scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing

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Abstract

Identifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does not exist to maximize variant calling accuracy. Furthermore, molecular duplicates generated in these experiments have not been utilized to optimally detect variant co-expression. Herein, we introduce scSNV designed from the ground up to “collapse” molecular duplicates and accurately identify variants and their co-expression. We demonstrate that scSNV is fast, with a reduced false-positive variant call rate, and enables the co-detection of genetic variants and A>G RNA edits across twenty-two samples.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02364-5.

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The Sequence Alignment/Map format and SAMtools

Heng Li, Bob Handsaker, Alec Wysoker … (2009)

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Author and article information

Contributors

Gavin W. Wilson: gavin.wilson@uhnresearch.ca

Jonathan C. Yeung: jonathan.yeung@uhn.ca

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 7 May 2021

Publication date PMC-release: 7 May 2021

Publication date Collection: 2021

Volume: 22

Electronic Location Identifier: 144

Affiliations

[1 ]GRID grid.231844.8, ISNI 0000 0004 0474 0428, Latner Thoracic Surgery Research Laboratories, , University Health Network, ; 101 College St., 2-501, Toronto, ON M5G 2C4 Canada

[2 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Division of Thoracic Surgery, Department of Surgery, , University of Toronto, ; Toronto, M5G 2C4 Canada

[3 ]GRID grid.417184.f, ISNI 0000 0001 0661 1177, Toronto General Hospital, ; 200 Elizabeth St, 9N-983, Toronto, ON M5G 2C4 Canada

Article

Publisher ID: 2364

DOI: 10.1186/s13059-021-02364-5

PMC ID: 8103760

PubMed ID: 33962667

SO-VID: dad8e81e-95de-4dbc-aae2-ef81bab94028

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 8 July 2020

Date accepted : 23 April 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100010261, Thoracic Surgery Foundation;

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: single-cell rna-seq,genetic variation,alignment,variant calling

Data availability:

ScienceOpen disciplines: Genetics

Keywords: single-cell rna-seq, genetic variation, alignment, variant calling

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scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing

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Abstract

Supplementary Information

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Arabidopsis genomics

Most cited references 31

The Sequence Alignment/Map format and SAMtools

STAR: ultrafast universal RNA-seq aligner.

Comprehensive Integration of Single-Cell Data

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Cited by 4

Most referenced authors 3,913