001     130277
005     20240228143421.0
024 7 _ |a 10.1371/journal.pone.0167417
|2 doi
024 7 _ |a pmid:27907167
|2 pmid
024 7 _ |a pmc:PMC5132003
|2 pmc
024 7 _ |a altmetric:14417506
|2 altmetric
037 _ _ |a DKFZ-2017-05356
041 _ _ |a eng
082 _ _ |a 500
100 1 _ |a Okonechnikov, Konstantin
|0 P:(DE-He78)34b3639de467b2c700920d7cbc3d2110
|b 0
|e First author
|u dkfz
245 _ _ |a InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data.
260 _ _ |a Lawrence, Kan.
|c 2016
|b PLoS
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1522135111_11126
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Analysis of fusion transcripts has become increasingly important due to their link with cancer development. Since high-throughput sequencing approaches survey fusion events exhaustively, several computational methods for the detection of gene fusions from RNA-seq data have been developed. This kind of analysis, however, is complicated by native trans-splicing events, the splicing-induced complexity of the transcriptome and biases and artefacts introduced in experiments and data analysis. There are a number of tools available for the detection of fusions from RNA-seq data; however, certain differences in specificity and sensitivity between commonly used approaches have been found. The ability to detect gene fusions of different types, including isoform fusions and fusions involving non-coding regions, has not been thoroughly studied yet. Here, we propose a novel computational toolkit called InFusion for fusion gene detection from RNA-seq data. InFusion introduces several unique features, such as discovery of fusions involving intergenic regions, and detection of anti-sense transcription in chimeric RNAs based on strand-specificity. Our approach demonstrates superior detection accuracy on simulated data and several public RNA-seq datasets. This improved performance was also evident when evaluating data from RNA deep-sequencing of two well-established prostate cancer cell lines. InFusion identified 26 novel fusion events that were validated in vitro, including alternatively spliced gene fusion isoforms and chimeric transcripts that include intergenic regions. The toolkit is freely available to download from http:/bitbucket.org/kokonech/infusion.
536 _ _ |a 312 - Functional and structural genomics (POF3-312)
|0 G:(DE-HGF)POF3-312
|c POF3-312
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed,
650 _ 7 |a Oncogene Proteins, Fusion
|2 NLM Chemicals
700 1 _ |a Imai-Matsushima, Aki
|b 1
700 1 _ |a Paul, Lukas
|0 0000-0001-9410-4078
|b 2
700 1 _ |a Seitz, Alexander
|b 3
700 1 _ |a Meyer, Thomas F
|b 4
700 1 _ |a Garcia-Alcalde, Fernando
|b 5
773 _ _ |a 10.1371/journal.pone.0167417
|g Vol. 11, no. 12, p. e0167417 -
|0 PERI:(DE-600)2267670-3
|n 12
|p e0167417 -
|t PLoS one
|v 11
|y 2016
|x 1932-6203
909 C O |o oai:inrepo02.dkfz.de:130277
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 0
|6 P:(DE-He78)34b3639de467b2c700920d7cbc3d2110
913 1 _ |a DE-HGF
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-312
|2 G:(DE-HGF)POF3-300
|v Functional and structural genomics
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Gesundheit
914 1 _ |y 2016
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b PLOS ONE : 2015
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Thomson Reuters Master Journal List
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1040
|2 StatID
|b Zoological Record
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
920 1 _ |0 I:(DE-He78)B062-20160331
|k B062
|l Pädiatrische Neuroonkologie
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)B062-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21