Transcriptome overview MCQs With Answer

Transcriptome overview MCQs With Answer

Introduction: This quiz set on transcriptomics is designed specifically for M.Pharm students studying Bioinformatics and Computational Biotechnology. It covers core concepts of the transcriptome, experimental and computational methods for measuring RNA expression, data preprocessing, quantification metrics, differential expression analysis, and advanced topics such as single-cell and long-read RNA sequencing. Questions emphasize practical considerations in experimental design, library preparation, mapping/assembly strategies, normalization, and statistical testing relevant to pharmaceutical research. Use these MCQs to reinforce conceptual understanding and prepare for applied tasks such as interpreting RNA‑Seq data in drug discovery, biomarker identification, and pharmacogenomics studies.

Q1. What is the most accurate description of the transcriptome?

• The complete set of DNA sequences in a cell
• The set of all RNA molecules transcribed in a cell or tissue at a given time
• All proteins expressed by a cell
• The collection of metabolites present in a cell

Correct Answer: The set of all RNA molecules transcribed in a cell or tissue at a given time

Q2. Which RNA‑Seq library preparation method enriches for mature polyadenylated mRNA?

• rRNA depletion
• Poly(A) selection
• Small RNA enrichment
• Random hexamer depletion

Correct Answer: Poly(A) selection

Q3. Which molecule is typically removed to increase the fraction of informative reads in total RNA sequencing?

• mRNA
• tRNA
• rRNA
• snRNA

Correct Answer: rRNA

Q4. In RNA‑Seq quantification, what does TPM stand for and why is it preferred over RPKM for comparing across samples?

• Transcript Percentage Metric; it normalizes for transcript GC content
• Transcripts Per Million; it normalizes per sample so sum of values is comparable across samples
• Reads Per Kilobase per Million; it corrects for sequencing errors
• Total Phred Mean; it estimates read quality across libraries

Correct Answer: Transcripts Per Million; it normalizes per sample so sum of values is comparable across samples

Q5. Which alignment‑free tool uses pseudoalignment and k‑mers for fast transcript quantification?

• BWA
• STAR
• Kallisto
• TopHat

Correct Answer: Kallisto

Q6. When analyzing differential expression with count data, which statistical distribution is most commonly used to model read counts?

• Normal distribution
• Poisson distribution
• Negative binomial distribution
• Uniform distribution

Correct Answer: Negative binomial distribution

Q7. What is the primary purpose of read quality control tools like FASTQC before downstream transcriptome analysis?

• To align reads to the genome
• To assess base quality, adapter contamination, and GC bias
• To quantify gene expression directly
• To perform differential expression testing

Correct Answer: To assess base quality, adapter contamination, and GC bias

Q8. Which approach is appropriate when no reference genome is available for transcriptome reconstruction?

• Reference-guided assembly with STAR
• De novo transcriptome assembly (e.g., Trinity)
• Direct quantification using Salmon
• Poly(A) selection

Correct Answer: De novo transcriptome assembly (e.g., Trinity)

Q9. In differential expression analysis, what does FDR control for?

• False detection rate of sequencing errors
• False discovery rate of multiple hypothesis testing
• Fragment duplication rate in libraries
• Frequency distribution range of counts

Correct Answer: False discovery rate of multiple hypothesis testing

Q10. Which normalization method is designed to account for compositional differences and extreme expression values between RNA‑Seq samples?

• Total count scaling
• Trimmed Mean of M-values (TMM)
• GC content normalization only
• Median polishing

Correct Answer: Trimmed Mean of M-values (TMM)

Q11. What experimental factor is most critical to increase statistical power for detecting differential expression?

• Having more technical replicates instead of biological replicates
• Increasing read length only
• Increasing the number of biological replicates
• Sequencing more different cell types

Correct Answer: Increasing the number of biological replicates

Q12. Which of the following best describes alternative splicing’s impact on the transcriptome?

• Alternative splicing reduces transcript diversity
• It generates multiple transcript isoforms from a single gene, increasing proteomic and regulatory complexity
• Alternative splicing only occurs in prokaryotes
• It eliminates untranslated regions to shorten transcripts

Correct Answer: It generates multiple transcript isoforms from a single gene, increasing proteomic and regulatory complexity

Q13. For single‑cell RNA‑Seq, which challenge is especially prominent compared to bulk RNA‑Seq?

• Lower per-cell sequencing depth and higher dropout rate
• Easier mapping due to fewer reads
• No need for normalization across cells
• Higher rRNA contamination in single cells

Correct Answer: Lower per-cell sequencing depth and higher dropout rate

Q14. Which sequencing platform is most advantageous for full-length isoform sequencing and direct isoform phasing?

• Short‑read Illumina
• Microarray hybridization
• Long‑read platforms such as PacBio or Oxford Nanopore
• Sanger sequencing of genomic DNA

Correct Answer: Long‑read platforms such as PacBio or Oxford Nanopore

Q15. What is strand-specific (stranded) RNA‑Seq useful for in transcriptome analysis?

• Distinguishing sense and antisense transcript expression
• Increasing read length
• Removing ribosomal RNA
• Measuring protein abundance directly

Correct Answer: Distinguishing sense and antisense transcript expression

Q16. Which metric directly reflects sequencing library complexity and duplication levels?

• FPKM
• Duplication rate or unique molecular identifier (UMI) counts
• TPM only
• RPKM normalized by gene length

Correct Answer: Duplication rate or unique molecular identifier (UMI) counts

Q17. Why is batch effect correction important in transcriptome studies?

• Batch effects always improve signal and should be amplified
• To remove biological variation so only technical noise remains
• To mitigate non-biological systematic differences that can confound differential expression
• Because normalization is unnecessary after batch correction

Correct Answer: To mitigate non-biological systematic differences that can confound differential expression

Q18. Which downstream analysis helps to visualize sample relationships and major sources of variation in transcriptome datasets?

• Heatmap of random reads
• PCA (Principal Component Analysis)
• Sanger sequencing chromatogram inspection
• Adapter trimming

Correct Answer: PCA (Principal Component Analysis)

Q19. In the context of transcript annotation, what is a major benefit of using a well‑curated reference annotation?

• It eliminates the need for sequencing
• It improves accuracy of transcript quantification and splice junction assignment
• It increases raw read quality scores
• It prevents the discovery of novel isoforms

Correct Answer: It improves accuracy of transcript quantification and splice junction assignment

Q20. Which practice reduces bias introduced by highly expressed genes when comparing expression profiles across samples?

• Sequencing only highly expressed genes
• Using normalization methods that account for library composition, such as TMM or DESeq2’s median ratio
• Discarding low‑expression genes entirely without thresholding
• Relying solely on raw read counts for comparison

Correct Answer: Using normalization methods that account for library composition, such as TMM or DESeq2’s median ratio

Download