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SoyBase, a USDA genetic and genomics database, holds professionally curated soybean genetic and genomic data, which is integrated and made accessible to researchers and breeders. The site holds several reference genome assemblies, as well as genetic maps, thousands of mapped traits, expression and epigenetic data, pedigree information, and extensive variant and genotyping data sets. SoyBase displays include genetic, genomic, and epigenetic maps of the soybean genome. Gene expression data is presented in the genome viewer as heat maps and pictorial and tabular displays in gene report pages. Millions of sequence variants have been added, representing variations across various collections of cultivars. This variant data is explorable using new interactive tools to visualize the distribution of those variants across the genome, between selected accessions. SoyBase holds several reference-quality soybean genome assemblies, accessible via various query tools and browsers, including a new visualization system for exploring the soybean pan-genome. SoyBase also serves as a nexus of announcements pertinent to the greater soybean research community. The database also includes a soybean-specific anatomic and biochemical trait ontology. The database can be accessed at https://soybase.org.

In this chapter, we introduce the main components of the Legume Information System ( https://legumeinfo.org ) and several associated resources. Additionally, we provide an example of their use by exploring a biological question: is there a common molecular basis, across legume species, that underlies the photoperiod-mediated transition from vegetative to reproductive development, that is, days to flowering? The Legume Information System (LIS) holds genetic and genomic data for a large number of crop and model legumes and provides a set of online bioinformatic tools designed to help biologists address questions and tasks related to legume biology. Such tasks include identifying the molecular basis of agronomic traits; identifying orthologs/syntelogs for known genes; determining gene expression patterns; accessing genomic datasets; identifying markers for breeding work; and identifying genetic similarities and differences among selected accessions. LIS integrates with other legume-focused informatics resources such as SoyBase ( https://soybase.org ), PeanutBase ( https://peanutbase.org ), and projects of the Legume Federation ( https://legumefederation.org ).

As sequencing prices drop, genomic data accumulates-seemingly at a steadily increasing pace. Most genomic data potentially have value beyond the initial purpose-but only if shared with the scientific community. This, of course, is often easier said than done. Some of the challenges in sharing genomic data include data volume (raw file sizes and number of files), complexities, formats, nomenclatures, metadata descriptions, and the choice of a repository. In this paper, we describe 10 quick tips for sharing open genomic data.

epiSNP is a program for identifying pairwise single nucleotide polymorphism (SNP) interactions (epistasis) in quantitative-trait genome-wide association studies (GWAS). A parallel MPI version (EPISNPmpi) was created in 2008 to address this computationally expensive analysis on large data sets with many quantitative traits and SNP markers. However, the falling cost of genotyping has led to an explosion of large-scale GWAS data sets that challenge EPISNPmpi’s ability to compute results in a reasonable amount of time. Therefore, we optimized epiSNP for modern multi-core and highly parallel many-core processors to efficiently handle these large data sets. This paper describes the serial optimizations, dynamic load balancing using MPI-3 RMA operations, and shared-memory parallelization with OpenMP to further enhance load balancing and allow execution on the Intel Xeon Phi coprocessor (MIC). For a large GWAS data set, our optimizations provided a 38.43× speedup over EPISNPmpi on 126 nodes using 2 MICs on TACC’s Stampede Supercomputer. We also describe a Coarray Fortran (CAF) version that demonstrates the suitability of PGAS languages for problems with this computational pattern. We show that the Coarray version performs competitively with the MPI version on the NERSC Edison Cray XC30 supercomputer. Finally, the performance benefits of hyper-threading for this application on Edison (average 1.35× speedup) are demonstrated.

