Ultrafast functional profiling of RNA-seq data for nonmodel organisms.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA-sequencing (RNA-seq) analysis is computationally intensive, especially for nonmodel organisms lacking reference genomes.
• Traditional methods often require transcriptome de novo assembly, which is time-consuming and resource-heavy.

Purpose of the Study:

• To develop an ultrafast, all-in-one tool for direct functional quantification of RNA-seq reads.
• To overcome the computational bottleneck in analyzing RNA-seq data for organisms without reference genomes.

Main Methods:

• Seq2Fun performs raw read quality control, including error correction and poly(A) tail removal.
• It translates RNA-seq reads into amino acid fragments for homology searches against a protein database.
• The pipeline avoids intermediate file I/O for efficient processing.

Main Results:

• Seq2Fun achieves functional quantification directly from RNA-seq reads, eliminating the need for de novo assembly.
• The tool processes over 2,000,000 reads per minute, exceeding conventional methods by over 120 times.
• High accuracy was maintained across various test datasets.

Conclusions:

• Seq2Fun significantly accelerates RNA-seq data analysis for nonmodel organisms.
• The tool provides a computationally efficient and accurate alternative for functional gene quantification.
• Seq2Fun enables broader genomic research on organisms lacking reference genomes.