; handout page numbers are being added as they are verified. Use the Take the BIF401 quiz link below to practise all 20 MCQs interactively with a live score counter. See the Solved MCQs page and the midterm and finalterm mega hubs for solved papers for every subject. Compiled and verified by Virtual Ustaad. Last updated: 6 November 2026.
1. Midterm Solved MCQs
Q1. What is the acronym meaning/definition of BLAST? A) Basic Local Alignment Search Tool B) Biological Sequence Analysis Tool C) Basic Linear Alignment Search Technique D) Bioinformatics Local Analysis System Tool Correct answer: A — Basic Local Alignment Search Tool Reference: VU handout, Lecture — Database Searching; BLAST. Page — not confirmed in source.
Q2. The BLAST program was developed in _______. A) 1992 B) 1995 C) 1990 D) 1991 Correct answer: C — 1990 Reference: VU handout, Lecture — Database Searching; BLAST. Page — not confirmed in source.
Q3. Humans have over _________ genes. A) 30,000 B) 25,000 C) 35,000 D) 39,000 Correct answer: B — 25,000 Reference: VU handout, Lecture — Introduction to Bioinformatics; the Human Genome. Page — not confirmed in source.
Q4. The genes may produce _________ different proteins. A) < 250,000 B) > 25,000 C) > 250,000 D) < 25,000 Correct answer: C — > 250,000 Reference: VU handout, Lecture — Introduction to Bioinformatics; Genes and Proteins. Page — not confirmed in source.
Q5. Data is being accumulated at an _____ rate. A) Exponentially increasing B) Double increasing C) Decreasing D) Stable Correct answer: A — Exponentially increasing Reference: VU handout, Lecture — Introduction to Bioinformatics. Page — not confirmed in source.
Q6. Since genome and _____ information is publicly available, you can process this information after downloading it. A) Proteome B) Evolutionary studies C) System biology D) Both b and c Correct answer: A — Proteome Reference: VU handout, Lecture — Introduction to Bioinformatics; Biological Databases. Page — not confirmed in source.
Q7. The phylogenetic "tree of life" basically consists of the following three domains: A) Bacteria, archae, monera B) Plantae, Animalia, fungi C) Bacteria, archaea, eucarya D) Eucarya, viruses, protozoa Correct answer: C — Bacteria, archaea, eucarya Reference: VU handout, Lecture — Phylogenetics. Page — not confirmed in source.
Q8. Which database stores protein sequences? A) GenBank B) UniProt C) PDB D) PubMed Correct answer: B — UniProt Reference: VU handout, Lecture — Biological Databases. Page — not confirmed in source.
Q9. When did Smith–Waterman first describe the algorithm for local alignment? A) 1950 B) 1970 C) 1981 D) 1925 Correct answer: C — 1981 Reference: VU handout, Lecture — Sequence Alignment; Dynamic Programming. Page — not confirmed in source.
Q10. Which of the following is NOT an approach to local alignment? A) Smith–Waterman algorithm B) K-tuple (word) method C) Words method D) Needleman–Wunsch algorithm Correct answer: D — Needleman–Wunsch algorithm Reference: VU handout, Lecture — Sequence Alignment; Local vs Global Alignment. Page — not confirmed in source.
2. Finalterm Solved MCQs
Q1. BLASTX uses __________ as queries to search against a protein sequence database. A) Protein sequences B) Nucleotide sequences, translated in all six reading frames C) RNA sequences only D) Amino acid sequences directly Correct answer: B — Nucleotide sequences, translated in all six reading frames Reference: VU handout, Lecture — Basic BLAST Programs. Page — not confirmed in source.
Q2. Which BLAST program compares a protein query against a nucleotide database translated in all six reading frames? A) BLASTN B) BLASTP C) BLASTX D) TBLASTN Correct answer: D — TBLASTN Reference: VU handout, Lecture — Basic BLAST Programs. Page — not confirmed in source.
