BIF601 — Bioinformatics Computing I: Midterm & Finalterm Solved MCQs and Subjective

This solved paper covers BIF601 — Bioinformatics Computing 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 computing, the genetic code, biological databases, Entrez, Perl, Python and SQL. 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 BIF601 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. The correct order of information flow in the central dogma of molecular biology is:
A) Protein → RNA → DNA
B) DNA → RNA → Protein
C) RNA → DNA → Protein
D) DNA → Protein → RNA
Correct answer: B — DNA → RNA → Protein
Reference: VU handout, Lecture — Introduction to Bioinformatics; the Central Dogma. Page — not confirmed in source.

Q2. How many different codons are possible with the four RNA bases read in triplets?
A) 16
B) 20
C) 61
D) 64
Correct answer: D — 64
Reference: VU handout, Lecture — The Genetic Code. Page — not confirmed in source.

Q3. The start codon in most protein-coding genes is:
A) UAA
B) AUG
C) UAG
D) GUG
Correct answer: B — AUG
Reference: VU handout, Lecture — The Genetic Code; Translation. Page — not confirmed in source.

Q4. Which of the following are the three stop (termination) codons?
A) AUG, GUG, UUG
B) UAA, UAG, UGA
C) UAA, UAC, UAU
D) AGG, AGA, UGA
Correct answer: B — UAA, UAG, UGA
Reference: VU handout, Lecture — The Genetic Code; Translation. Page — not confirmed in source.

Q5. The three primary nucleotide-sequence databases that exchange data daily as the International Nucleotide Sequence Database Collaboration (INSDC) are:
A) GenBank, EMBL-ENA and DDBJ
B) GenBank, UniProt and PDB
C) UniProt, Swiss-Prot and TrEMBL
D) PDB, SCOP and CATH
Correct answer: A — GenBank, EMBL-ENA and DDBJ
Reference: VU handout, Lecture — Biological Databases; Primary Databases. Page — not confirmed in source.

Q6. Entrez is:
A) A protein-structure prediction program
B) NCBI's integrated search and retrieval system for its databases
C) A multiple sequence alignment program
D) A genome assembly program
Correct answer: B — NCBI's integrated search and retrieval system for its databases
Reference: VU handout, Lecture — Data Retrieval; Entrez. Page — not confirmed in source.

Q7. Swiss-Prot (now UniProtKB/Swiss-Prot) is best described as a:
A) Manually curated, annotated protein-sequence database
B) Raw, unannotated nucleotide archive
C) 3D protein-structure database
D) Gene-expression database
Correct answer: A — Manually curated, annotated protein-sequence database
Reference: VU handout, Lecture — Biological Databases; Protein Databases. Page — not confirmed in source.

Q8. Perl became a popular first language for bioinformatics mainly because of its strength in:
A) 3D graphics rendering
B) Text processing and regular expressions
C) Real-time operating systems
D) Relational database design
Correct answer: B — Text processing and regular expressions
Reference: VU handout, Lecture — Programming for Bioinformatics; Perl. Page — not confirmed in source.

Q9. In Biopython, the module used to read and write sequence files in formats such as FASTA and GenBank is:
A) Bio.Align
B) Bio.SeqIO
C) Bio.PDB
D) Bio.Blast
Correct answer: B — Bio.SeqIO
Reference: VU handout, Lecture — Programming for Bioinformatics; Python and Biopython. Page — not confirmed in source.

Q10. EMBOSS is:
A) A single program for BLAST searching
B) An open-source suite of sequence-analysis programs
C) A protein-structure database
D) A genome browser
Correct answer: B — An open-source suite of sequence-analysis programs
Reference: VU handout, Lecture — Bioinformatics Software Suites; EMBOSS. Page — not confirmed in source.

2. Finalterm Solved MCQs

Q1. An open reading frame (ORF) is:
A) Any stretch of DNA between two restriction sites
B) A stretch of DNA that begins with a start codon and ends with a stop codon in the same reading frame, with no intervening stop codon
C) The promoter region of a gene
D) A sequence that codes for tRNA
Correct answer: B — A stretch of DNA that begins with a start codon and ends with a stop codon in the same reading frame, with no intervening stop codon
Reference: VU handout, Lecture — Gene Finding; Open Reading Frames. Page — not confirmed in source.

