Defense responses of peanut () to biotic and abiotic stresses include the synthesis of prenylated stilbenoids. Members of this compound class show several protective activities in human disease studies, and the list of potential therapeutic targets continues to expand. Despite their medical and biological importance, the biosynthetic pathways of prenylated stilbenoids remain to be elucidated, and the genes encoding stilbenoid-specific prenyltransferases have yet to be identified in any plant species. In this study, we combined targeted transcriptomic and metabolomic analyses to discover prenyltransferase genes in elicitor-treated peanut hairy root cultures. Transcripts encoding five enzymes were identified, and two of these were functionally characterized in a transient expression system consisting of-infiltrated leaves of We observed that one of these prenyltransferases, AhR4DT-1, catalyzes a key reaction in the biosynthesis of prenylated stilbenoids, in which resveratrol is prenylated at its C-4 position to form arachidin-2, whereas another, AhR3'DT-1, added the prenyl group to C-3' of resveratrol. Each of these prenyltransferases was highly specific for stilbenoid substrates, and we confirmed their subcellular location in the plastid by fluorescence microscopy. Structural analysis of the prenylated stilbenoids suggested that these two prenyltransferase activities represent the first committed steps in the biosynthesis of a large number of prenylated stilbenoids and their derivatives in peanut. In summary, we have identified five candidate prenyltransferases in peanut and confirmed that two of them are stilbenoid-specific, advancing our understanding of this specialized enzyme family and shedding critical light onto the biosynthesis of bioactive stilbenoids.

Although the reference genome of Solanum tuberosum Group Phureja double-monoploid (DM) clone is available, knowledge on the genetic diversity of the highly heterozygous tetraploid Group Tuberosum, representing most cultivated varieties, remains largely unexplored. This lack of knowledge hinders further progress in potato research. In conducted investigation, we first merged and manually curated the two existing partially-overlapping DM genome-based gene models, creating a union of genes in Phureja scaffold. Next, we compiled available and newly generated RNA-Seq datasets (cca. 1.5 billion reads) for three tetraploid potato genotypes (cultivar Désirée, cultivar Rywal, and breeding clone PW363) with diverse breeding pedigrees. Short-read transcriptomes were assembled using several de novo assemblers under different settings to test for optimal outcome. For cultivar Rywal, PacBio Iso-Seq full-length transcriptome sequencing was also performed. EvidentialGene redundancy-reducing pipeline complemented with in-house developed scripts was employed to produce accurate and complete cultivar-specific transcriptomes, as well as to attain the pan-transcriptome. The generated transcriptomes and pan-transcriptome represent a valuable resource for potato gene variability exploration, high-throughput omics analyses, and breeding programmes.

Background: Although the reference genome of Solanum tuberosum group Phureja double-monoploid (DM) clone is available, knowledge on the genetic diversity of the highly heterozygous tetraploid group Tuberosum, representing most cultivated varieties, remains largely unexplored. This lack of knowledge hinders further progress in potato research and its subsequent applications in breeding. Results: For the DM genome assembly, two only partially-overlapping gene models exist di ering in a unique set of genes and intron/exon structure predictions. First step was to merge and manually curate the merged gene model, creating a union of genes in Phureja sca old. We next compiled available RNA-Seq datasets (cca. 1.5 billion reads) for three tetraploid potato genotypes (cultivar Désirée, cultivar Rywal, and breeding clone PW363) with diverse breeding pedigrees. Short-read transcriptomes were assembled using CLC, Trinity, Velvet, and rnaSPAdes de novo assemblers using di erent settings to test for optimal outcome. In addition, for cultivar Rywal, PacBio Iso-Seq full-length transcriptome sequencing was also performed. Revised EvidentialGene redundancy-reducing pipeline was employed to produce accurate and complete cultivar-speci c transcriptomes from assemblers output, as well as to attain the pan-transcriptome. Due to being the most diverse dataset in terms of tissues (stem, seedlings and roots) and experimental conditions, cv. Désirée was the most complete transcriptome (95.8% BUSCO completeness). For cv. Rywal and breeding clone PW363 data were available for leaf samples only and the resulting transcriptomes were less complete than cv. Désirée (89.8% and 89.3% BUSCO completeness, respectively). Cross comparison of these cultivar-speci c transcriptomes and merged DM gene model suggests that the core potato transcriptome is comprised of 16,339 genes. The pan-transcriptome contains a total of 95,779 transcripts, of which 54,614 transcripts are not present in the Phureja genome. These represent the variants of the novel genes found in the potato pan-genome. Conclusions: Our analysis shows that the available gene model of double-monoploid potato from group Phureja is, to some degree, not complete. The generated transcriptomes and pan-transcriptome represent a valuable resource for potato gene variability exploration, high-throughput -omics analyses, and future breeding programmes.