Legume Information System (LIS), at http://legumeinfo.org, is a genomic data portal (GDP) for the legume family. LIS provides access to genetic and genomic information for major crop and model legumes. With more than two-dozen domesticated legume species, there are numerous specialists working on particular species, and also numerous GDPs for these species. LIS has been redesigned in the last three years both to better integrate data sets across the crop and model legumes, and to better accommodate specialized GDPs that serve particular legume species. To integrate data sets, LIS provides genome and map viewers, holds synteny mappings among all sequenced legume species and provides a set of gene families to allow traversal among orthologous and paralogous sequences across the legumes. To better accommodate other specialized GDPs, LIS uses open-source GMOD components where possible, and advocates use of common data templates, formats, schemas and interfaces so that data collected by one legume research community are accessible across all legume GDPs, through similar interfaces and using common APIs. This federated model for the legumes is managed as part of the 'Legume Federation' project (accessible via http://legumefederation.org), which can be thought of as an umbrella project encompassing LIS and other legume GDPs.

Powerful high performance computing systems of the future are expected to have higher failure rates than current systems. As a result, HPC applications running on such future systems are more likely to encounter a system failure than on today's machines. Application fault tolerance is therefore becoming more important to avoid costly waste of resources associated with rerunning failed applications. The MPI 3.1 standard does not address the issue of MPI process failures. Checkpoint/restart is commonly used to add fault tolerance to MPI applications. However, there can be complicated issues impacting an MPI application's ability to correctly and efficiently write checkpoint files, particularly if Fortran I/O statements are used. Moreover, it may be inefficient restart a large number MPI processes from a checkpoint. Several MPI fault tolerance libraries, such as ULFM, are being developed to enabl MPI programs to recover from MPI process failures. This can circumvent much of the overhead of an application restart, including rescheduling, launching, initializing, and reading checkpoint data. Each library uses a different approach to recovery from MPI process failures. Unfortunately, some of the proposed recovery models are incompatible with Fortran. This paper intends to help Fortran MPI application developers avoid problems when developing fault-tolerant MPI applications.

SAMtools is a suite of tools that is widely-used in genomics workflows for post-processing sequence alignment data from large high-throughput sequencing data sets. A common use of SAMtools is to sort the standard Binary Alignment/Map (BAM) format emitted by many sequence aligners. This can be computationally- and I/O-intensive: BAM files can be many gigabytes in size, and may need to be decompressed before sorting and compressed afterwards. As a result, BAM-file sorting can be a bottleneck in genomics workflows. This paper presents a case study on the performance characterization and optimization of BAM sorting with SAMtools. OpenMP task parallelism to enhance concurrency and memory optimization techniques were employed in both SAMtools and the underlying library HTSlib. Utilizing all 32 processor cores on the benchmark system, the optimizations resulted in a speedup of 3.92X for an in-memory sort of 24.6 GiB of BAM data (102.6 GiB uncompressed), while a 1.55X speedup was achieved for an out-of-core sort.

The partial correlation coefficient with information theory PCIT method is an important technique for detecting interactions between networks. The PCIT algorithm has been used in the biological context to infer complex regulatory mechanisms and interactions in genetic networks, in genome wide association studies, and in other similar problems. In this work, the PCIT algorithm is re-implemented with exemplary parallel, vector, input/output I/O, memory, and instruction optimizations for today's multi-core and many-core architectures. The evolution and performance of the new code targets the processor architectures of the Stampede supercomputer but will also benefit other architectures. The Stampede system consists of an Intel Xeon E5 processor base system with an innovative component consist of Intel Xeon Phi Coprocessors. Optimized results and an analysis are presented for both the Xeon and the Xeon Phi. Copyright © 2014 John Wiley & Sons, Ltd.

