MGcount: a total RNA-seq quantification tool to address multi-mapping and multi-overlapping alignments ambiguity in...

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Total-RNA sequencing (total-RNA-seq) enables simultaneous analysis of coding and non-coding RNA.
• Traditional pipelines struggle with total-RNA-seq data due to varying transcript lengths, biogenesis, and overlapping regions.
• Ambiguous genomic alignments are common in total-RNA-seq, necessitating flexible quantification methods.

Purpose of the Study:

• To develop a flexible computational tool for total-RNA sequencing data.
• To address the challenge of ambiguous alignments in total-RNA-seq datasets.
• To enable accurate quantification of diverse RNA biotypes simultaneously.

Main Methods:

• Introduced Multi-Graph count (MGcount), a novel quantification tool for total-RNA-seq.
• Implemented a hierarchical read assignment strategy for small-RNA and long-RNA features.
• Utilized a graph-based approach to aggregate multi-mapping reads from similar RNA sequences.

Main Results:

• MGcount effectively handles ambiguous alignments by integrating reads across different genomic locations and overlapping features.
• The tool produces a transcriptomic count matrix compatible with downstream RNA-sequencing analysis.
• MGcount provides both bulk and single-cell resolution, along with graphs modeling transcript structures.

Conclusions:

• MGcount offers a flexible solution for total-RNA-seq quantification, integrating diverse RNA biotypes.
• The tool accurately estimates concentrations of protein-coding, long non-coding, and small non-coding transcripts.
• MGcount is available as a Python module or executable program with source code provided.