SeqOthello: querying RNA-seq experiments at scale

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Abstract

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs.

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Most cited references 19

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Expression Atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments

Robert Petryszak, Tony Burdett, Benedetto Fiorelli … (2013)

Expression Atlas (http://www.ebi.ac.uk/gxa) is a value-added database providing information about gene, protein and splice variant expression in different cell types, organism parts, developmental stages, diseases and other biological and experimental conditions. The database consists of selected high-quality microarray and RNA-sequencing experiments from ArrayExpress that have been manually curated, annotated with Experimental Factor Ontology terms and processed using standardized microarray and RNA-sequencing analysis methods. The new version of Expression Atlas introduces the concept of ‘baseline’ expression, i.e. gene and splice variant abundance levels in healthy or untreated conditions, such as tissues or cell types. Differential gene expression data benefit from an in-depth curation of experimental intent, resulting in biologically meaningful ‘contrasts’, i.e. instances of differential pairwise comparisons between two sets of biological replicates. Other novel aspects of Expression Atlas are its strict quality control of raw experimental data, up-to-date RNA-sequencing analysis methods, expression data at the level of gene sets, as well as genes and a more powerful search interface designed to maximize the biological value provided to the user.

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Network Applications of Bloom Filters: A Survey

Andrei Broder, Michael Mitzenmacher (2004)

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Author and article information

Contributors

Ye Yu: ye.yu@uky.edu

Jinpeng Liu: jinpeng.liu@uky.edu

Xinan Liu: xinan.liu@uky.edu

Yi Zhang: yi.zhang@uky.edu

Eamonn Magner: eamonn.magner5@uky.edu

Erik Lehnert: erik.lehnert@sbgenomics.com

Chen Qian: cqian12@ucsc.edu

Jinze Liu: liuj@cs.uky.edu

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): 19 October 2018

Publication date PMC-release: 19 October 2018

Publication date Collection: 2018

Volume: 19

Electronic Location Identifier: 167

Affiliations

[1 ]ISNI 0000 0004 1936 8438, GRID grid.266539.d, Department of Computer Science, , University of Kentucky, ; 301 Rose St, Lexington, KY 40508 USA

[2 ]GRID grid.492568.4, Seven Bridges Genomics Inc, ; 1 Main St, 5th Floor, Suite 500, Cambridge, MA 02142 USA

[3 ]ISNI 0000 0001 0740 6917, GRID grid.205975.c, Department of Computer Engineering, , University of California Santa Cruz, ; 1156 High Street, Santa Cruz, CA 95064 USA

Article

Publisher ID: 1535

DOI: 10.1186/s13059-018-1535-9

PMC ID: 6194578

PubMed ID: 30340508

SO-VID: 641dce01-6a6c-48f0-aa2a-722bb3587a91

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

History

Date received : 18 February 2018

Date accepted : 11 September 2018

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000145, Division of Information and Intelligent Systems;

Award ID: 1701681

Award Recipient : Jinze Liu

Funded by: National Science Foundation (US)

Award ID: 1054631

Award Recipient : Jinze Liu

Funded by: FundRef http://dx.doi.org/10.13039/100000144, Division of Computer and Network Systems;

Award ID: 1717948

Award Recipient : Chen Qian

Funded by: National Institutes of Health (US)

Award ID: P30CA177558

Award Recipient : Jinze Liu

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: 1UL1TR001998-01

Award Recipient : Jinze Liu

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: rna-seq,tcga,gene fusion,pan-cancer,query,compression,othello,seqothello

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ScienceOpen disciplines: Genetics

Keywords: rna-seq, tcga, gene fusion, pan-cancer, query, compression, othello, seqothello

SeqOthello: querying RNA-seq experiments at scale

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Abstract

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Most cited references 19

Reproducible RNA-seq analysis using recount2

Expression Atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments

Network Applications of Bloom Filters: A Survey

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