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dc.contributor.author Sanders, Sheri
dc.contributor.author Ganote, Carrie
dc.contributor.author Papudeshi, Bhavya
dc.contributor.author Mockaitis, Keithanne
dc.contributor.author Doak, Tom
dc.date.accessioned 2018-02-05T21:51:03Z
dc.date.available 2018-02-05T21:51:03Z
dc.date.issued 2018-01-17
dc.identifier.citation Sanders, S., C. Ganote, B. Papudeshi, K. Mockaitis, and T. Doak. (2018). NCGAS makes robust transcriptome analysis easier with a readily usable workflow following de novo assembly best practices. Plant and Animal Genomics 2018, San Diego, CA. Retrieved from http://hdl.handle.net/2022/21904 en
dc.identifier.uri https://hdl.handle.net/2022/21904
dc.description.abstract The National Center for Genome Analysis Support (NCGAS) assists research groups with de novo transcriptome assembly. Best practices for such analyses include sample pooling, running multiple assembler algorithms with multiple parameters, combining the assemblies, and filtering the redundancy/erroneously assembled transcripts. These combined de novo transcriptome assemblies can put a technical burden on genomic researchers who may not be fully computationally trained on efficient use of HPC clusters. NCGAS has created a workflow template to move client data through 19 parallelized assemblies using four software packages (Trinity, SOAP-denovo, transABySS, and VelvetOases) and multiple khmers. The transcripts are then combined and filtered using EviGenes to output putative transcripts and alternative forms in a replicable manner. The process is semi-automated but flexible enough to allow researchers to adjust parameters if they desire. While designed for IU machines and XSEDE’s Bridges, allocations on these machines are available to any genomics researchers in US and the job scripts can be easily adjusted for other job handlers/clusters. This workflow provides a low bar for entry into robust transcriptome assembly that follows best practices, while also providing a replicable means of filtering large numbers of transcripts into a draft version of a transcriptome. Scripts can be found at https://github.com/NCGAS/IndianaUniversity/tree/master/Transcriptome_Workflow_Mason. en
dc.description.sponsorship This research is based upon work supported by the National Science Foundation under grant No. ABI-1458641 to Indiana University. en
dc.language.iso en en
dc.publisher Plant and Animal Genomics 2018 en
dc.rights Except where otherwise noted, the contents of this presentation are copyright of the Trustees of Indiana University. This license includes the following terms: You are free to share -to copy, distribute and transmit the work and to remix -to adapt the work under the following conditions: attribution -you must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). For any reuse or distribution, you must make clear to others the license terms of this work. en
dc.rights.uri https://creativecommons.org/licenses/by/4.0/ en
dc.subject NCGAS, Transcriptomics en
dc.title NCGAS makes robust transcriptome analysis easier with a readily usable workflow following de novo assembly best practices en
dc.type Presentation en


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