For species with potential as new crops, rapid improvement may be facilitated by new genomic methods. Apios (Apios americana Medik.), once a staple food source of Native American Indians, produces protein-rich tubers, tolerates a wide range of soils, and symbiotically fixes nitrogen. We report the first high-quality de novo transcriptome assembly, an expression atlas, and a set of 58,154 SNP and 39,609 gene expression markers (GEMs) for characterization of a breeding collection. Both SNPs and GEMs identify six genotypic clusters in the collection. Transcripts mapped to the Phaseolus vulgaris genome–another phaseoloid legume with the same chromosome number–provide provisional genetic locations for 46,852 SNPs. Linkage disequilibrium decays within 10 kb (based on the provisional genetic locations), consistent with outcrossing reproduction. SNPs and GEMs identify more than 21 marker-trait associations for at least 11 traits. This study demonstrates a holistic approach for mining plant collections to accelerate crop improvement.

Summary The objective of this study was to determine how prenatal and postnatal dietary omega-3 fatty acids alter white blood cell (leukocyte) DNA methylation of offspring. Fifteen gilts (n = 5 per treatment) were selected from one of three treatments: (i) control diet throughout gestation, lactation and nursery phase (CON); (ii) algal omega-3 fatty acid supplementation enriched in EPA and DHA (Gromega™) fed throughout gestation, lactation and nursery phase (Cn3); or (iii) Gromega™ supplementation maternally, during gestation and lactation only, and control diet during the nursery phase (Mn3). At 11 weeks of age and after 8 weeks of post-weaning nursery feeding, buffy coat genomic DNA was subjected to methyl CpG binding protein sequencing. The methylation enriched profile mapped to 26% of the porcine genome. On chromosome 4, a 27.7-kb differentially methylated region downstream of RUNX1T1 was hypomethylated in the Mn3 and Cn3 groups by 91.6% and 85.0% respectively compared to CON pigs. Conversely, hypermethylation was detected in intergenic regions of chromosomes 4 and 12. Regulatory impact factor and differential hubbing methods were used to identify pathways that were coordinately regulated by methylation due to feeding EPA and DHA during pregnancy. Despite limited ability to detect differential methylation, we describe methods that allow the identification of coordinated epigenetic regulation that could not otherwise be detected from subtle single locus changes in methylation. These data provide evidence of novel epigenetic regulation by maternal and early life supplementation of omega-3 fatty acids that may have implications to growth and inflammatory processes.

A comprehensive transcriptome assembly for pigeonpea has been developed by analyzing 128.9 million short Illumina GA IIx single end reads, 2.19 million single end FLX/454 reads, and 18 353 Sanger expressed sequenced tags from more than 16 genotypes. The resultant transcriptome assembly, referred to as CcTA v2, comprised 21 434 transcript assembly contigs (TACs) with an N50 of 1510 bp, the largest one being ∼8 kb. Of the 21 434 TACs, 16 622 (77.5%) could be mapped on to the soybean genome build 1.0.9 under fairly stringent alignment parameters. Based on knowledge of intron junctions, 10 009 primer pairs were designed from 5033 TACs for amplifying intron spanning regions (ISRs). By using in silico mapping of BAC-end-derived SSR loci of pigeonpea on the soybean genome as a reference, putative mapping positions at the chromosome level were predicted for 6284 ISR markers, covering all 11 pigeonpea chromosomes. A subset of 128 ISR markers were analyzed on a set of eight genotypes. While 116 markers were validated, 70 markers showed one to three alleles, with an average of 0.16 polymorphism information content (PIC) value. In summary, the CcTA v2 transcript assembly and ISR markers will serve as a useful resource to accelerate genetic research and breeding applications in pigeonpea.

The triacylglyceride (TAG) fatty acid content in meat from Angus-sired cattle was analyzed for non-additive genetic effects. A total of 11,482 significant DNA marker interactions (false discovery rate [FDR] < 0.05) were detected across thirty-seven different TAG fatty acids. Interactions were not evenly distributed amongst all fatty acids analyzed, and types of interactions (additive-by-additive, additive-by-dominance, and dominance-by-dominance) varied within each individual fatty acid. These results indicate that it may be possible to account for additional genetic variance amongst TAG fatty acids over and above individual markers.

