MOTIVATION: The discovery of novel gene fusions can lead to a better comprehension of cancer progression and development. The emergence of deep sequencing of trancriptome, known as RNA-seq, has opened many opportunities for the identification of this class of genomic alterations, leading to the discovery of novel chimeric transcripts in melanomas, breast cancers and lymphomas. Nowadays, few computational approaches have been developed for the detection of chimeric transcripts. Although all of these computational methods show good sensitivity, much work remains to reduce the huge number of false-positive calls that arises from this analysis. RESULTS: We proposed a novel computational framework, named chimEric tranScript detection algorithm (EricScript), for the identification of gene fusion products in paired-end RNA-seq data. Our simulation study on synthetic data demonstrates that EricScript enables to achieve higher sensitivity and specificity than existing methods with noticeably lower running times. We also applied our method to publicly available RNA-seq tumour datasets, and we showed its capability in rediscovering known gene fusions.
MOTIVATION: The discovery of novel gene fusions can lead to a better comprehension of cancer progression and development. The emergence of deep sequencing of trancriptome, known as RNA-seq, has opened many opportunities for the identification of this class of genomic alterations, leading to the discovery of novel chimeric transcripts in melanomas, breast cancers and lymphomas. Nowadays, few computational approaches have been developed for the detection of chimeric transcripts. Although all of these computational methods show good sensitivity, much work remains to reduce the huge number of false-positive calls that arises from this analysis. RESULTS: We proposed a novel computational framework, named chimEric tranScript detection algorithm (EricScript), for the identification of gene fusion products in paired-end RNA-seq data. Our simulation study on synthetic data demonstrates that EricScript enables to achieve higher sensitivity and specificity than existing methods with noticeably lower running times. We also applied our method to publicly available RNA-seq tumour datasets, and we showed its capability in rediscovering known gene fusions.
Authors: Ahmed Gilani; Andrew Donson; Kurtis D Davies; Susan L Whiteway; Jessica Lake; John DeSisto; Lindsey Hoffman; Nicholas K Foreman; B K Kleinschmidt-DeMasters; Adam L Green Journal: J Neurooncol Date: 2019-12-24 Impact factor: 4.130
Authors: Yongchao Dou; Emily A Kawaler; Daniel Cui Zhou; Marina A Gritsenko; Chen Huang; Lili Blumenberg; Alla Karpova; Vladislav A Petyuk; Sara R Savage; Shankha Satpathy; Wenke Liu; Yige Wu; Chia-Feng Tsai; Bo Wen; Zhi Li; Song Cao; Jamie Moon; Zhiao Shi; MacIntosh Cornwell; Matthew A Wyczalkowski; Rosalie K Chu; Suhas Vasaikar; Hua Zhou; Qingsong Gao; Ronald J Moore; Kai Li; Sunantha Sethuraman; Matthew E Monroe; Rui Zhao; David Heiman; Karsten Krug; Karl Clauser; Ramani Kothadia; Yosef Maruvka; Alexander R Pico; Amanda E Oliphant; Emily L Hoskins; Samuel L Pugh; Sean J I Beecroft; David W Adams; Jonathan C Jarman; Andy Kong; Hui-Yin Chang; Boris Reva; Yuxing Liao; Dmitry Rykunov; Antonio Colaprico; Xi Steven Chen; Andrzej Czekański; Marcin Jędryka; Rafał Matkowski; Maciej Wiznerowicz; Tara Hiltke; Emily Boja; Christopher R Kinsinger; Mehdi Mesri; Ana I Robles; Henry Rodriguez; David Mutch; Katherine Fuh; Matthew J Ellis; Deborah DeLair; Mathangi Thiagarajan; D R Mani; Gad Getz; Michael Noble; Alexey I Nesvizhskii; Pei Wang; Matthew L Anderson; Douglas A Levine; Richard D Smith; Samuel H Payne; Kelly V Ruggles; Karin D Rodland; Li Ding; Bing Zhang; Tao Liu; David Fenyö Journal: Cell Date: 2020-02-13 Impact factor: 41.582