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10.1093/nar/gkw282 http://scihub22266oqcxt.onion/10.1093/nar/gkw282 C4889949!4889949 !27105842     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27105842 &cmd=llinks): Failed to open stream: HTTP request failed! 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Discovering and understanding oncogenic gene fusions through data intensive computational approaches |pdf=|usr=}}{{27105842 }} gab.com Text Discovering and understanding oncogenic gene fusions through data intensive computational approaches JO: Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=27105842 #FOAvip Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different-yet highly complementary and symbiotic-approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation. Twit Text FOAVip Discovering and understanding oncogenic gene fusions through data intensive computational approaches ????Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=27105842 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27105842 #FOAvip English Wikipedia <ref>{{Cite pmid|27105842 }}</ref> Discovering and understanding oncogenic gene fusions through data intensive computational approaches #MMPMID27105842 Latysheva NS ; Babu MM Nucleic Acids Res 2016[Jun]; 44 (10 ): 4487-503 PMID27105842 show ga Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different-yet highly complementary and symbiotic-approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation. |*Gene Fusion [MESH] |*Oncogenes [MESH] |Algorithms [MESH] |Computational Biology/*methods [MESH] |Databases, Genetic [MESH] |Gene Expression Regulation [MESH] |Humans [MESH] |Neoplasms/genetics [MESH] |Oncogene Proteins, Fusion/*chemistry/genetics [MESH]Discovering and understanding oncogenic gene fusions through data inte(epmc).pdf DeepDyve Pubget Overpricing