Q3. Which of the following is NOT a variant of BLAST? A) BLASTN B) BLASTP C) BLASTX D) TBLASTNX Correct answer: D — TBLASTNX Reference: VU handout, Lecture — Basic BLAST Programs. Page — not confirmed in source.
Q4. In sequence alignment by BLAST, each word from the query sequence is typically _______ residues for protein sequences and _______ residues for DNA sequences. A) 3; 11 B) 11; 3 C) 5; 5 D) 2; 7 Correct answer: A — 3; 11 Reference: VU handout, Lecture — BLAST Algorithm; Word Matching. Page — not confirmed in source.
Q5. What does the E-value indicate in BLAST results? A) The number of alignments expected to occur by chance with the given score B) The exact number of matching residues C) The length of the query sequence D) The percentage identity of the alignment Correct answer: A — The number of alignments expected to occur by chance with the given score Reference: VU handout, Lecture — BLAST Results; E-value and Bit Score. Page — not confirmed in source.
Q6. Currently, there are two major heuristic algorithms for performing database searches: A) Smith–Waterman and Needleman–Wunsch B) BLAST and FASTA C) ClustalW and MUSCLE D) HMMER and PSI-BLAST Correct answer: B — BLAST and FASTA Reference: VU handout, Lecture — Heuristic Database Searching. Page — not confirmed in source.
Q7. Which of the following is untrue regarding BLAST and FASTA? A) FASTA is faster than BLAST B) FASTA is the most accurate C) BLAST has limited choices of databases D) FASTA is more sensitive for DNA–DNA comparisons Correct answer: A — FASTA is faster than BLAST Reference: VU handout, Lecture — BLAST vs FASTA. Page — not confirmed in source.
Q8. The PAM (Point Accepted Mutation) scoring matrix is derived from: A) Randomly generated alignments B) Observed mutations in closely related proteins C) DNA base frequencies D) Protein 3D structures only Correct answer: B — Observed mutations in closely related proteins Reference: VU handout, Lecture — Scoring Matrices; PAM and BLOSUM. Page — not confirmed in source.
Q9. A dot plot is used in bioinformatics to: A) Predict the 3D structure of a protein B) Visually compare two sequences and identify regions of similarity C) Measure the mass of a protein D) Sequence a genome by synthesis Correct answer: B — Visually compare two sequences and identify regions of similarity Reference: VU handout, Lecture — Dot Plots. Page — not confirmed in source.
Q10. In a phylogenetic tree, what is the difference between a rooted and an unrooted tree? A) A rooted tree has a common ancestor (root) showing the direction of evolution; an unrooted tree shows relationships without a root B) A rooted tree has no branches; an unrooted tree has branches C) A rooted tree is drawn only for DNA; an unrooted tree only for proteins D) There is no difference Correct answer: A — A rooted tree has a common ancestor (root) showing the direction of evolution; an unrooted tree shows relationships without a root Reference: VU handout, Lecture — Phylogenetic Trees. Page — not confirmed in source.
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3. Midterm Subjective
Q1. What is bioinformatics? What is the need for bioinformatics, and what is its scope? Answer: Bioinformatics is the application of computer science, mathematics and statistics to the management and analysis of biological data — especially DNA, RNA and protein sequences and structures. Need: modern biological experiments produce data at an exponentially increasing rate (genome sequencing, proteomics), far too much to analyse by hand; the data must be stored on computers, searched, compared and interpreted. Scope: sequence analysis and alignment; biological databases (GenBank, UniProt, PDB); database searching (BLAST, FASTA); gene and protein structure prediction; phylogenetics; drug design; and personalised medicine. As the archive asks: "Enlist few points in scope of bio informatics?" — the points above are the standard answer. Reference: VU handout, Lecture — Introduction to Bioinformatics. Page — not confirmed in source.
