Jeremy J. Jay

Although sequencing and other high-throughput data production technologies are increasingly affordable, data analysis and interpretation remains a significant factor in the cost of -omics studies. Despite the broad acceptance of findable, accessible, interoperable, and reusable (FAIR) data principles which focus on data discoverability and annotation, data integration remains a significant bottleneck in linking prior work in order to better understand novel research. Relevant and timely information discovery is difficult for increasingly multi-disciplinary projects when scientists cannot easily keep up with work across multiple fields. Computational tools are necessary to accurately describe data contents, and empower linkage to existing resources without prior knowledge of the various database resources. We developed the Databio tool, accessible at https://datab.io/, to automate data parsing, identifier detection, and streamline common tasks to provide a point-and-click approach to data manipulation and integration in life sciences research and translational medicine. Databio uses fast real-time data structures and a data warehouse of 137 million identifiers, with automated heuristics to describe data provenance without highly specialized knowledge or bioinformatics training.

Summary: Databio is capable of providing fast and accurate annotation of gene-oriented data sets, coupled with an integrated identifier conversion service to empower downstream data mining and computational analysis. Databio is enabled by fast real-time data structures applied to over 137 million unique identifiers, and uses automated heuristics to permit accurate data provenance without highly specialized knowledge and bioinformatics training. Availability and Implementation: Freely available on the web at https://datab.io/ . Source code and binaries are freely available for download at https://github.com/joiningdata/databio/ , implemented in Go and supported on Linux, Windows, and macOS.

Biomedical research is gaining ground on human disease through many types of “omics”, which is leading to increasingly effective treatments and broad applications for precision medicine. The majority of disease treatments still revolve around drugs and biologics. Although food is consumed in much higher quantities, we understand very little about how the human body metabolizes and uses the full range of nutrients, or how these processes affect human health and disease risk. Nutrient composition databases are used by dietitians to describe common consumer food products, but these fail to identify chemicals with the same nomenclature as metabolic pathways in basic life sciences research and with far less precision. Consumer-oriented nutrient compositions often describe generic substances (e.g. Sugars) while scientific reporting is often much more specific (e.g. Dextrose, Fructose, etc.). Integrating these two fields of research presents a difficult challenge for novel applications of precision nutrition. This data set provides a manually curated collection of nutrient identifiers from the USDA’s Nutrition Data Bases and maps them to PubChem (a resource for cheminformatics and drug discovery research), biomedical literature records in PubMed using Medical Subject Headings, biological pathways using the Chemical Entities of Biological Interest ontology.

Concise visualization is critical to present large amounts of information in a minimal space that can be interpreted quickly. Clinical applications in precision medicine present an important use case due to the time dependent nature of the interpretations, although visualization is increasingly necessary across the life sciences. In this paper we describe the Lollipops software for the presentation of panel or exome sequencing results. Source code and binaries are freely available at https://github.com/pbnjay/lollipops. Although other software and web resources exist to produce lollipop diagrams, these packages are less suited to clinical applications. The demands of precision medicine require the ability to easily fit into a workflow and incorporate external information without manual intervention. The Lollipops software provides a simple command line interface that only requires an official gene symbol and mutation list making it easily scriptable. External information is integrated using the publicly available Uniprot and Pfam resources. Heuristics are used to select the most informative components and condense them for a concise plot. The output is a flexible Scalable Vector Graphic (SVG) diagram that can be displayed in a web page or graphic illustration tool. The Lollipops software creates information-dense, publication-quality mutation plots for automated pipelines and high-throughput workflows in precision medicine. The automatic data integration enables clinical data security, and visualization heuristics concisely present knowledge with minimal user configuration.

Hexaploid oat ( Avena sativa L., 2 n = 6 x = 42) is a member of the Poaceae family and has a large genome (∼12.5 Gb) containing 21 chromosome pairs from three ancestral genomes. Physical rearrangements among parental genomes have hindered the development of linkage maps in this species. The objective of this work was to develop a single high‐density consensus linkage map that is representative of the majority of commonly grown oat varieties. Data from a cDNA‐derived single‐nucleotide polymorphism (SNP) array and genotyping‐by‐sequencing (GBS) were collected from the progeny of 12 biparental recombinant inbred line populations derived from 19 parents representing oat germplasm cultivated primarily in North America. Linkage groups from all mapping populations were compared to identify 21 clusters of conserved collinearity. Linkage groups within each cluster were then merged into 21 consensus chromosomes, generating a framework consensus map of 7202 markers spanning 2843 cM. An additional 9678 markers were placed on this map with a lower degree of certainty. Assignment to physical chromosomes with high confidence was made for nine chromosomes. Comparison of homeologous regions among oat chromosomes and matches to orthologous regions of rice ( Oryza sativa L.) reveal that the hexaploid oat genome has been highly rearranged relative to its ancestral diploid genomes as a result of frequent translocations among chromosomes. Heterogeneous chromosome rearrangements among populations were also evident, probably accounting for the failure of some linkage groups to match the consensus. This work contributes to a further understanding of the organization and evolution of hexaploid grass genomes.

