Gene prediction methods MCQs With Answer

Gene prediction methods MCQs With Answer is a focused quiz set designed for M.Pharm students studying Bioinformatics and Computational Biotechnology. This collection explains core principles behind computational gene finding, contrasting ab initio models with evidence-based approaches, and highlighting practical challenges such as splice-site detection, alternative splicing, pseudogene discrimination, and the use of RNA‑Seq or comparative genomics as supporting evidence. Each question probes conceptual understanding and applied reasoning—covering algorithms (HMMs, neural nets), prominent gene-finders, evaluation metrics and annotation pipelines—so you can confidently evaluate and interpret gene predictions in pharmacogenomics, drug-target discovery and downstream genomic annotation tasks.

Q1. Which fundamental difference best distinguishes ab initio gene prediction methods from homology-based methods?

• Ab initio methods rely solely on sequence-derived signals and content patterns, whereas homology-based methods use similarity to known genes.
• Ab initio methods only work on prokaryotes, while homology-based methods only work on eukaryotes.
• Ab initio methods require RNA-Seq data, while homology-based methods do not.
• Ab initio methods always produce fewer false positives than homology-based methods.

Correct Answer: Ab initio methods rely solely on sequence-derived signals and content patterns, whereas homology-based methods use similarity to known genes.

Q2. Which algorithmic framework is most commonly used to model exon–intron structure and state transitions in many ab initio gene finders?

• Hidden Markov Models (HMMs)
• k-means clustering
• Principal component analysis (PCA)
• Minimum spanning tree

Correct Answer: Hidden Markov Models (HMMs)

Q3. In eukaryotic gene prediction, which short conserved motif is most frequently used to identify canonical splice donor and acceptor sites?

• GT–AG rule (donor GT, acceptor AG)
• TATA–TATA rule
• Poly-A tail motif AATAAA
• CAAT box

Correct Answer: GT–AG rule (donor GT, acceptor AG)

Q4. Which characteristic is a major challenge for predicting small exons in eukaryotic genomes?

• Their short length makes composition and signal statistics insufficiently distinct from background.
• They always lack canonical splice sites.
• They are rich in CpG islands that confuse models.
• They produce no open reading frame (ORF).

Correct Answer: Their short length makes composition and signal statistics insufficiently distinct from background.

Q5. Which gene-finding tool is historically optimized for prokaryotic genomes and uses interpolated Markov models for coding region prediction?

• Glimmer
• AUGUSTUS
• SNAP
• MAKER

Correct Answer: Glimmer

Q6. What is the main advantage of integrating RNA‑Seq data into gene prediction pipelines?

• It provides direct transcript evidence: exon boundaries and splice junctions to refine models.
• It eliminates all need for ab initio prediction.
• It guarantees perfect full-length transcript assembly without errors.
• It increases GC content, improving prediction quality.

Correct Answer: It provides direct transcript evidence: exon boundaries and splice junctions to refine models.

Q7. Which metric describes the proportion of true exons correctly predicted by a gene finder?

• Sensitivity (recall)
• Precision (positive predictive value)
• Specificity (true negative rate)
• FDR (false discovery rate)

Correct Answer: Sensitivity (recall)

Q8. When annotating a eukaryotic genome, why is repeat masking (e.g., with RepeatMasker) typically performed before gene prediction?

• To prevent repetitive elements from being misidentified as protein-coding exons.
• To remove all non-coding RNAs from the genome sequence permanently.
• To annotate promoters and enhancers prior to gene finding.
• To convert intronic sequences into exons.

Correct Answer: To prevent repetitive elements from being misidentified as protein-coding exons.

Q9. Which feature is most useful to distinguish a processed pseudogene from a functional gene during annotation?

• Lack of introns and presence of poly-A tail remnants
• Presence of canonical splice sites
• High expression levels across tissues
• Presence of an upstream TATA box

Correct Answer: Lack of introns and presence of poly-A tail remnants

Q10. Which evidence-based annotation integrator is designed to combine ab initio predictions, protein homology and transcript evidence into a unified gene set?

