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- Publisher Website: 10.1093/bioinformatics/btw732
- Scopus: eid_2-s2.0-85020082480
- PMID: 28025200
- WOS: WOS:000397265300025
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Article: Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud
Title | Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud |
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Authors | |
Issue Date | 2017 |
Citation | Bioinformatics, 2017, v. 33, n. 5, p. 767-769 How to Cite? |
Abstract | © The Author 2016. Published by Oxford University Press. All rights reserved. Single-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellization of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq datasets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6-145.4 times faster using Falco than running on a highly optimized standalone computer. Falco also allows users to utilize low-cost spot instances of Amazon Web Services, providing a ∼65% reduction in cost of analysis. |
Persistent Identifier | http://hdl.handle.net/10722/262744 |
ISSN | 2023 Impact Factor: 4.4 2023 SCImago Journal Rankings: 2.574 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yang, Andrian | - |
dc.contributor.author | Troup, Michael | - |
dc.contributor.author | Lin, Peijie | - |
dc.contributor.author | Ho, Joshua W.K. | - |
dc.date.accessioned | 2018-10-08T02:46:55Z | - |
dc.date.available | 2018-10-08T02:46:55Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Bioinformatics, 2017, v. 33, n. 5, p. 767-769 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262744 | - |
dc.description.abstract | © The Author 2016. Published by Oxford University Press. All rights reserved. Single-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellization of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq datasets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6-145.4 times faster using Falco than running on a highly optimized standalone computer. Falco also allows users to utilize low-cost spot instances of Amazon Web Services, providing a ∼65% reduction in cost of analysis. | - |
dc.language | eng | - |
dc.relation.ispartof | Bioinformatics | - |
dc.title | Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud | - |
dc.type | Article | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1093/bioinformatics/btw732 | - |
dc.identifier.pmid | 28025200 | - |
dc.identifier.scopus | eid_2-s2.0-85020082480 | - |
dc.identifier.volume | 33 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 767 | - |
dc.identifier.epage | 769 | - |
dc.identifier.eissn | 1460-2059 | - |
dc.identifier.isi | WOS:000397265300025 | - |