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GENOME INFORMATICS

Bridge over troubled transcripts

Nature Computational Science volume 2, pages 142–143 (2022)Cite this article

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Scallop2, a tool that enables accurate reference-guided transcriptome assembly, capitalizes on recent developments in short-read sequencing protocols for single-cell RNA sequencing by leveraging multi-end and paired-end information.

Analysis of RNA using high-throughput sequencing (RNA-seq) has become the gold standard experimental assay for profiling the transcriptome. Nowadays, there is a well-established set of computational tools that enable various RNA-seq-related tasks, including gene expression quantification, differential gene expression analyses using bulk RNA-seq data1, and unsupervised clustering and trajectory analysis using single-cell RNA-seq data2. In particular, the ability to profile the transcriptomes of individual cells has allowed for an improved understanding of the heterogeneity of complex tissues3. Typically, each gene is assigned a single value to represent its expression level, but this ignores the diversity of isoforms that can be derived from a single gene: most genes can be transcribed into multiple different isoforms, sometimes with important functional differences4. Inferring the relative isoform levels, both for bulk and single-cell RNA-seq data, using short reads is, however, challenging due to the high degree of overlap between them. One solution would be to use long-read technologies, for instance, Oxford Nanopore or PacBio, both of which produce reads that can be several kilobases long and can be used for profiling the transcriptome of single cells5. Unfortunately, since both technologies have higher error rates and relatively low throughput, they are unlikely to replace short reads in the near future. Another approach is to perform transcriptome assembly by piecing together overlapping sequences from RNA-seq reads6. Writing in Nature Computational Science, Mingfu Shao and colleagues7 propose Scallop2, a tool for reference-guided transcriptome assembly. In particular, Scallop2 features algorithmic innovations that can take full advantage of recently developed short-read sequencing protocols for single-cell RNA-seq.

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Fig. 1: Scallop2 uses UMIs to infer additional information from splice graphs.

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Authors and Affiliations

  1. Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada

    Guillermo E. Parada

  2. Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

    Martin Hemberg

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  1. Guillermo E. Parada
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  2. Martin Hemberg
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Correspondence to Martin Hemberg.

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Parada, G.E., Hemberg, M. Bridge over troubled transcripts. Nat Comput Sci 2, 142–143 (2022). https://doi.org/10.1038/s43588-022-00219-y

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