Q2. The restriction enzyme EcoRI recognises and cuts the sequence:
A) GGATCC
B) GAATTC
C) AAGCTT
D) TCTAGA
Correct answer: B — GAATTC
Reference: VU handout, Lecture — Restriction Mapping; Restriction Enzymes. Page — not confirmed in source.

Q3. For a short DNA primer, the approximate melting temperature by the Wallace rule is:
A) 2(A+T) + 4(G+C) °C
B) 4(A+T) + 2(G+C) °C
C) (A+T) + (G+C) °C
D) 2(A+T) − 4(G+C) °C
Correct answer: A — 2(A+T) + 4(G+C) °C
Reference: VU handout, Lecture — Restriction Mapping; Restriction Enzymes. Page — not confirmed in source.

Q4. The GC content of the DNA sequence 5'-GCGCATATGC-3' is:
A) 40%
B) 50%
C) 60%
D) 70%
Correct answer: C — 60%
Reference: VU handout, Lecture — Sequence Composition. Page — not confirmed in source.

Q5. The reverse complement of the DNA sequence 5'-ATGCGT-3' is:
A) 5'-ATGCGT-3'
B) 5'-TACGCA-3'
C) 5'-ACGCAT-3'
D) 5'-TGCGTA-3'
Correct answer: C — 5'-ACGCAT-3'
Reference: VU handout, Lecture — Sequence Manipulation; Reverse Complement. Page — not confirmed in source.

Q6. A GenBank flatfile differs from a FASTA file in that the GenBank file:
A) Contains only the raw sequence with no annotation
B) Contains annotation and feature information (source, CDS, references) as well as the sequence
C) Stores protein 3D coordinates
D) Cannot be read by any program
Correct answer: B — Contains annotation and feature information (source, CDS, references) as well as the sequence
Reference: VU handout, Lecture — Sequence File Formats; GenBank Format. Page — not confirmed in source.

Q7. SRS (Sequence Retrieval System) is:
A) A database of protein structures
B) A system for integrated retrieval of entries from many different biological databases
C) A sequence alignment algorithm
D) A primer-design tool
Correct answer: B — A system for integrated retrieval of entries from many different biological databases
Reference: VU handout, Lecture — Data Retrieval; SRS. Page — not confirmed in source.

Q8. In Unix/Linux, which command searches a file for lines matching a pattern — for example, finding all FASTA headers in a sequence file?
A) sort
B) grep
C) chmod
D) mkdir
Correct answer: B — grep
Reference: VU handout, Lecture — Unix for Bioinformatics. Page — not confirmed in source.

Q9. Which SQL statement retrieves all rows from a table called genes where the organism is human?
A) INSERT INTO genes VALUES ('human')
B) SELECT * FROM genes WHERE organism = 'human'
C) DELETE FROM genes WHERE organism = 'human'
D) UPDATE genes SET organism = 'human'
Correct answer: B — SELECT * FROM genes WHERE organism = 'human'
Reference: VU handout, Lecture — Biological Databases; Relational Databases and SQL. Page — not confirmed in source.

Q10. How many codons in the standard genetic code are sense codons, i.e. codons that specify amino acids?
A) 20
B) 61
C) 64
D) 3
Correct answer: B — 61
Reference: VU handout, Lecture — The Genetic Code. Page — not confirmed in source.

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3. Midterm Subjective

Q1. What is the central dogma of molecular biology? Why did biology need computing — that is, why did bioinformatics arise?
Answer: The central dogma (Crick) states that genetic information flows from DNA to RNA to protein: DNA is transcribed into messenger RNA, and mRNA is translated into protein (with reverse transcription, RNA → DNA, as a known exception in retroviruses). Bioinformatics arose because modern biology produces data far faster than humans can analyse by hand: a single genome contains billions of bases, sequencing projects generate terabytes of data, and databases such as GenBank double in size roughly every 18 months. Computers are needed to store the data, search it (e.g. find a gene in a genome), compare sequences, predict structures and make sense of it all. Without computing, the Human Genome Project and modern genomics would be impossible.
Reference: VU handout, Lecture — Introduction to Bioinformatics; the Central Dogma. Page — not confirmed in source.

