A FAIR-Based Approach to Enhancing the Discovery and Re-Use of Transcriptomic Data Assets for Nuclear Receptor Signaling Pathways

Public transcriptomic assets in the nuclear receptor (NR) signaling field hold considerable collective potential for exposing underappreciated aspects of NR regulation of gene expression. This potential is undermined however by a series of enduring informatic pain points that retard the routine re-use of these datasets. Here we describe a coordinated biocuration and web development approach to redress this situation that is closely aligned with ideals articulated in the FAIR (findable, accessible, interoperable, re-usable) principles on data stewardship. To improve findability, biocurators engage authors of studies in collaborating journals to secure datasets for deposition in public archives. Annotated derivatives of the archived datasets are assigned digital object identifiers and regulatory molecule identifiers that support persistent linkages between datasets and their associated research articles, integration in relevant records in gene and small molecule knowledgebases, and indexing by dataset search engines. To enhance their accessibility and interoperability, datasets are visualizable in responsively designed web pages, retrievable in machine-readable spreadsheets, or through an application programming interface. Re-use of the datasets is supported by their interrogation as a universe of data points through the Transcriptomine search engine, highlighting transcriptional intersections between NR signaling pathways, physiological processes and disease states. We illustrate the value of our approach in connecting disparate research communities using a use case of persistent interoperability between the Nuclear Receptor Signaling Atlas and the Pharmacogenomics Knowledgebase. Our FAIR-aligned model demonstrates the enduring value of discovery-scale datasets that accrues from their systematic compilation, biocuration and distribution across the digital biomedical research enterprise

Presenting and Sharing Clinical Data using the eTRIKS Standards Master Tree for tranSMART

Motivation Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. Results We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. Availability The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS - Master Tree branch, where the discussed examples can be visualized

Data from: Novel conserved genotypes correspond to antibiotic resistance phenotypes of E. coli clinical isolates

Current efforts to understand antibiotic resistance on the whole genome scale tend to focus on known genes even as high throughput sequencing strategies uncover novel mechanisms. To identify genomic variations associated with antibiotic resistance, we employed a modified genome-wide association study; we sequenced genomic DNA from pools of E. coli clinical isolates with similar antibiotic resistance phenotypes using SOLiD technology to uncover SNPs unanimously conserved in each pool. The multidrug-resistant pools were genotypically similar to SMS-3-5, a previously sequenced multidrug-resistant isolate from a polluted environment. The similarity was evenly spread across the entire genome and not limited to plasmid or pathogenicity island loci. Among the pools of clinical isolates, genomic variation was concentrated adjacent to previously reported inversion and duplication differences between the SMS-3-5 isolate and the drug-susceptible laboratory strain, DH10B. Single nucleotide polymorphisms (SNPs) that result in non-synonymous changes in gyrA (encoding the well-known S83L allele associated with fluoroquinolone resistance), mutM, ligB, and recG were unanimously conserved in every fluoroquinolone-resistant pool. Alleles of the latter three genes are tightly linked among most sequenced E. coli genomes, and had not been implicated in antibiotic resistance previously. The changes in these genes map to amino acid positions in alpha helices that are involved in DNA binding. Plasmid- encoded complementation of null strains with either allelic variant of mutM or ligB resulted in variable responses to ultraviolet light or hydrogen peroxide treatment as markers of induced DNA damage, indicating their importance in DNA metabolism and revealing a potential mechanism for fluoroquinolone resistance. Our approach uncovered evidence that additional DNA binding enzymes may contribute to fluoroquinolone resistance and further implicate environmental bacteria as a reservoir for antibiotic resistance

Google Page Rank of selected biomedical research resources.

<p>Google Page Rank of selected biomedical research resources.</p