While we confirmed the continental-insular divide among mtDNA haplotypes, maintenance of both Y DNA haplotypes on Bali, deep within the Indonesian archipelago calls into question the mechanism by which Y DNA diversity has been maintained. It also suggests the continental-insular designation is less appropriate for Y DNA, leading us to propose geographically neutral Y haplotype designations.

e21152 Background: The medical community is continually searching for the best way to treat cancer. The value and utility of biomarkers in guiding treatment decisions is widely accepted but remains a challenge for the bedside clinician and requires ongoing validation and correlation to clinical outcomes. Caris Life Sciences has a dedicated team of scientists who study volumes of scientific literature, synthesize biomarker research and by way of an evidence-based electronic rules engine, translates the application of the literature to biomarker analysis of tumor tissue (The Target Now Report) in support of biomarker-drug association evidence useful in clinical decision-making. Subsequently, Caris initiated the Caris Registry to capture clinical disease, treatment and outcome data from patients who have a Target Now Report. Methods: The Caris Registry is a web-based data entry platform based on an IRB approved protocol. The eligible subject for the Registry will have a qualified Target Now Report. All clinical data elements are defined and supported by the NCI caBIG standardized data dictionary. Disease history/status, treatments and outcomes are captured at enrollment with Day 1 defined as the date of the Target Now Report and every 9 months for 5 years or death whichever is first. Results: As of January 19, 2012, there are 68 participating centers across the country and 43 centers pending IRB submission. There are 852 Target Now cases enrolled with the following cancer lineage distribution: Breast 209, Ovary 169, Lung 117, Colon 79, Endometrium 33, and other 245. There are 323 completed follow up reports and 175 completed end of study reports capturing vital status and cancer related deaths. Conclusions: Caris has successfully launched a scientifically valid and regulatory compliant Registry and database intended to become a robust library of tumor biomarker results linked to clinical outcomes data. As the library grows, data mining could provide vital information access to researchers, pharmaceutical firms, government, academia and insurers for use in drug development, molecular and biomarker research, economic impact assessments, healthcare policy discussion and most importantly directing personalized cancer treatment.

National Center for Genomic Analysis Support (NCGAS) is an NSF-funded center whose goal is to help biologists optimize their bioinformatic pipelines to best address their research questions. To help reach these goals, NCGAS provides a range of services based on the researchers needs. We currently host gateways, Trinity Galaxy and Genepattern, with built-in workflows to support cancer researchers who have little background in command line work. Both these gateways are projects funded by the Information Technologies in Cancer Research (ITCR) program of NIH, NCI, to aid in the genomic and transcriptomic characterization of cancers. NSF-funded researchers comfortable in command line can gain access to IU cyberinfrastructure (HIPAA compliant), to run or develop bioinformatic workflows. Bioinformatic packages are already installed system wide, with reference databases, and new programs are always added upon user’s request. NCGAS is also an ambassador of Extreme Science and Engineering Discovery Environment (XSEDE) resources, providing access to other HIPAA compliant national cyberinfrastructure (Texas Advanced Computing Center, Pittsburgh Supercomputing Center, etc.), available to the entire research community. XSEDE resources include the Jetstream cloud, based at IU and TACC, allowing researchers to use preconfigured virtual machines (VMs) already setup, create custom VM environments for their own work, and to share with classes and collaborators. Data can be transferred and managed between all of these resources securely with already available Globus endpoints which allows transfer for terabytes of data quickly. Additionally, NCGAS hosts workshops to help researchers get started. These training events can be scheduled online or at your local university upon request. NCGAS currently supports projects working on transcriptome, genome-level assembly, phylogenetics, metagenomics, and meta-transcriptomics analysis through providing a range of compute resources that best serve the research project. Citation Format: Bhavya Papudeshi, Carrie Ganote, Sheri Sanders, Thomas G. Doak. National cyberinfrastructure and bioinformatic analysis support available to the cancer research community [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5109.

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One of the challenges to adoption of HPC is the disjunction between those who need it and those who know it. Biology (specifically, genomics) is a growing field for computational use, but the typical biologist does not have an established informatics background. The National Center for Genome Analysis Support (NCGAS) aids users in getting past the initial shock of the command line and guides them toward savvy cluster use. NCGAS is initiating a push to become domain champions alongside Oklahoma State's Brian Cougar. Our position at IU gives us a close relationship with XSEDE and we already fulfill a role in pushing users toward XSEDE resources when our local clusters are ill-suited to the job. We currently act as liaison between biologists and Jetstream, IU and TACC's research computing cloud. Typical issues include: Software installation; Software usage -what parameters do I choose, and how do I interpret the results; Batch job submission; Understanding how queues and job handlers work; Data movement, Spinning up VMs on Jetstream We will discuss how we have structured our support, and illustrate our impact on XSEDE resources.