Q2. Classify biological databases into primary, secondary and composite databases, giving two examples of each and saying what each stores.
Answer: Primary databases store original experimental data submitted by researchers: e.g. GenBank/ENA/DDBJ (nucleotide sequences) and PDB (experimentally determined 3D structures). Secondary databases store data derived by analysing primary data: e.g. UniProtKB/Swiss-Prot (protein sequences with curated annotation derived from the literature and sequence analysis) and PROSITE (protein motifs/patterns derived from aligned sequences). Composite databases combine data from several primary sources: e.g. NCBI Entrez/RefSeq (RefSeq is a curated, non-redundant collection built from GenBank and other sources) and Ensembl (genome annotation combining sequence, gene models and comparative data). The distinction matters because primary data is archival and uncurated, while secondary and composite resources add value through curation and integration.
Reference: VU handout, Lecture — Biological Databases; Database Classification. Page — not confirmed in source.

Q3. Compare Perl and Python as languages for bioinformatics. What is a regular expression, and give one example of its use on a sequence file.
Answer: Perl was the first dominant bioinformatics language: it is excellent at text processing, has powerful built-in regular expressions, and has the BioPerl library of sequence-handling modules; its syntax can be hard to read in large programs. Python is now the most widely used: it is readable, has the Biopython library (Bio.SeqIO, Bio.Blast, Bio.PDB), strong scientific libraries (NumPy, pandas), and is used in most modern pipelines. Both are interpreted, cross-platform and good for 'glue' code that connects tools. A regular expression is a pattern that describes a set of strings: e.g. the pattern ^> matches lines beginning with > — the header lines of a FASTA file — so grep '^>' file.fasta lists all sequence names in the file. Another example, [ATGC]+, matches a run of DNA bases.
Reference: VU handout, Lecture — Programming for Bioinformatics; Perl, Python and Regular Expressions. Page — not confirmed in source.

Q4. Explain the genetic code: how many codons are there, what are the start and stop codons, and what does 'degenerate' mean? Why is the code (almost) universal?
Answer: The genetic code maps 64 triplet codons (4³ combinations of A, U, G, C) to 20 amino acids plus stop signals: 61 sense codons and 3 stop codons (UAA, UAG, UGA). The usual start codon is AUG, which also codes for methionine. The code is degenerate (redundant): most amino acids are specified by more than one codon — leucine, serine and arginine have six codons each — while each codon specifies only one amino acid (the code is unambiguous). It is (almost) universal: the same codons mean the same amino acids in nearly all organisms, from bacteria to humans, which is strong evidence for a common origin of life and is what makes genetic engineering and cross-species gene transfer possible. A few exceptions exist (e.g. mitochondrial codes differ slightly).
Reference: VU handout, Lecture — The Genetic Code. Page — not confirmed in source.

Q5. Compare the FASTA, GenBank and EMBL sequence file formats. When would you use each one?
Answer: FASTA is the simplest format: a header line beginning with > (identifier and description) followed by the raw sequence on one or more lines. It carries no annotation, so it is the standard input for alignment and search programs (BLAST, Clustal) and for sharing plain sequences. A GenBank flatfile is a richly annotated record: a header (LOCUS, DEFINITION, ACCESSION, SOURCE, REFERENCES), a FEATURES table (source, gene, CDS with locations and translation), and the sequence in the ORIGIN section — used when the annotation matters, e.g. downloading a gene with its coding regions. EMBL format is the European equivalent of GenBank (from ENA), with the same idea: an ID line, feature table and sequence, used for data exchanged through the INSDC collaboration. In short: FASTA for computation, GenBank/EMBL flatfiles for annotated records and archival exchange.
Reference: VU handout, Lecture — Sequence File Formats. Page — not confirmed in source.

4. Finalterm Subjective

Q1. What is an open reading frame (ORF)? Describe the steps of finding ORFs in a DNA sequence, and explain six-frame translation.
Answer: An ORF is a stretch of DNA that starts with a start codon (ATG in DNA), continues with codons, and ends with an in-frame stop codon (TAA, TAG or TGA), with no stop codon inside. To find ORFs: (1) read the sequence in all three forward reading frames (starting at positions 1, 2 and 3); (2) in each frame, scan for a start codon, then continue until the first in-frame stop codon — that span is an ORF; (3) repeat on the reverse-complement strand, which gives the other three frames — six frames in total, because DNA is double-stranded and either strand may be the coding strand; (4) translate each ORF to protein and keep the longest ones as candidate genes; (5) check the candidates against protein databases (e.g. by BLAST) for supporting evidence. Six-frame translation is therefore the standard first step in finding genes in an unannotated genome: a real gene must appear as a long ORF in one of the six frames, while random DNA produces only short ORFs.
Reference: VU handout, Lecture — Gene Finding; ORFs and Six-Frame Translation. Page — not confirmed in source.

