The Aliment to Bodily Condition knowledgebase (ABCkb): a database connecting plants and human health.

Objective: Overconsumption of processed foods has led to an increase in chronic diet-related diseases such obesity and type 2 diabetes. Although diets high in fresh fruits and vegetables are linked with healthier outcomes, the specific mechanisms for these relationships are poorly understood. Experiments examining plant phytochemical production and breeding programs, or separately on the health effects of nutritional supplements have yielded results that are sparse, siloed, and difficult to integrate between the domains of human health and agriculture.

Automated gene data integration with Databio.

Objective: 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.

Connecting nutrition composition measures to biomedical research.

Objectives: 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.

A Consensus Map in Cultivated Hexaploid Oat Reveals Conserved Grass Synteny with Substantial Subgenome Rearrangement.

Hexaploid oat ( L., 2 = 6 = 42) is a member of the Poaceae family and has a large genome (∼12.5 Gb) containing 21 chromosome pairs from three ancestral genomes.

Lollipops in the Clinic: Information Dense Mutation Plots for Precision Medicine.

Introduction: 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.

Cross-Species Integrative Functional Genomics in GeneWeaver Reveals a Role for Pafah1b1 in Altered Response to Alcohol.

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 genome-wide functional genomics studies. A wealth of such data exists, but it is often found in disparate, non-computable forms.

Identification of a QTL in Mus musculus for alcohol preference, withdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics.

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.

Performing integrative functional genomics analysis in GeneWeaver.org.

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.

Cross species integration of functional genomics experiments.

There is an increasing recognition of the value in integrating behavioral genomics data across species. The fragmentation of public resources, interoperability, and available representations present challenges due to the array of identifiers used to represent each genome feature. Once data are organized into a coherent collection, they can be integrated using a variety of methods to analyze convergent evidence for the roles of genes in behaviors.

A systematic comparison of genome-scale clustering algorithms.

Background: A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation.

GeneWeaver: a web-based system for integrative functional genomics.

High-throughput genome technologies have produced a wealth of data on the association of genes and gene products to biological functions. Investigators have discovered value in combining their experimental results with published genome-wide association studies, quantitative trait locus, microarray, RNA-sequencing and mutant phenotyping studies to identify gene-function associations across diverse experiments, species, conditions, behaviors or biological processes. These experimental results are typically derived from disparate data repositories, publication supplements or reconstructions from primary data stores.

Autism candidate genes via mouse phenomics.

Autism spectrum disorders (ASD) represent a group of developmental disabilities with a strong genetic basis. The laboratory mouse is increasingly used as a model organism for ASD, and MGI, the Mouse Genome Informatics resource, is the primary model organism database for the laboratory mouse. MGI uses the Mammalian Phenotype (MP) ontology to describe mouse models of human diseases.

Ontological Discovery Environment: a system for integrating gene-phenotype associations.

The wealth of genomic technologies has enabled biologists to rapidly ascribe phenotypic characters to biological substrates. Central to effective biological investigation is the operational definition of the process under investigation. We propose an elucidation of categories of biological characters, including disease relevant traits, based on natural endogenous processes and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and current semantics such as disease names and processes.