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Article: SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples

TitleSpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples
Authors
Issue Date2014
PublisherOxford University Press. The Journal's web site is located at http://nar.oxfordjournals.org/
Citation
Nucleic Acids Research, 2014, v. 42 n. 15, article no. e121 How to Cite?
AbstractConventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet.
Persistent Identifierhttp://hdl.handle.net/10722/200455
ISSN
2015 Impact Factor: 9.202
2015 SCImago Journal Rankings: 7.458

 

DC FieldValueLanguage
dc.contributor.authorYALAMANCHILI, HKen_US
dc.contributor.authorLI, Zen_US
dc.contributor.authorWANG, Pen_US
dc.contributor.authorWong, MPen_US
dc.contributor.authorYao, JJen_US
dc.contributor.authorWang, JJen_US
dc.date.accessioned2014-08-21T06:47:23Z-
dc.date.available2014-08-21T06:47:23Z-
dc.date.issued2014en_US
dc.identifier.citationNucleic Acids Research, 2014, v. 42 n. 15, article no. e121en_US
dc.identifier.issn0305-1048-
dc.identifier.urihttp://hdl.handle.net/10722/200455-
dc.description.abstractConventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet.-
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://nar.oxfordjournals.org/-
dc.relation.ispartofNucleic Acids Researchen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samplesen_US
dc.typeArticleen_US
dc.identifier.emailWong, MP: mwpik@hku.hken_US
dc.identifier.emailYao, JJ: jeffyao@hku.hken_US
dc.identifier.emailWang, JJ: junwen@hku.hken_US
dc.identifier.authorityWong, MP=rp00348en_US
dc.identifier.authorityYao, JJ=rp01473en_US
dc.identifier.authorityWang, JJ=rp00280en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1093/nar/gku577en_US
dc.identifier.pmid25034693-
dc.identifier.hkuros232067en_US
dc.identifier.volume42-
dc.identifier.issue15-
dc.publisher.placeUnited Kingdom-

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