A comparison of strategies for generating artificial replicates in RNA-seq experiments.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Technical replicates are crucial for RNA-seq reproducibility but are often omitted due to cost.
• Artificial replicate generation methods include bootstrapping reads, column bootstrapping, and data mixing.
• Evaluating these methods is essential for reliable differential expression and gene-set enrichment analysis.

Purpose of the Study:

• To compare the effectiveness of different artificial replicate generation strategies for RNA-seq data.
• To determine which method best reflects the reproducibility of differential expression and gene-set enrichment analyses.
• To assess the suitability of these methods under controlled experimental conditions.

Main Methods:

• Performed a virus infection RNA-seq experiment with true technical replicates (paired sequencing).
• Generated artificial replicates using FASTQ read bootstrapping, column bootstrapping, and data mixing.
• Conducted differential expression and Gene Ontology (GO) term enrichment analyses on true and artificial replicates.

Main Results:

• FASTQ read bootstrapping produced results (p-values, fold changes) closest to true replicates.
• Column bootstrapping and data mixing yielded results less similar to true replicates.
• True replicates showed less overlap in identified genes/GO terms than artificial replicates from column bootstrap or data mixing.

Conclusions:

• FASTQ read bootstrapping is superior for studying RNA-seq reproducibility in differential expression and GO analysis.
• While computationally intensive, FASTQ bootstrapping offers a more accurate simulation of technical variability.
• The findings suggest FASTQ bootstrapping's applicability to other high-throughput sequencing analyses.