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

• Published in: BMC bioinformatics Vol. 23; no. 1; pp. 39 - 21
• Main Authors: Hita, Andrea, Brocart, Gilles, Fernandez, Ana, Rehmsmeier, Marc, Alemany, Anna, Schvartzman, Sol
• Format: Journal Article
• Language: English
• Published: London BioMed Central 14.01.2022; Springer Nature B.V; BMC
• Subjects: Algorithms; Alignment; Annotations; Bioinformatics; Biomedical and Life Sciences; Biotypes; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer applications; Computer programs; Copy number; Experiments; Gene expression; Gene mapping; Gene sequencing; Genomics; Life Sciences; Microarrays; MicroRNAs; NGS; Non-coding; Pipelines; Proteins; Quantification; Ribonucleic acid; RNA; RNA-Seq; Sequence Analysis, RNA; Small RNA; Software; Source code; Transcription; Transcriptome; Transcriptomes; Transcriptomics; Map equation; Alignment; RNA-seq; Quantification; Multi-mapping; Small RNA; Non-coding; NGS; Multi-overlapping; Counting; Single-cell
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04544-3

Background Total-RNA sequencing (total-RNA-seq) allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines have traditionally focused on particular biotypes, making assumptions that are not fullfilled by total-RNA-seq datasets. Transcripts from distinct RNA biotypes vary in length, biogenesis, and function, can overlap in a genomic region, and may be present in the genome with a high copy number. Consequently, reads from total-RNA-seq libraries may cause ambiguous genomic alignments, demanding for flexible quantification approaches. Results Here we present Multi-Graph count (MGcount), a total-RNA-seq quantification tool combining two strategies for handling ambiguous alignments. First, MGcount assigns reads hierarchically to small-RNA and long-RNA features to account for length disparity when transcripts overlap in the same genomic position. Next, MGcount aggregates RNA products with similar sequences where reads systematically multi-map using a graph-based approach. MGcount outputs a transcriptomic count matrix compatible with RNA-sequencing downstream analysis pipelines, with both bulk and single-cell resolution, and the graphs that model repeated transcript structures for different biotypes. The software can be used as a python module or as a single-file executable program. Conclusions MGcount is a flexible total-RNA-seq quantification tool that successfully integrates reads that align to multiple genomic locations or that overlap with multiple gene features. Its approach is suitable for the simultaneous estimation of protein-coding, long non-coding and small non-coding transcript concentration, in both precursor and processed forms. Both source code and compiled software are available at https://github.com/hitaandrea/MGcount .