Background The homeodomain leucine zipper (HD-Zip) transcription factor family is one of the largest plant specific superfamilies, and includes genes with roles in modulation of plant growth and response to environmental stresses. Many HD-Zip genes are characterized in Arabidopsis (Arabidopsis thaliana), and members of the family are being investigated for abiotic stress responses in rice (Oryza sativa), maize (Zea mays), poplar (Populus trichocarpa) and cucumber (Cucmis sativus). Findings in these species suggest HD-Zip genes as high priority candidates for crop improvement. Results In this study we have identified members of the HD-Zip gene family in soybean cv. ‘Williams 82’, and characterized their expression under dehydration and salt stress. Homology searches with BLASTP and Hidden Markov Model guided sequence alignments identified 101 HD-Zip genes in the soybean genome. Phylogeny reconstruction coupled with domain and gene structure analyses using soybean, Arabidopsis, rice, grape (Vitis vinifera), and Medicago truncatula homologues enabled placement of these sequences into four previously described subfamilies. Of the 101 HD-Zip genes identified in soybean, 88 exist as whole-genome duplication-derived gene pairs, indicating high retention of these genes following polyploidy in Glycine ~13 Mya. The HD-Zip genes exhibit ubiquitous expression patterns across 24 conditions that include 17 tissues of soybean. An RNA-Seq experiment performed to study differential gene expression at 0, 1, 6 and 12 hr soybean roots under dehydration and salt stress identified 20 differentially expressed (DE) genes. Several of these DE genes are orthologs of genes previously reported to play a role under abiotic stress, implying conservation of HD-Zip gene functions across species. Screening of HD-Zip promoters identified transcription factor binding sites that are overrepresented in the DE genes under both dehydration and salt stress, providing further support for the role of HD-Zip genes in abiotic stress responses. Conclusions We provide a thorough description of soybean HD-Zip genes, and identify potential candidates with probable roles in dehydration and salt stress. Expression profiles generated for all soybean genes, under dehydration and salt stress, at four time points, will serve as an important resource for the soybean research community, and will aid in understanding plant responses to abiotic stress. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-950) contains supplementary material, which is available to authorized users.

Software pipelines are often used in scientific applications in fields such as bioinformatics. Unix pipes allow processes in the pipeline to concurrently execute and communicate without disk I/O on a single shared-memory node. However, a significant number of CPU-bound, memory-bound, and I/O-bound software pipelines could benefit from the distribution of work across multiple nodes. Rewriting applications to use MPI is often time consuming. mpififo is an MPI-based tool that allows for the easy distribution of the processes in a pipeline across multiple nodes. This paper describes the design of mpififo, and presents an example of its use in a bioinformatics application. mpififo is available for download at https://bitbucket.org/nathanweeks/mpififo.

The PCIT method is an important technique for detecting interactions between networks. The PCIT algorithm has been used in the biological context to infer complex regulatory mechanisms and interactions in genetic networks, in genome wide association studies, and in other similar problems. In this work, the PCIT algorithm is re-implemented with exemplary parallel, vector, I/O, memory and instruction optimizations for today's multi- and many-core architectures. The evolution and performance of the new code targets the processor architectures of the Stampede supercomputer, but will also benefit other architectures. The Stampede system consists of an Intel Xeon E5 processor base system with an innovative component comprised of Intel Xeon Phi Coprocessors. Optimized results and an analysis are presented for both the Xeon and the Xeon Phi.

De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.

