Browsing by Author "Mockaitis, Keithanne"

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Cyberinfrastructure resources enabling creation of the loblolly pine reference transcriptome
(ACM, New York, NY, 2015-07-26) Wu, Le-Shin; Ganote, Carrie; Doak, Thomas; Barnett, William K.; Mockaitis, Keithanne; Stewart, Craig A.
Today's genomics technologies generate more sequence data than ever before possible, and at substantially lower costs, serving researchers across biological disciplines in transformative ways. Building transcriptome assemblies from RNA sequencing reads is one application of next-generation sequencing (NGS) that has held a central role in biological discovery in both model and non- model organisms, with and without whole genome sequence references. A major limitation in effective building of transcriptome references is no longer the sequencing data generation itself, but the computing infrastructure and expertise needed to assemble, analyze and manage the data. Here we describe a currently available resource dedicated to achieving such goals, and its use for extensive RNA assembly of up to 1.3 billion reads representing the massive transcriptome of loblolly pine, using four major assembly software installations. The Mason cluster, an XSEDE second tier resource at Indiana University, provides the necessary fast CPU cycles, large memory, and high I/O throughput for conducting large-scale genomics research. The National Center for Genome Analysis Support, or NCGAS, provides technical support in using HPC systems, bioinformatic support for determining the appropriate method to analyze a given dataset, and practical assistance in running computations. We demonstrate that a sufficient supercomputing resource and good workflow design are elements that are essential to large eukaryotic genomics and transcriptomics projects such as the complex transcriptome of loblolly pine, gene expression data that inform annotation and functional interpretation of the largest genome sequence reference to date.
NCGAS makes robust transcriptome analysis easier with a readily usable workflow following de novo assembly best practices
(Plant and Animal Genomics 2018, 2018-01-17) Sanders, Sheri; Ganote, Carrie; Papudeshi, Bhavya; Mockaitis, Keithanne; Doak, Tom
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.
Solving the challenges of complex genome analysis collaborations on-line using XSEDE resources (Poster)
(Plant and Animal Genomics 2018, 2018-01-15) Ganote, Carrie; Sanders, Sheri; Wu, Le-Shin; Doak, Tom; Mockaitis, Keithanne
Whole genome reference and functional genomics projects most often benefit from a diversity of expertise and the integrated contributions of multiple research groups. The importance of web-enabled data sharing and open access software to progress in genome research is indisputable. Dissemination of genome resource information through internet-based resources, especially customizing and encouraging the optimal use of analysis tools to serve a specific research community, often lags behind data generation. This lag can inhibit biological interpretations and downstream experimentation, much of which should be undertaken before a genome resource project is completed. File systems for storing data and analysis outputs of today’s project standards must be large and secure, and must offer sustained access to fulfill the hosting requirements an active and dispersed research community needs. Users must interact with a computing resource powerful enough to get their jobs done, and sites must be expandable and flexible to accommodate a growing demand for intra-genera comparisons and pan-genomics of a species. We have been using NSF XSEDE computational resources including Jetstream along with the High Performance Computing systems of Indiana University to meet these challenges for a variety of collaborative plant genomics studies. Currently these efforts are impacting metabolic gene discovery in the tetraploid Arachis hypogaea and whole genomic reference studies of the tetraploid Coffea arabica and its diploid progenitors. Here we show practical examples of XSEDE resource use and development that may benefit other genomic research groups seeking to increase the effectiveness of their computing and collaboration.
Stilbenoid prenylation pathway discovery in peanut using targeted transcriptomics (Poster)
(Plant and Animal Genomics 2018, 2018-01-15) Sanders, Sheri; Podicheti, Ram; Yang, Tianhong; Fang, Lingling; Jayanthi, Srinivas; Rajan, Gayathri; Kumar, Thallapuranam Krishnaswarmy Suresh; Medina-Bolivar, Fabricio; Mockaitis, Keithanne
In peanut, Arachis hypogaea, defense responses to biotic and abiotic stresses include the synthesis of prenylated stilbenoids, with over 45 such compounds identified to date. The diversification of secondary metabolites in plants is expanded by prenylation activities, and in recent studies this modification has been shown to enhance biological activities of polyphenolic compounds. We describe our discovery of genes responsible for stilbenoid prenylation* as well as studies underway to understand the regulation of these metabolic programs in peanut. Sequencing RNA from a well-characterized peanut hairy root system, we built a transcriptome reference and correlated transcripts with metabolites produced over a time course of elicitation. Transcripts encoding candidate enzymes were identified and characterized functionally by heterologous transient expression. Prenyltransferases we call AhR4DT-1 and AhR3’DT-1 catalyze distinct reactions, and our studies suggest that these act in the first committed steps that convert non-prenylated into prenylated stilbenoids. Here we highlight the functional transcriptomics that led to these discoveries, and our ongoing approaches to find other genes that act in the regulation of this defensive metabolic program. Our identification of the first plant stilbenoid-specific prenyltransferases advances the understanding of this specialized gene family, and contributes some of the functional definition that is needed generally to refine the annotations of plant genomes. *Yang T, Fang L, Sanders S, Jayanthi S, Rajan G, Podicheti R, Kumar TKS, Mockaitis K, Medina-Bolivar F, 2017. JBC, in press. Stilbenoid prenyltransferases define key steps in the diversification of peanut phytoalexins.