cloudrnaSPAdes: isoform assembly using bulk barcoded RNA sequencing data.

TitlecloudrnaSPAdes: isoform assembly using bulk barcoded RNA sequencing data.
Publication TypeJournal Article
Year of Publication2024
AuthorsMeleshko D, Prjbelski AD, Raiko M, Tomescu AI, Tilgner H, Hajirasouliha I
Date Published2024 Feb 01
KeywordsGenomics, High-Throughput Nucleotide Sequencing, Humans, Protein Isoforms, RNA, RNA-Seq, Sequence Analysis, RNA, Transcriptome

MOTIVATION: Recent advancements in long-read RNA sequencing have enabled the examination of full-length isoforms, previously uncaptured by short-read sequencing methods. An alternative powerful method for studying isoforms is through the use of barcoded short-read RNA reads, for which a barcode indicates whether two short-reads arise from the same molecule or not. Such techniques included the 10x Genomics linked-read based SParse Isoform Sequencing (SPIso-seq), as well as Loop-Seq, or Tell-Seq. Some applications, such as novel-isoform discovery, require very high coverage. Obtaining high coverage using long reads can be difficult, making barcoded RNA-seq data a valuable alternative for this task. However, most annotation pipelines are not able to work with a set of short reads instead of a single transcript, also not able to work with coverage gaps within a molecule if any. In order to overcome this challenge, we present an RNA-seq assembler that allows the determination of the expressed isoform per barcode.

RESULTS: In this article, we present cloudrnaSPAdes, a tool for assembling full-length isoforms from barcoded RNA-seq linked-read data in a reference-free fashion. Evaluating it on simulated and real human data, we found that cloudrnaSPAdes accurately assembles isoforms, even for genes with high isoform diversity.

AVAILABILITY AND IMPLEMENTATION: cloudrnaSPAdes is a feature release of a SPAdes assembler and version used for this article is available at

Alternate JournalBioinformatics
PubMed ID38262343
PubMed Central IDPMC10868327
Grant ListR35 GM138152 / GM / NIGMS NIH HHS / United States