Q2. What is BLAST? What does it stand for, and what is it used for? Answer: BLAST — Basic Local Alignment Search Tool — was developed by Stephen Altschul and colleagues at NCBI in 1990. It is a heuristic program that compares a query sequence against a sequence database to find sequences similar to the query, i.e. to identify a sequence, find homologues, and infer the function of an unknown sequence from similar known sequences. It uses a word method: it breaks the query into short words, finds those words in the database, and extends the matches into local alignments, scoring them with a substitution matrix (e.g. BLOSUM62); results are reported with a bit score and an E-value — the lower the E-value, the more significant the match. Reference: VU handout, Lecture — Database Searching; BLAST. Page — not confirmed in source.
Q3. Distinguish between global, local and multiple sequence alignment. Which algorithms are used for each? Answer: Global alignment aligns two sequences over their entire length (best for sequences of similar length that are similar throughout) — the Needleman–Wunsch algorithm (dynamic programming). Local alignment finds the most similar regions/subsequences between two sequences (best for sequences that share only a conserved domain) — the Smith–Waterman algorithm (1981), also dynamic programming. Multiple sequence alignment aligns three or more sequences together to reveal conserved regions across a family. In practice, database searching uses heuristic word methods (BLAST, FASTA) rather than full dynamic programming, because they are 50–100 times faster on large databases. Reference: VU handout, Lecture — Sequence Alignment. Page — not confirmed in source.
Q4. What are biological databases? Give examples of nucleotide, protein and structure databases, and say what each stores. Answer: Biological databases are organised collections of biological data, stored on computers and publicly available, so that researchers can download and analyse them. Examples: nucleotide-sequence databases — GenBank, ENA, DDBJ (store DNA/RNA sequences); protein-sequence databases — UniProt (stores protein sequences and their annotation — what a UniProt entry tells you about a protein query: its sequence, function, family, and cross-references); protein-structure databases — PDB, the Worldwide Protein Data Bank (stores 3D structures of proteins, nucleic acids and ligand interactions); and RefSeq (a large but non-redundant collection of genes and genomes, good for BLAST searches). The first bioinformatics database was established by Margaret Dayhoff (protein sequences). Reference: VU handout, Lecture — Biological Databases. Page — not confirmed in source.
Q5. What is FASTA, and why do we use the FASTA algorithm? How does it differ from BLAST? Answer: FASTA is a heuristic database-search program (Pearson & Lipman) and also the name of the simple sequence file format (a header line beginning with ">" followed by the sequence). The FASTA algorithm is used to search a sequence database for sequences similar to a query, quickly, using a word/ktup method: it finds short identical words shared by the query and a database sequence, then extends and scores the best regions. Difference from BLAST: both are heuristic word methods and neither guarantees the optimal alignment, but BLAST is faster and more popular, while FASTA is more sensitive (more accurate), especially for DNA–DNA comparisons; BLAST uses a fixed word size (3 for proteins, 11 for DNA) and a different extension/scoring scheme. Reference: VU handout, Lecture — FASTA Algorithm. Page — not confirmed in source.
4. Finalterm Subjective
Q1. What are the uses and functions of BLAST? Explain blastx, tblastn and tblastx. Answer: BLAST is used to: identify an unknown sequence by finding similar sequences in a database; find homologues (evolutionarily related sequences); infer the function of a gene/protein from similar known sequences; and compare sequences quickly using heuristic word matching. The programs differ in query and database type: BLASTN — nucleotide query vs nucleotide database; BLASTP — protein query vs protein database; BLASTX — nucleotide query, translated in all six reading frames, vs a protein database; TBLASTN — protein query vs a nucleotide database translated in all six reading frames; TBLASTX — nucleotide query, translated in six frames, vs a nucleotide database translated in six frames. (There is no TBLASTNX.) Reference: VU handout, Lecture — Basic BLAST Programs. Page — not confirmed in source.
