KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNA-seq) generates large k-mer count tables from multi-sample data.
• Identifying condition-specific or differentially expressed sequences is crucial for biological insights.
• Existing methods may rely on gene or transcript annotations, limiting discovery.

Purpose of the Study:

• To introduce KaMRaT, a novel computational tool for processing large k-mer count tables from multi-sample RNA-seq data.
• To enable the identification of condition-specific or differentially expressed sequences irrespective of gene annotation.

Main Methods:

• KaMRaT is implemented in C++ for efficient processing.
• Key functions include scoring k-mers based on count statistics.
• The tool merges overlapping k-mers into contigs and selects sequences based on sample occurrence.

Main Results:

• KaMRaT effectively processes large k-mer datasets.
• The software facilitates the identification of condition-specific sequences.
• Differential expression analysis is achievable without relying on prior gene annotation.

Conclusions:

• KaMRaT provides a robust solution for analyzing RNA-seq k-mer data.
• The tool enhances the discovery of novel, unannotated sequences with differential expression patterns.
• Availability of source code promotes accessibility and further development.