RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads

Publication type:
Journal article
Metadata:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000547055000003&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1186/s12859-020-03593-4
Externe Identifier
  • Clarivate Analytics Document Solution ID: MH9QQ
  • PubMed Identifier: 32611394
ISSN
1471-2105
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC BIOINFORMATICS
Schlüsselwörter
  • Single-cell sequencing
  • RNA
  • Locality sensitive hashing
  • Big data
Artikelnummer
ARTN 274
Datum der Veröffentlichung
2020
Status
Published
Titel
RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads
Sub types
  • Article
Ausgabe der Zeitschrift
21

Data source: Web of Science (Lite)

Other metadata sources:
Abstract
<jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Obtaining data from single-cell transcriptomic sequencing allows for the investigation of cell-specific gene expression patterns, which could not be addressed a few years ago. With the advancement of droplet-based protocols the number of studied cells continues to increase rapidly. This establishes the need for software tools for efficient processing of the produced large-scale datasets. We address this need by presenting RainDrop for fast gene-cell count matrix computation from single-cell RNA-seq data produced by 10x Genomics Chromium technology.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>RainDrop can process single-cell transcriptomic datasets consisting of 784 million reads sequenced from around 8.000 cells in less than 40 minutes on a standard workstation. It significantly outperforms the established Cell Ranger pipeline and the recently introduced Alevin tool in terms of runtime by a maximal (average) speedup of 30.4 (22.6) and 3.5 (2.4), respectively, while keeping high agreements of the generated results.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>RainDrop is a software tool for highly efficient processing of large-scale droplet-based single-cell RNA-seq datasets on standard workstations written in C++. It is available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://gitlab.rlp.net/stnieble/raindrop">https://gitlab.rlp.net/stnieble/raindrop</jats:ext-link>.</jats:p> </jats:sec>
Autoren
DOI
10.1186/s12859-020-03593-4
eISSN
1471-2105
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Bioinformatics
Sprache
en
Artikelnummer
274
Online publication date
2020
Datum der Veröffentlichung
2020
Status
Published
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1186/s12859-020-03593-4
Datum der Datenerfassung
2021
Titel
RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads
Ausgabe der Zeitschrift
21

Data source: Crossref

Abstract
<h4>Background</h4>Obtaining data from single-cell transcriptomic sequencing allows for the investigation of cell-specific gene expression patterns, which could not be addressed a few years ago. With the advancement of droplet-based protocols the number of studied cells continues to increase rapidly. This establishes the need for software tools for efficient processing of the produced large-scale datasets. We address this need by presenting RainDrop for fast gene-cell count matrix computation from single-cell RNA-seq data produced by 10x Genomics Chromium technology.<h4>Results</h4>RainDrop can process single-cell transcriptomic datasets consisting of 784 million reads sequenced from around 8.000 cells in less than 40 minutes on a standard workstation. It significantly outperforms the established Cell Ranger pipeline and the recently introduced Alevin tool in terms of runtime by a maximal (average) speedup of 30.4 (22.6) and 3.5 (2.4), respectively, while keeping high agreements of the generated results.<h4>Conclusions</h4>RainDrop is a software tool for highly efficient processing of large-scale droplet-based single-cell RNA-seq datasets on standard workstations written in C++. It is available at https://gitlab.rlp.net/stnieble/raindrop .
Addresses
  • Department of Computer Science, Johannes Gutenberg University, Mainz, 55099, Germany.
Autoren
DOI
10.1186/s12859-020-03593-4
eISSN
1471-2105
Externe Identifier
  • PubMed Identifier: 32611394
  • PubMed Central ID: PMC7329424
Open access
true
ISSN
1471-2105
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC bioinformatics
Schlüsselwörter
  • Humans
  • Sequence Analysis, RNA
  • User-Computer Interface
  • Information Storage and Retrieval
  • Databases, Genetic
  • Single-Cell Analysis
Sprache
eng
Medium
Electronic
Online publication date
2020
Open access status
Open Access
Paginierung
274
Datum der Veröffentlichung
2020
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2020
Titel
RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads.
Sub types
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
21

Files

https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-020-03593-4 https://europepmc.org/articles/PMC7329424?pdf=render

Data source: Europe PubMed Central

Abstract
BACKGROUND: Obtaining data from single-cell transcriptomic sequencing allows for the investigation of cell-specific gene expression patterns, which could not be addressed a few years ago. With the advancement of droplet-based protocols the number of studied cells continues to increase rapidly. This establishes the need for software tools for efficient processing of the produced large-scale datasets. We address this need by presenting RainDrop for fast gene-cell count matrix computation from single-cell RNA-seq data produced by 10x Genomics Chromium technology. RESULTS: RainDrop can process single-cell transcriptomic datasets consisting of 784 million reads sequenced from around 8.000 cells in less than 40 minutes on a standard workstation. It significantly outperforms the established Cell Ranger pipeline and the recently introduced Alevin tool in terms of runtime by a maximal (average) speedup of 30.4 (22.6) and 3.5 (2.4), respectively, while keeping high agreements of the generated results. CONCLUSIONS: RainDrop is a software tool for highly efficient processing of large-scale droplet-based single-cell RNA-seq datasets on standard workstations written in C++. It is available at https://gitlab.rlp.net/stnieble/raindrop .
Date of acceptance
2020
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/32611394
DOI
10.1186/s12859-020-03593-4
eISSN
1471-2105
Externe Identifier
  • PubMed Central ID: PMC7329424
Ausgabe der Veröffentlichung
1
Zeitschrift
BMC Bioinformatics
Schlüsselwörter
  • Big data
  • Locality sensitive hashing
  • RNA
  • Single-cell sequencing
  • Databases, Genetic
  • Humans
  • Information Storage and Retrieval
  • Sequence Analysis, RNA
  • Single-Cell Analysis
  • User-Computer Interface
Sprache
eng
Country
England
Paginierung
274
PII
10.1186/s12859-020-03593-4
Datum der Veröffentlichung
2020
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Titel
RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
21

Data source: PubMed

Autoren
Zeitschrift
BMC Bioinform.
Artikelnummer
1
Paginierung
274 - 274
Datum der Veröffentlichung
2020
Titel
RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads.
Ausgabe der Zeitschrift
21

Data source: DBLP

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