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10.1002/wrna.1382 http://scihub22266oqcxt.onion/10.1002/wrna.1382 C5065767!5065767 !27485475     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27485475 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Wiley+Interdiscip+Rev+RNA 2016 ; 7 (6 ): 811-823 Nephropedia Template TP {{tp|p=27485475 |t=2016. Identifying fusion transcripts using next generation sequencing |pdf=|usr=}}{{27485475 }} gab.com Text Identifying fusion transcripts using next generation sequencing JO: Wiley Interdiscip Rev RNA http://www.kidney.de/pget.php?&tw=1&pmid=27485475 #FOAvip Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions have been used successfully for cancer diagnosis, prognosis, and therapeutic applications. In addition, many fusion transcripts are found in normal human cell lines and tissues, with some data supporting their role in normal physiology. Besides chromosomal rearrangement, intergenic splicing can generate them. Global identification of fusion transcripts becomes possible with the help of next generation sequencing technology like RNA-Seq. In the past decade, major advancements have been made for chimeric RNA discovery due to the development of advanced sequencing platform and software packages. However, current software tools behave differently in terms of specificity, sensitivity, time, and computational memory usage. Recent benchmarking studies showed that none of the tools are inclusive. The development of high performance (accurate and fast), and user-friendly fusion detection tool/pipeline is still an open quest. In this article, we review the existing software packages for fusion detection. We explain the methods of the tools, and discuss various factors that affect fusion detection. We summarize conclusions drawn from several comparative studies, and then discuss some of the pitfalls of these studies. We also describe the limitations of current tools, and suggest directions for future development. WIREs RNA 2016, 7:811-823. doi: 10.1002/wrna.1382 For further resources related to this article, please visit the WIREs website. Twit Text FOAVip Identifying fusion transcripts using next generation sequencing ????Wiley Interdiscip Rev RNA http://www.kidney.de/pget.php?&tw=1&pmid=27485475 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27485475 #FOAvip English Wikipedia <ref>{{Cite pmid|27485475 }}</ref> Identifying fusion transcripts using next generation sequencing #MMPMID27485475 Kumar S ; Razzaq SK ; Vo AD ; Gautam M ; Li H Wiley Interdiscip Rev RNA 2016[Nov]; 7 (6 ): 811-823 PMID27485475 show ga Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions have been used successfully for cancer diagnosis, prognosis, and therapeutic applications. In addition, many fusion transcripts are found in normal human cell lines and tissues, with some data supporting their role in normal physiology. Besides chromosomal rearrangement, intergenic splicing can generate them. Global identification of fusion transcripts becomes possible with the help of next generation sequencing technology like RNA-Seq. In the past decade, major advancements have been made for chimeric RNA discovery due to the development of advanced sequencing platform and software packages. However, current software tools behave differently in terms of specificity, sensitivity, time, and computational memory usage. Recent benchmarking studies showed that none of the tools are inclusive. The development of high performance (accurate and fast), and user-friendly fusion detection tool/pipeline is still an open quest. In this article, we review the existing software packages for fusion detection. We explain the methods of the tools, and discuss various factors that affect fusion detection. We summarize conclusions drawn from several comparative studies, and then discuss some of the pitfalls of these studies. We also describe the limitations of current tools, and suggest directions for future development. WIREs RNA 2016, 7:811-823. doi: 10.1002/wrna.1382 For further resources related to this article, please visit the WIREs website. |*Gene Expression Profiling [MESH] |*Gene Fusion [MESH] |Animals [MESH] |Computer Simulation [MESH] |High-Throughput Nucleotide Sequencing/*methods [MESH] |Humans [MESH]Identifying fusion transcripts using next generation sequencing (epmc).pdf DeepDyve Pubget Overpricing