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Accurate assembly of multi-end RNA-seq data with Scallop2

Nature Computational Science volume 2, pages 148–152 (2022)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Modern RNA-sequencing (RNA-seq) protocols can produce multi-end data, where multiple reads originating from the same transcript are attached to the same barcode. The long-range information in the multi-end reads is beneficial in phasing complicated spliced isoforms, but assembly algorithms that leverage such information are lacking. Here we introduce Scallop2, a reference-based assembler optimized for multi-end RNA-seq data. The algorithmic core of Scallop2 consists of three steps: (1) using an algorithm to ‘bridge’ multi-end reads into single-end phasing paths in the context of a splice graph, (2) employing a method to refine erroneous splice graphs by utilizing multi-end reads that fail to bridge and (3) piping the refined splice graph and bridged phasing paths into an algorithm that integrates multiple phase-preserving decompositions. Tested on 561 cells in two Smart-seq3 datasets and on ten Illumina paired-end RNA-seq samples, Scallop2 substantially improves the assembly accuracy compared with two popular assemblers (StringTie2 and Scallop).

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Fig. 1: Comparison of assembly accuracy of Scallop2, StringTie2, Scallop and CLASS2.

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Data availability

Smart-seq3 is a single-cell protocol that generates multi-end RNA-seq data using barcoding technology. The Smart-seq3 data used in this study was published with Smart-seq3 protocol17, publicly available at https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8735. We use two datasets downloaded from Smart-seq3: (1) HEK293T containing 192 human cells; (2) Mouse-Fibroblast including 369 mouse cells. For the Illumina platform, we use ten human paired-end RNA-seq samples downloaded from the ENCODE project22 and we refer to these samples as ENCODE10. Their accession IDs are SRR307903, SRR307911, SRR315323, SRR315334, SRR387661, SRR534291, SRR534307, SRR534319, SRR545695 and SRR545723. The alignments of these samples are available at the Penn State Data Commons repository23. Source data are provided with this paper.

Code availability

Scallop2 is available at the Zenodo repository24 and GitHub (https://github.com/Shao-Group/scallop2). Scripts and documentation that reproduce the experimental results are available at the Zenodo repository25 and GitHub (https://github.com/Shao-Group/scallop2-test).

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Acknowledgements

This work is partly supported by the US National Science Foundation (DBI-2019797 to M.S.), the US National Institutes of Health (R01HG011065 to M.S.) and the Charles K. Etner Career Development Professorship awarded to M.S. by The Pennsylvania State University. Initial algorithmic exploration of Scallop2 advancements were conducted with C. Kingsford (Computational Biology Department, School of Computer Science, Carnegie Mellon University) and were supported by the Gordon and Betty Moore Foundation (GMBF 4554 to C. Kingsford) and the US National Institutes of Health (R01GM122935 to C. Kingsford).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Electrical Engineering and Computer Science, The Pennsylvania State University, Pennsylvania, PA, USA

    Qimin Zhang, Qian Shi & Mingfu Shao

  2. Huck Institutes of the Life Sciences, The Pennsylvania State University, Pennsylvania, PA, USA

    Mingfu Shao

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  1. Qimin Zhang
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Contributions

Q.Z., Q.S. and M.S. designed and implemented the algorithms. The experimental comparisons were primarily conducted by Q.Z. All the authors drafted and approved this manuscript.

Corresponding author

Correspondence to Mingfu Shao.

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The authors declare no competing interests.

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Nature Computational Science thanks Manuel Garber, Alexandru Tomescu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Extended data

Extended Data Fig. 1 Illustrating the construction of the splice graph \(G\) and the associated multi-end phasing paths \({{{\mathcal{C}}}}\) from read alignments of a gene locus.

Inferred splice positions in the reference genome are marked with black bars. Exons and partial exons are labeled with numbers above the reference genome. Alignment reads with the same color represent they form a group (i.e., attached with the same barcode or being the two ends of a pair in paired-end RNA-seq reads); we use gray color to represent individual read forming group of its own. The read alignments are used as input to construct the splice graph and the associated multi-end phasing paths. From the given alignments 5 (partial) exons (numbered 1–5) are identified. A source vertex s and a sink vertex t are added to the splice graph. Each numbered arrow in the splice graph represents a directed edge and its weight.

Extended Data Fig. 2 Illustration of bridging a multi-end phasing path into a single-end phasing path.

Each numbered arrow represents a directed edge and its weight. Vertices and edges in the multi-end phasing path \(C\) are blue circled and arrowed respectively. Inferred bridging paths for (l1, l2) and (l2, l3) are marked red. The single-end phasing path \(h(C)=(1,2,3,4,6)\) is bridged using the splice graph and multi-end phasing path \(C=(l_1=(1,2),l_2=(4),l_3=(6))\) as input.

Extended Data Fig. 3 Illustration of identifying false starting/ending vertices.

Inferred splice positions in the reference genome are marked with black bars. Exons and partial exons are labeled with numbers above the reference genome. Alignment reads with the same color represent they form a group (i.e., attached with the same barcode or being the two ends of a pair in paired-end RNA-seq reads); we use gray color to represent individual read forming group of its own. From the given alignments 4 partial-exons (numbered 1–4) are identified. A source vertex s and a sink vertex t are added to the splice graph. Each numbered arrow represents a directed edge and its weight. The circled vertex with number 2 (respectively 3) is classified as ending (respectively starting) vertex as there is no departing (respectively entering) junction. A pseudo-edge (2,3) from the circled vertex with number 2 to the circled vertex with number 3 will be added to the splice graph for bridging. The bridging of blue reads and red reads (the second and the third ones) will cross this pseudo-edge, giving a pseudo-score of 2 for both vertices.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15, Tables 1 and 2, Notes 1–8 and Algorithm 1.

Source data

Source Data Fig. 1

Statistical source data for Fig. 1.

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Zhang, Q., Shi, Q. & Shao, M. Accurate assembly of multi-end RNA-seq data with Scallop2. Nat Comput Sci 2, 148–152 (2022). https://doi.org/10.1038/s43588-022-00216-1

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