Q2. Write down the steps involved in the FASTA algorithm. Answer: (1) Identify short identical words (ktups) shared between the query sequence and each database sequence. (2) Score the matching regions and select the best-scoring (highest-density) regions. (3) Join and rescore the best regions, allowing gaps, to form an initial alignment. (4) Perform a full alignment (a banded Smith–Waterman-type dynamic-programming alignment) on the best candidate sequences to produce the final alignment and score. FASTA is heuristic: it does not guarantee the optimal alignment, but it is far faster than full dynamic programming on large databases. Reference: VU handout, Lecture — FASTA Algorithm. Page — not confirmed in source.
Q3. Write the seven steps of homology modelling. What is a BLOSUM matrix, and what are its steps? Answer: Homology (comparative) modelling predicts a protein's 3D structure from a related protein of known structure. Seven steps: (1) identify a template — a homologous protein whose 3D structure is known (usually by BLAST search, with sufficient sequence identity); (2) align the target sequence to the template sequence; (3) build the backbone/model the conserved core from the template; (4) model the loops (regions that differ, e.g. insertions/deletions); (5) model the side chains; (6) refine/optimise the model (energy minimisation); (7) evaluate/validate the model (e.g. Ramachandran plot, 3D–1D profile/Bowie–Eisenberg method). BLOSUM matrices are amino-acid substitution matrices derived from conserved blocks of aligned sequences (BLOSUM62 is the default for BLASTP); building one involves: collecting blocks of aligned sequences, counting observed amino-acid substitutions, converting the counts to log-odds scores, and scaling the matrix. Reference: VU handout, Lecture — Homology Modelling; Scoring Matrices. Page — not confirmed in source.
Q4. What is a dot plot? How is it used to compare sequences, and what do its features show? Answer: A dot plot is a visual method for comparing two sequences: one sequence is placed along the horizontal axis and the other along the vertical axis, and a dot is placed wherever the two sequences have the same residue at that position. A continuous diagonal line shows a region of similarity (identity) between the two sequences; breaks or shifts in the diagonal indicate insertions, deletions or rearrangements; parallel diagonals indicate repeated regions; scattered dots are random matches (noise). Dot plots are a quick, intuitive first look at sequence similarity before a formal alignment is done. Reference: VU handout, Lecture — Dot Plots. Page — not confirmed in source.
Q5. How do you show a phylogenetic tree, and what are the types used to visualise it? Distinguish rooted and unrooted trees, and explain what pseudoknots are in RNA secondary structure. Answer: A phylogenetic tree shows the evolutionary relationships between sequences/species; it is visualised as a branching diagram (cladogram/phylogram), usually drawn with the taxa at the tips and branch lengths proportional to evolutionary distance. Rooted trees have a root — the most recent common ancestor — and show the direction of evolution; unrooted trees show only the relationships (neighbour-joining distances) between taxa, with no root and no time direction. Rooted trees are used when the direction of evolution matters (e.g. tracing ancestry); unrooted trees when only the pattern of relatedness matters. Pseudoknots: in RNA secondary structure, a pseudoknot is formed when bases in a loop pair with bases outside the loop, so the structure is no longer a simple nested set of base pairs — the pairing crosses over, giving a knotted fold that is important in some RNAs (e.g. ribosomal and viral RNAs) and is harder to predict than ordinary stem-loops. Reference: VU handout, Lecture — Phylogenetic Trees; RNA Secondary Structure. Page — not confirmed in source.
Frequently asked questions
How many questions are in this BIF401 solved paper?
30 questions in total: 10 midterm MCQs, 10 finalterm MCQs, 5 midterm subjective questions and 5 finalterm subjective questions, all with solved answers and handout references.
Are the answers verified?
Yes. Every answer was solved independently and cross-checked against at least two sources, including the VU handout. Where a handout page number is confirmed it is cited under the question; page numbers still being verified are being added over time.
Can I practise these MCQs as a quiz?
Yes — use the Take the BIF401 quiz link in this post to practise all 20 MCQs interactively on the Solved MCQs page, with a live score counter.
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