Background The nutritional and economic value of many crops is effectively a function of seed protein and oil content. Insight into the genetic and molecular control mechanisms involved in the deposition of these constituents in the developing seed is needed to guide crop improvement. A quantitative trait locus (QTL) on Linkage Group I (LG I) of soybean (Glycine max (L.) Merrill) has a striking effect on seed protein content. Results A soybean near-isogenic line (NIL) pair contrasting in seed protein and differing in an introgressed genomic segment containing the LG I protein QTL was used as a resource to demarcate the QTL region and to study variation in transcript abundance in developing seed. The LG I QTL region was delineated to less than 8.4 Mbp of genomic sequence on chromosome 20. Using Affymetrix® Soy GeneChip and high-throughput Illumina® whole transcriptome sequencing platforms, 13 genes displaying significant seed transcript accumulation differences between NILs were identified that mapped to the 8.4 Mbp LG I protein QTL region. Conclusions This study identifies gene candidates at the LG I protein QTL for potential involvement in the regulation of protein content in the soybean seed. The results demonstrate the power of complementary approaches to characterize contrasting NILs and provide genome-wide transcriptome insight towards understanding seed biology and the soybean genome.

Background The Soybean Consensus Map 4.0 facilitated the anchoring of 95.6% of the soybean whole genome sequence developed by the Joint Genome Institute, Department of Energy, but its marker density was only sufficient to properly orient 66% of the sequence scaffolds. The discovery and genetic mapping of more single nucleotide polymorphism (SNP) markers were needed to anchor and orient the remaining genome sequence. To that end, next generation sequencing and high-throughput genotyping were combined to obtain a much higher resolution genetic map that could be used to anchor and orient most of the remaining sequence and to help validate the integrity of the existing scaffold builds. Results A total of 7,108 to 25,047 predicted SNPs were discovered using a reduced representation library that was subsequently sequenced by the Illumina sequence-by-synthesis method on the clonal single molecule array platform. Using multiple SNP prediction methods, the validation rate of these SNPs ranged from 79% to 92.5%. A high resolution genetic map using 444 recombinant inbred lines was created with 1,790 SNP markers. Of the 1,790 mapped SNP markers, 1,240 markers had been selectively chosen to target existing unanchored or un-oriented sequence scaffolds, thereby increasing the amount of anchored sequence to 97%. Conclusion We have demonstrated how next generation sequencing was combined with high-throughput SNP detection assays to quickly discover large numbers of SNPs. Those SNPs were then used to create a high resolution genetic map that assisted in the assembly of scaffolds from the 8× whole genome shotgun sequences into pseudomolecules corresponding to chromosomes of the organism.

