Accurate assembly of multiple RNA-seq samples with Aletsch

• Published in: Bioinformatics (Oxford, England) Vol. 40; no. Supplement_1; pp. i307 - i317
• Main Authors: Shi, Qian, Zhang, Qimin, Shao, Mingfu
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 28.06.2024; Oxford Publishing Limited (England)
• Subjects: Adaptability; Algorithms; Apexes; Assembling; Availability; Chromosomes; Datasets; Decoding; Decomposition; Gene sequencing; Graph theory; High-Throughput Nucleotide Sequencing - methods; Humans; Ribonucleic acid; RNA; RNA-Seq - methods; Sequence Analysis, RNA - methods; Software
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btae215

Motivation High-throughput RNA sequencing has become indispensable for decoding gene activities, yet the challenge of reconstructing full-length transcripts persists. Traditional single-sample assemblers frequently produce fragmented transcripts, especially in single-cell RNA-seq data. While algorithms designed for assembling multiple samples exist, they encounter various limitations. Results We present Aletsch, a new assembler for multiple bulk or single-cell RNA-seq samples. Aletsch incorporates several algorithmic innovations, including a “bridging” system that can effectively integrate multiple samples to restore missed junctions in individual samples, and a new graph-decomposition algorithm that leverages “supporting” information across multiple samples to guide the decomposition of complex vertices. A standout feature of Aletsch is its application of a random forest model with 50 well-designed features for scoring transcripts. We demonstrate its robust adaptability across different chromosomes, datasets, and species. Our experiments, conducted on RNA-seq data from several protocols, firmly demonstrate Aletsch’s significant outperformance over existing meta-assemblers. As an example, when measured with the partial area under the precision-recall curve (pAUC, constrained by precision), Aletsch surpasses the leading assemblers TransMeta by 22.9%–62.1% and PsiCLASS by 23.0%–175.5% on human datasets. Availability and implementation Aletsch is freely available at https://github.com/Shao-Group/aletsch. Scripts that reproduce the experimental results of this manuscript is available at https://github.com/Shao-Group/aletsch-test.