Genome annotation techniques MCQs With Answer

Genome annotation techniques MCQs With Answer

This quiz set is designed for M. Pharm students studying bioinformatics and computational biotechnology. Genome annotation connects raw DNA sequences to biological meaning by identifying genes, non-coding elements, regulatory regions and assigning likely functions. These questions focus on both structural and functional annotation methods, including ab initio prediction, homology-based approaches, transcriptome-assisted annotation, domain detection with HMMs, repeat masking, annotation file formats and pipelines. Emphasis is placed on practical tools and interpretation of results (BLAST, Pfam, tRNAscan-SE, RepeatMasker, MAKER, GFF3) and on assessing annotation quality. The questions aim to deepen conceptual understanding and prepare students for real-world annotation tasks in pharmaceutical genomics and molecular research.

Q1. What is the primary goal of genome annotation?

• To sequence the entire genome using high-throughput methods
• To identify and describe genomic elements such as genes, exons, regulatory regions and other features
• To perform phylogenetic analysis of species
• To synthesize proteins predicted from a genome

Correct Answer: To identify and describe genomic elements such as genes, exons, regulatory regions and other features

Q2. Which statement best distinguishes structural annotation from functional annotation?

• Structural annotation assigns gene function; functional annotation locates gene coordinates
• Structural annotation locates genomic features like exons and introns; functional annotation assigns biological roles to those features
• Both terms mean the same and are used interchangeably
• Structural annotation predicts protein 3D structure; functional annotation predicts metabolic pathways

Correct Answer: Structural annotation locates genomic features like exons and introns; functional annotation assigns biological roles to those features

Q3. Ab initio gene prediction methods primarily rely on which information?

• Experimental protein expression data from mass spectrometry
• Intrinsic sequence signals such as codon usage, start/stop codons and splice site motifs
• Functional annotations from orthologous genes in other species
• Pathway databases like KEGG

Correct Answer: Intrinsic sequence signals such as codon usage, start/stop codons and splice site motifs

Q4. Which approach uses sequence similarity to previously annotated genes to predict gene models in a new genome?

• Ab initio prediction
• Homology-based annotation (similarity searches such as BLAST)
• De novo transcriptome assembly without reference
• Repeat masking

Correct Answer: Homology-based annotation (similarity searches such as BLAST)

Q5. Why are prokaryotic genomes generally easier to annotate for coding sequences than eukaryotic genomes?

• Prokaryotes lack regulatory regions entirely
• Prokaryotic genes usually lack introns and have continuous open reading frames
• Prokaryotes have smaller genomes so no annotation is needed
• Eukaryotes do not have start and stop codons

Correct Answer: Prokaryotic genes usually lack introns and have continuous open reading frames

Q6. What canonical dinucleotide motif is most commonly used by gene prediction tools to identify eukaryotic intron boundaries?

• AA-TT
• GT-AG
• CC-GG
• AT-AC

Correct Answer: GT-AG

Q7. Hidden Markov Models (HMMs) are widely used in annotation pipelines for which purpose?

• Sequencing raw reads into contigs
• Detecting conserved protein domains and family profiles (e.g., Pfam)
• Performing metabolic flux analysis
• Masking repetitive DNA prior to assembly

Correct Answer: Detecting conserved protein domains and family profiles (e.g., Pfam)

Q8. Which specialized tool is commonly used to identify transfer RNA (tRNA) genes in genomic sequences?

• RepeatMasker
• tRNAscan-SE
• GATK
• MAKER

Correct Answer: tRNAscan-SE

Q9. Which tool is designed to identify and mask interspersed repeats and low complexity regions in genomic sequences before annotation?

• BLAST
• RepeatMasker
• InterProScan
• Trinity

Correct Answer: RepeatMasker

Q10. Which file format is most commonly used to represent genomic feature coordinates (gene models, exons) for genome browsers and pipelines?

• FASTA
• GFF3 (General Feature Format version 3)
• VCF
• PDB

Correct Answer: GFF3 (General Feature Format version 3)

Q11. What is the main advantage of annotation pipelines such as MAKER in eukaryotic genome projects?

• They perform de novo genome assembly from raw reads
• They integrate ab initio predictions, protein homology and RNA evidence to produce consensus gene models
• They only use ab initio predictions to ensure independence from external data
• They exclusively annotate microbial genomes

Correct Answer: They integrate ab initio predictions, protein homology and RNA evidence to produce consensus gene models

Q12. How does RNA-seq data most directly improve genome annotation?

• By masking repetitive elements in the genome
• By providing transcript evidence for exon boundaries, splice variants and expression-supported gene models
• By predicting protein tertiary structures
• By identifying protein domains using HMMs

Correct Answer: By providing transcript evidence for exon boundaries, splice variants and expression-supported gene models

Q13. Which database is primarily used to identify conserved protein domains during functional annotation?

• KEGG
• Pfam
• GENBANK raw reads
• GFF3

Correct Answer: Pfam

Q14. In comparative annotation, what defines orthologous genes?

• Genes within the same genome that result from gene duplication
• Genes in different species that diverged by a speciation event and often retain similar functions
• Non-coding RNAs that regulate gene expression
• Genes that are only found in prokaryotes

Correct Answer: Genes in different species that diverged by a speciation event and often retain similar functions

Q15. Which metric describes the proportion of predicted annotations that are true positives?

• Sensitivity (recall)
• Specificity
• Precision (positive predictive value)
• False discovery rate

Correct Answer: Precision (positive predictive value)

Q16. Proteogenomics contributes to genome annotation by using which experimental data?

• Chromatin immunoprecipitation sequencing (ChIP-seq)
• Mass spectrometry-derived peptide evidence to confirm or refine coding regions
• Single nucleotide polymorphism arrays
• Electron microscopy images

Correct Answer: Mass spectrometry-derived peptide evidence to confirm or refine coding regions

Q17. Which tool or resource is specialized for predicting the effect of genomic variants on genes and proteins?

• SnpEff
• RepeatMasker
• tRNAscan-SE
• ClustalW

Correct Answer: SnpEff

Q18. What is a primary benefit of manual curation in genome annotation despite the availability of automated pipelines?

• Manual curation is faster than automated methods
• Manual curation can resolve complex or ambiguous gene models and improve annotation accuracy
• Manual curation eliminates the need for experimental validation
• Manual curation prevents any future updates to the annotation

Correct Answer: Manual curation can resolve complex or ambiguous gene models and improve annotation accuracy

Q19. Which feature is most indicative of a pseudogene in a genome annotation?

• An intact open reading frame with conserved domains
• Presence of premature stop codons, frameshifts or truncation relative to functional homologs
• High expression levels in RNA-seq data
• Conserved splice junctions and full-length transcripts

Correct Answer: Presence of premature stop codons, frameshifts or truncation relative to functional homologs

Q20. Best practice for maintaining high-quality genome annotation over time is to:

• Never change annotations once published to preserve original data
• Regularly update annotations using new assemblies, transcriptomic/proteomic evidence and improved algorithms
• Rely solely on ab initio predictors developed ten years ago
• Annotate only coding sequences and ignore non-coding elements indefinitely

Correct Answer: Regularly update annotations using new assemblies, transcriptomic/proteomic evidence and improved algorithms

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