2016
DOI: 10.1371/journal.pone.0167417
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InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data

Abstract: 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 … Show more

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Cited by 45 publications
(31 citation statements)
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“…Differential gene expression analysis between subgroups was performed using the R/Bioconductor package DESeq2 with contrast adjustment for multiple groups comparison. Fusion gene discovery was performed by the InFusion toolkit v.0.6.3 20 …”
Section: Methodsmentioning
confidence: 99%
“…Differential gene expression analysis between subgroups was performed using the R/Bioconductor package DESeq2 with contrast adjustment for multiple groups comparison. Fusion gene discovery was performed by the InFusion toolkit v.0.6.3 20 …”
Section: Methodsmentioning
confidence: 99%
“…The innovative role of reverse transcribed cDNAs is further enhanced by the well-documented phenomenon of “trans-splicing”, which involves the joining of sequences from two distinct transcripts [ 368 , 369 , 370 , 371 , 372 , 373 , 374 , 375 , 376 , 377 , 378 , 379 ]. Genome insertion of cDNA reverse-transcribed from a trans-spliced RNA molecule generates a novel chimeric domain coding sequence [ 379 , 380 ].…”
Section: Genome Writing By Natural Genetic Engineering—protein Evomentioning
confidence: 99%
“…Except for one TruncatedCoding fusion detected by both deFuse and INTEGRATE, all TruncatedCoding and TruncatedNonCoding fusions were detected by deFuse alone. In previous works on fusion callers Zhang et al 2016;Okonechnikov et al 2016) or the comparison of them (Liu et al 2016), deFuse did not belong to the best performance group. This could be because most fusion callers do not report truncated fusions, leading to their exclusion from the validation benchmark.…”
Section: Computational Validation Of the Fusion Detection Pipelinementioning
confidence: 76%