Q2. What are the principles of PCR primer design? Calculate the melting temperature of the primer 5'-GCGTATACGA-3' by the Wallace rule.
Answer: A good primer is typically 18–24 bases long; has 40–60% GC content; has a melting temperature (Tm) of about 55–65 °C, with the two primers of a pair within ~5 °C of each other; avoids runs of the same base, self-complementarity (which causes hairpins) and complementarity between the two primers (which causes primer-dimers); has a GC-rich 3' end ('GC clamp') so the 3' end binds firmly, because extension starts there; and is unique in the template (checked by BLAST) so it amplifies only the intended target. Wallace rule: Tm ≈ 2(A+T) + 4(G+C). For 5'-GCGTATACGA-3' (10 bases): G,C,G,T,A,T,A,C,G,A — count G/C = 5 (G,C,G,C,G) and A/T = 5, so Tm ≈ 2×5 + 4×5 = 10 + 20 = 30 °C. This is a very short primer, so its Tm is low; a real PCR primer of ~20 bases would give a Tm in the useful 55–65 °C range.
Reference: VU handout, Lecture — Primer Design. Page — not confirmed in source.

Q3. What is reverse complementing, and why is it needed? What is in-silico restriction mapping, and how is it done?
Answer: DNA is double-stranded and antiparallel: the two strands run in opposite directions and pair A–T, G–C. The reverse complement of a sequence is its complement read backwards (e.g. 5'-ATGCGT-3' → complement 3'-TACGCA-5' → written 5'→3' as 5'-ACGCAT-3'). It is needed whenever we work with the opposite strand — for example, a gene may be encoded on the strand we have not sequenced, primers must match the correct strand, and ORF finding must scan both strands. In-silico restriction mapping means predicting where restriction enzymes cut a DNA sequence on the computer instead of in the lab: the program scans the sequence (and its reverse complement) for each enzyme's recognition site (e.g. GAATTC for EcoRI, GGATCC for BamHI), records the cut positions, and reports the fragment sizes that a digest would produce. It is used to plan cloning, to verify a plasmid map, and to choose enzymes that cut a vector once but not inside an insert.
Reference: VU handout, Lecture — Sequence Manipulation; Restriction Mapping. Page — not confirmed in source.

Q4. Describe how you would retrieve a gene sequence from a public database: compare the Entrez and SRS retrieval systems.
Answer: A typical workflow: (1) search by keyword, accession number or organism (e.g. in NCBI Entrez, type the gene name and filter to the Nucleotide database); (2) inspect the returned records and pick the right one (correct organism, complete CDS); (3) download the record in FASTA format (sequence only) or GenBank format (with annotation); (4) use the accession number to cite the exact version. Entrez is NCBI's system: it federates many NCBI databases (PubMed, Nucleotide, Protein, Structure, Gene) and links related records — from a gene you can jump to its protein, its structure and the papers about it. SRS (Sequence Retrieval System, from EBI) is the European counterpart: it indexes many databanks (EMBL, Swiss-Prot, PDB, PROSITE, etc.) under one query language and also links entries across databases. Both solve the same problem — one entry point to many databases with cross-links — but they cover different (overlapping) database collections and use different query interfaces.
Reference: VU handout, Lecture — Data Retrieval; Entrez and SRS. Page — not confirmed in source.

Q5. Why are biological data stored in relational databases? Write an SQL query that finds all human protein-coding genes on chromosome 7, and explain each part.
Answer: Biological data is highly structured and heavily cross-linked: a gene has a sequence, a location, a function, a protein product, literature references and links to other databases. A relational database stores this in tables with defined relationships, so queries can combine tables (e.g. genes joined to proteins), data is stored once (no duplication), integrity rules prevent inconsistent entries, and indexes make searching millions of records fast. Example query:
SELECT gene_name, start_pos, end_pos FROM genes WHERE organism = 'human' AND chromosome = 7 AND biotype = 'protein_coding';
SELECT gene_name, start_pos, end_pos chooses the columns returned; FROM genes names the table; WHERE organism = 'human' keeps only human rows; AND chromosome = 7 restricts to chromosome 7; AND biotype = 'protein_coding' keeps only protein-coding genes. Real databases such as Ensembl expose exactly this kind of schema (genes, transcripts, proteins tables) and can be queried with SQL or through their APIs.
Reference: VU handout, Lecture — Biological Databases; Relational Databases and SQL. Page — not confirmed in source.