Like many other crops, the cultivated peanut (Arachis hypogaea L.) is of hybrid origin and has a polyploid genome that contains essentially complete sets of chromosomes from two ancestral species. Here we report the genome sequence of peanut and show that after its polyploid origin, the genome has evolved through mobile-element activity, deletions and by the flow of genetic information between corresponding ancestral chromosomes (that is, homeologous recombination). Uniformity of patterns of homeologous recombination at the ends of chromosomes favors a single origin for cultivated peanut and its wild counterpart A. monticola. However, through much of the genome, homeologous recombination has created diversity. Using new polyploid hybrids made from the ancestral species, we show how this can generate phenotypic changes such as spontaneous changes in the color of the flowers. We suggest that diversity generated by these genetic mechanisms helped to favor the domestication of the polyploid A. hypogaea over other diploid Arachis species cultivated by humans. Fil: Bertioli, David J.. University of Georgia; Estados Unidos Fil: Jenkins, Jerry. Hudsonalpha Institute For Biotechnology; Estados Unidos Fil: Clevenger, Josh. University of Georgia; Estados Unidos Fil: Dudchenko, Olga. The Center for Genome Architecture; Estados Unidos Fil: Gao, Dongying. University of Georgia; Estados Unidos Fil: Seijo, José Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; Argentina Fil: Leal Bertioli, Soraya C.M.. Universidad Nacional del Nordeste; Argentina Fil: Ren, Longhui. University of Georgia; Estados Unidos Fil: Farmer, Andrew D.. University of Georgia; Estados Unidos Fil: Pandey, Manish K.. Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); India Fil: Samoluk, Sergio Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; Argentina Fil: Abernathy, Brian. University of Georgia; Estados Unidos Fil: Agarwal, Gaurav. University of Georgia; Estados Unidos Fil: Ballén Taborda, Carolina. University of Georgia; Estados Unidos Fil: Cameron, Connor. National Center for Genome Resources; Estados Unidos Fil: Campbell, Jacqueline. University of Iowa; Estados Unidos Fil: Chavarro, Carolina. University of Georgia; Estados Unidos Fil: Chitikineni, Annapurna. Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); India Fil: Chu, Ye. University of Georgia; Estados Unidos Fil: Dash, Sudhansu. National Center for Genome Resources; Estados Unidos Fil: El Baidouri, Moaine. Centre National de la Recherche Scientifique; Francia Fil: Guo, Baozhu. University of Georgia; Estados Unidos Fil: Huang, Wei. University of Iowa; Estados Unidos Fil: Kim, Kyung Do. University of Georgia; Estados Unidos. Corporate R&D, LG Chem; Corea del Sur Fil: Korani, Walid. University of Georgia; Estados Unidos Fil: Lanciano, Sophie. Centre National de la Recherche Scientifique; Francia Fil: Lui, Christopher G.. The Center for Genome Architecture; Estados Unidos Fil: Mirouze, Marie. Centre National de la Recherche Scientifique; Francia Fil: Moretzsohn, Márcio C.. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Pham, Melanie. The Center for Genome Architecture; Estados Unidos Fil: Shin, Jin Hee. University of Georgia; Estados Unidos Fil: Shirasawa, Kenta Shirasawa. Department of Frontier Research and Development, Kazusa DNA Research Institute; Japón Fil: Sinharoy, Senjuti. National Institute of Plant Genome Research; India Fil: Sreedasyam, Avinash. Hudson Alpha Institute of Biotechnology; Estados Unidos Fil: Weeks, Nathan T.. United States Department of Agriculture; Estados Unidos Fil: Zhang, Xinyou. Henan Academy of Agricultural Sciences; China Fil: Zheng, Zheng. Henan Academy of Agricultural Sciences; China Fil: Sun, Ziqi. Henan Academy of Agricultural Sciences; China Fil: Froenicke, Lutz. University of California at Davis; Estados Unidos Fil: Aiden, Erez L.. The Center for Genome Architecture; Estados Unidos Fil: Michelmore, Richard. University of California at Davis; Estados Unidos Fil: Varshney, Rajeev K.. Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); India Fil: Holbrook, C. Corley. United States Department of Agriculture; Estados Unidos Fil: Cannon, Ethalinda K. S.. University of Iowa; Estados Unidos Fil: Scheffler, Brian E.. United States Department of Agriculture; Estados Unidos Fil: Grimwood, Jane. Hudson Alpha Institute of Biotechnology; Estados Unidos Fil: Ozias-Akins, Peggy. University of Georgia; Estados Unidos Fil: Cannon, Steven B.. United States Department of Agriculture; Estados Unidos Fil: Jackson, Scott A.. University of Georgia; Estados Unidos Fil: Schmutz, Jeremy. Hudson Alpha Institute of Biotechnology; Estados Unidos

Background Consumers are becoming increasingly conscientious about the nutritional value of their food. Consumption of some fatty acids has been associated with human health traits such as blood pressure and cardiovascular disease. Therefore, it is important to investigate genetic variation in content of fatty acids present in meat. Previously publications reported regions of the cattle genome that are additively associated with variation in fatty acid content. This study evaluated epistatic interactions, which could account for additional genetic variation in fatty acid content. Results Epistatic interactions for 44 fatty acid traits in a population of Angus beef cattle were evaluated with EpiSNPmpi. False discovery rate (FDR) was controlled at 5 % and was limited to well-represented genotypic combinations. Epistatic interactions were detected for 37 triacylglyceride (TAG), 36 phospholipid (PL) fatty acid traits, and three weight traits. A total of 6,181, 7,168, and 0 significant epistatic interactions (FDR