Biology subjects

Stoeckert, C. J.

Publications and source records attributed to Stoeckert, C. J..

7 recordsLinked to original sources

bioRxiv2025

Development of an Application Ontology for Beta Cell Genomics Based On the Ontology for Biomedical Investigations

The development process for the beta cell genomics application ontology (BCGO) is described. This process should be generally applicable and consists of integration of a subset of reference ontologies. A key element is use of the Ontology for Biomedical Investigation (OBI) as an ontology framework. Another element is enriching ontologies using existing patterns when needed. The ontology is validated in three aspects based on our needs including data annotation, queries and automated classification. The BCGO is available on: http://purl.obolibrary.org/obo/bcgo.owl.

bioinformatics↗

bioRxiv2021

OBO Foundry in 2021: Operationalizing Open Data Principles to Evaluate Ontologies

Biological ontologies are used to organize, curate, and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application, and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontologys compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data FAIR.

bioinformatics↗

bioRxiv2021

Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing

Over the last two decades, molecular biology has been changed by the introduction of high-throughput technologies. Data sharing requirements have prompted the establishment of persistent data archives. A standardized approach for recording and managing these data was first proposed in the Minimal Information About a Microarray Experiment (MIAME) guidelines. The Minimal Information about a high throughput nucleotide Sequencing Experiment (MINSEQE) proposal was introduced in 2008 as a logical extension of the guidelines to next-generation sequencing (NGS) technologies used for transcriptome analysis. We present a historical snapshot of the data-sharing situation focusing on transcriptomics data from both microarray and RNA-sequencing experiments published between 2009 and 2013, a period during which RNA-seq studies became increasingly popular for transcriptome analysis. We assess how much data from RNA-seq based experiments is actually available in persistent data archives, compared to data derived from microarray based experiments, and evaluate how these types of data differ. Based on this analysis, we provide recommendations to improve RNA-seq data availability, reusability, and reproducibility.

bioinformatics↗

bioRxiv2020

NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's Disease genetic and genomic knowledge

INTRODUCTIONThe NIAGADS Alzheimers Genomics Database (GenomicsDB) is a public knowledgebase of Alzheimers disease (AD) genetic datasets and genomic annotations. METHODSIt uses a custom systems architecture to adopt and enforce rigorous standards that facilitate harmonization of AD-relevant GWAS summary statistics datasets with functional annotations, including a database of >230 million annotated variants from the AD Sequencing Projects joint-calling efforts. RESULTSThe knowledgebase generates genome browser tracks and interactive compiled from harmonized datasets and annotations in the underlying database. These facilitate data sharing and discovery, by contextualizing AD-risk associations in a broader functional genomic context or summarizing them in the context of functionally annotated genes and variants. DISCUSSIONCreated to make AD-genetics knowledge more accessible to AD-researchers, the GenomicsDB shares annotated AD-relevant summary statistics datasets via a web interface designed to guide users unfamiliar with genetic data in not only exploring, but also interpreting this ever-growing volume of data.

bioinformatics↗

bioRxiv2020

A novel tool for standardizing clinical data in a realism-based common data model

Standardizing clinical information in a common data model is important for promoting interoperability and facilitating high quality research. Semantic Web technologies such as Resource Description Framework can be utilized to their full potential when a clinical data model accurately reflects the reality of the clinical situation it describes. To this end, the Open Biomedical Ontologies Foundry provides a set of ontologies that conform to the principles of realism and can be used to create a realism-based clinical data model. However, the challenge of programmatically defining such a model and loading data from disparate sources into the model has not been addressed by pre-existing software solutions. The PennTURBO Semantic Engine is a tool developed at the University of Pennsylvania that works in conjunction with data aggregation software to transform source-specific RDF data into a source-independent, realism-based data model. This system sources classes from an application ontology and specifically defines how instances of those classes may relate to each other. Additionally, the system defines and executes RDF data transformations by launching dynamically generated SPARQL update statements. The Semantic Engine was designed as a generalizable RDF data standardization tool, and is able to work with various data models and incoming data sources. Its human-readable configuration files can easily be shared between institutions, providing the basis for collaboration on a standard realism-based clinical data model.

bioinformatics↗

bioRxiv2017

iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks

Weighted-gene correlation network analysis (WGCNA) is frequently used to identify highly co-expressed clusters of genes (modules) within whole-transcriptome datasets. However, transcriptome-scale networks tend to be highly connected, making it challenging for the hierarchical clustering underlying the WGCNA-based classification to discriminate coherently expressed gene sets without significant information loss from either a priori filtering of the expression dataset or a posteriori pruning of the cluster dendrogram.\n\nHere we present iterativeWGCNA, a Python-wrapped extension for the WGCNA R software package that improves the robustness of detected modules and minimizes information loss. The method works by pruning poorly fitting genes from estimated modules and then re-running WGCNA to refine gene clusters. After refining, pruned genes are assembled into a new expression dataset to isolate overlapping modules and the process repeated. In doing so, iterativeWGCNA provides an unsupervised, non-biased filtering to generate a robust, comprehensive network-based classification of whole-transcriptome expression datasets.

bioinformatics↗

bioRxiv2017

MicrobiomeDB: a systems biology platform for integrating, mining and analyzing microbiome experiments.

MicrobiomeDB (http://microbiomeDB.org) is a data discovery and analysis platform that empowers researchers to fully leverage experimental variables to interrogate microbiome datasets. MicrobiomeDB was developed in collaboration with the Eukaryotic Pathogens Bioinformatics Resource Center (http://EuPathDB.org) and leverages the infrastructure and user interface of EuPathDB, which allows users to construct in silico experiments using an intuitive graphical strategy approach. The current release of the database integrates microbial census data with sample details for nearly 14,000 samples originating from human, animal and environmental sources, including over 9,000 samples from healthy human subjects in the Human Microbiome Project (http://portal.ihmpdcc.org/). Query results can be statistically analyzed and graphically visualized via interactive web applications launched directly in the browser, providing insight into microbial community diversity and allowing users to identify taxa associated with any experimental covariate.

microbiology↗