Background: One of our goals for the echinoderm tree of life project (http://echinotol.org) is to identify orthologs suitable for phylogenetic analysis from next-generation transcriptome data. The current dataset is the largest assembled for echinoderm phylogeny and transcriptomics. We used RNA-Seq to profile adult tissues from 42 echinoderm specimens from 24 orders and 37 families. In order to achieve sampling members of clades that span key evolutionary divergence, many of our exemplars were collected from deep and polar seas. Description: A small fraction of the transcriptome data we produced is being used for phylogenetic reconstruction. Thus to make a larger dataset available to researchers with a wide variety of interests, we made a web-based application, EchinoDB (http://echinodb.uncc.edu). EchinoDB is a repository of orthologous transcripts from echinoderms that is searchable via keywords and sequence similarity. Conclusions: From transcripts we identified 749,397 clusters of orthologous loci. We have developed the information technology to manage and search the loci their annotations with respect to the Sea Urchin (Strongylocentrotus purpuratus) genome. Several users have already taken advantage of these data for spin-off projects in developmental biology, gene family studies, and neuroscience. We hope others will search EchinoDB to discover datasets relevant to a variety of additional questions in comparative biology.

Identifying the biological substrates of complex neurobehavioral traits such as alcohol dependency pose a tremendous challenge given the diverse model systems and phenotypic assessments used. To address this problem we have developed a platform for integrated analysis of high-throughput or ‘whole genome’ functional genomics studies. A wealth of such data exists, but it is often found in disparate, non-computable forms. Our interactive web-based software system, GeneWeaver (http://www.geneweaver.org), couples curated results from genomic studies to graph theoretical tools for combinatorial analysis. Using this system we identified a gene underlying multiple alcohol-related phenotypes in four species. A search of over 60,000 gene sets in GeneWeaver’s database revealed alcohol-related experimental results including genes identified in mouse genetic mapping studies, alcohol selected Drosophila lines, Rattus differential expression and human alcoholic brains. We identified highly connected genes and compared these to genes currently annotated for alcohol-related behaviors and processes. The most highly connected gene not annotated to alcohol was Pafah1b1. Experimental validation using a Pafah1b1 conditional knock-out mouse confirmed that this gene is associated with an increased preference for alcohol and an altered thermoregulatory response to alcohol. Although this gene has not been previously implicated in alcohol-related behaviors, its function in various neural mechanisms makes a role in alcohol-related phenomena plausible. By making diverse cross-species functional genomics data readily computable, we were able to identify and confirm a novel alcohol-related gene that may have implications for alcohol use disorders and other effects of alcohol.

Extensive genetic and genomic studies of the relationship between alcohol drinking preference and withdrawal severity have been performed using animal models. Data from multiple such publications and public data resources have been incorporated in the GeneWeaver database with >60,000 gene sets including 285 alcohol withdrawal and preference-related gene sets. Among these are evidence for positional candidates regulating these behaviors in overlapping quantitative trait loci (QTL) mapped in distinct mouse populations. Combinatorial integration of functional genomics experimental results revealed a single QTL positional candidate gene in one of the loci common to both preference and withdrawal. Functional validation studies in Ap3m2 knockout mice confirmed these relationships. Genetic validation involves confirming the existence of segregating polymorphisms that could account for the phenotypic effect. By exploiting recent advances in mouse genotyping, sequence, epigenetics, and phylogeny resources, we confirmed that Ap3m2 resides in an appropriately segregating genomic region. We have demonstrated genetic and alcohol-induced regulation of Ap3m2 expression. Although sequence analysis revealed no polymorphisms in the Ap3m2-coding region that could account for all phenotypic differences, there are several upstream SNPs that could. We have identified one of these to be an H3K4me3 site that exhibits strain differences in methylation. Thus, by making cross-species functional genomics readily computable we identified a common QTL candidate for two related bio-behavioral processes via functional evidence and demonstrate sufficiency of the genetic locus as a source of variation underlying two traits.

Functional genomics experiments and analyses give rise to large sets of results, each typically quantifying the relation of molecular entities including genes, gene products, polymorphisms, and other genomic features with biological characteristics or processes. There is tremendous utility and value in using these data in an integrative fashion to find convergent evidence for the role of genes in various processes, to identify functionally similar molecular entities, or to compare processes based on their genomic correlates. However, these gene-centered data are often deposited in diverse and non-interoperable stores. Therefore, integration requires biologists to implement computational algorithms and harmonization of gene identifiers both within and across species. The GeneWeaver web-based software system brings together a large data archive from diverse functional genomics data with a suite of combinatorial tools in an interactive environment. Account management features allow data and results to be shared among user-defined groups. Users can retrieve curated gene set data, upload, store, and share their own experimental results and perform integrative analyses including novel algorithmic approaches for set–set integration of genes and functions.