• MAKER
• Glimmer
• GeneMark.hmm
• BLASTn

Correct Answer: MAKER

Q11. In prokaryotic gene prediction, which sequence feature is commonly used to identify the translation start site upstream of the start codon?

• Shine–Dalgarno ribosome binding site
• Kozak consensus sequence
• TATA box
• Polyadenylation signal AATAAA

Correct Answer: Shine–Dalgarno ribosome binding site

Q12. Which of the following is a common cause of false positive gene predictions in ab initio methods?

• Long open reading frames occurring by chance in non-coding regions
• Excessive use of RNA‑Seq evidence
• Overabundance of homology-based support
• Exclusive use of experimentally validated gene models

Correct Answer: Long open reading frames occurring by chance in non-coding regions

Q13. Why is GC content heterogeneity (isochore structure) important to consider in gene prediction?

• GC-rich and GC-poor regions have different coding statistics and require locale-specific parameterization.
• GC content determines whether a gene uses GT–AG splice sites.
• Only GC-rich regions contain promoters.
• GC heterogeneity prevents the use of RNA‑Seq evidence.

Correct Answer: GC-rich and GC-poor regions have different coding statistics and require locale-specific parameterization.

Q14. What role do codon usage bias and hexamer composition play in ab initio gene prediction?

• They help discriminate coding sequences from non-coding background by characteristic composition patterns.
• They only affect promoter prediction, not coding region detection.
• They are used to predict RNA secondary structure.
• They primarily identify pseudogenes by stop codon enrichment.

Correct Answer: They help discriminate coding sequences from non-coding background by characteristic composition patterns.

Q15. Which prediction difficulty is specifically increased by alternative splicing in eukaryotes?

• Accurately defining all transcript isoforms and exon combinations from genomic sequence alone.
• Identifying Shine–Dalgarno sequences for each isoform.
• Detecting introns because they no longer exist in alternatively spliced genes.
• Predicting protein tertiary structure directly from splice variants.

Correct Answer: Accurately defining all transcript isoforms and exon combinations from genomic sequence alone.

Q16. Which approach improves gene model accuracy by combining predictions from multiple ab initio predictors and external evidence?

• Consensus or ensemble annotation
• Single best ab initio model selection
• Ignoring homology evidence entirely
• Random gene selection

Correct Answer: Consensus or ensemble annotation

Q17. Which evaluation curve is used to visualize the tradeoff between true positive rate and false positive rate in gene prediction assessments?

• Receiver Operating Characteristic (ROC) curve
• Precision–Recall histogram
• Kaplan–Meier survival curve
• Box-and-whisker plot

Correct Answer: Receiver Operating Characteristic (ROC) curve

Q18. When training an ab initio predictor for a newly sequenced organism, what is a critical requirement for accurate parameter estimation?

• A representative, high-quality set of annotated genes or reliable training sequences
• Only repetitive elements are needed for training
• Training must use sequences from distantly related species exclusively
• No training data are ever required for ab initio methods

Correct Answer: A representative, high-quality set of annotated genes or reliable training sequences

Q19. Which signal is most directly used to predict transcription termination and polyadenylation in eukaryotic gene models?

• Polyadenylation signal (e.g., AATAAA)
• Shine–Dalgarno sequence
• Kozak consensus sequence
• GT–AG splice junction

Correct Answer: Polyadenylation signal (e.g., AATAAA)

Q20. How do comparative genomics approaches improve gene prediction accuracy?

• By using conserved synteny and sequence conservation across related species to validate exons and gene boundaries.
• By increasing the error rate through inclusion of unrelated genomes.
• By eliminating the need to detect splice sites.
• By directly converting repeats into coding exons.

Correct Answer: By using conserved synteny and sequence conservation across related species to validate exons and gene boundaries.

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