Biology subjects

Eddy, J.

Publications and source records attributed to Eddy, J..

5 recordsLinked to original sources

bioRxiv2026

The GA4GH Workflow Execution Service: A Standard API for Workflow Execution and Management in Genomics

Rapid growth in life science data generation has led to large and increasingly distributed collections of sensitive genomic data. To analyse this data, researchers increasingly rely on computational workflows to enable scalable, portable and reproducible analysis. However, these workflows are executed by specialized workflow engines across heterogeneous compute environments, each with its own requirements. As a result, users often need engine- and compute-environment-specific knowledge to submit, monitor and manage workflow execution, increasing the barriers to interoperability. These challenges are compounded in large-scale projects, where workflow runs must be dispatched to local clusters, cloud platforms or data hosting infrastructures with different underlying technologies. We describe the Global Alliance for Genomics and Health (GA4GH) Workflow Execution Service (WES) API, an approved GA4GH standard first released in 2018, together with its data model and the endpoints that define a unified interface for workflow execution. By standardizing workflow invocation, the WES API allows a single client to submit, monitor and cancel workflow runs across diverse workflow engines and compute backends, such as scheduling clusters or cloud platforms. We surveyed thirteen adoptions of the standard, covering standalone services and platforms in four programming languages. In a two-site demonstration, a WES implementation from Germany and one from Japan executed the same federated allele-frequency analysis with site-specific adjustments and only aggregated results leaving either site, demonstrating that it is possible to run the same analysis across independently built and operated WES services in different countries. From the gaps identified, we propose extensions for community discussion.

bioinformatics↗

bioRxiv2026

Divergent excitatory and inhibitory signaling in a head direction circuit

Neural cell types are often clustered into inhibitory, excitatory, or neuromodulatory populations. However, the signaling mechanism between two neurons ultimately depends on their neurotransmitter-receptor pairings. Here, we demonstrate an example of a glutamatergic population of neurons that appears to either excite or inhibit their downstream partners depending on the type of glutamate receptor expressed in the downstream cell type. The upstream population encodes a sinusoidal head direction signal. The downstream partners that are excited by glutamate encode the same signal while the downstream partners that are inhibited by glutamate encode a 180{degrees} phase-shifted copy. The upstream population therefore appears to pass on two different phases of the same signal by making either excitatory or inhibitory connections to different downstream partners.

neuroscience↗

bioRxiv2024

AI-readiness for Biomedical Data: Bridge2AI Recommendations

Biomedical research is rapidly adopting artificial intelligence (AI). Yet the inherent complexity of biomedical data preparation requires implementing actionable, robust criteria for ethical and explainable AI (XAI) at the "pre-model" stage, encompassing data acquisition, detailed transformations, and ethical governance. Simple conformance to FAIR (Findable, Accessible, Interoperable, Reusable) Principles is insufficient. Here, we define criteria and practices for reliable AI-readiness of biomedical data, developed by the NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group across seven core dimensions of dataset AI-readiness: FAIRness, Provenance, Characterization, Ethics, Pre-model Explainability, Sustainability, and Computability. Conformance to these criteria provides a basis for pre-model scientific rigor and ethical integrity, mitigating downstream risks of bias and error prior to AI modeling. We apply and evaluate these standards across all four Bridge2AI flagship datasets, spanning functional genomics to clinical medicine, and encode them in machine-actionable metadata bound to the datasets. This framework sets a benchmark for preparing ethical, reusable datasets in biomedical AI and provides standardized methods for reliable pre-model data evaluation.

bioinformatics↗

bioRxiv2023

Precision-Cut Liver Slices as an ex vivo model to evaluate antifibrotic therapies for liver fibrosis and cirrhosis

BackgroundPrecision-Cut Liver Slices (PCLS) are an ex vivo culture model developed to study hepatic drug metabolism. One of the main benefits of this model is that it retains the structure and cellular composition of the native liver. PCLS also represents a potential model system to study liver fibrosis in a setting that more closely approximates in vivo pathology than in vitro methods. The aim of this study was to assess whether responses to antifibrotic interventions can be detected and quantified with PCLS. MethodsPCLS of 250 m thickness were prepared from four different murine fibrotic liver models: choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD), thioacetamide (TAA), diethylnitrosamine (DEN), and carbon tetrachloride (CCl4). PCLS were treated with 5 M Erlotinib for 72 hours. Histology and gene expression were then compared with in vivo murine experiments and TGF-{beta}1 activated hepatic stellate cells (HSCs). These types of PCLS characterization were also evaluated in PCLS from human cirrhotic liver. ResultsPCLS viability in culture was stable for 72 hours. Treatment of erlotinib, an EGFR inhibitor significantly inhibited the expression of profibrogenic genes Il6, Col1a1 and Timp1 in PCLS from CDAHFD-induced cirrhotic mice, and Il6, Col1a1 and Tgfb1 in PCLS from TAA-induced cirrhotic rats. Erlotinib treatment of PCLS from DEN-induced cirrhotic rats inhibited the expression of Col1a1, Timp1, Tgfb1 and Il6, which was consistent with the impact of erlotinib on Col1a1 and Tgfb1 expression in in vivo DEN-induced cirrhosis. Erlotinib treatment of PCLS from CCl4-induced cirrhosis caused reduced expression of Timp1, Col1a1 and Tgfb1, which was consistent with the effect of erlotinib in in vivo CCl4-induced cirrhosis. In addition, in HSCs at PCLS from normal mice, TGF-{beta}1 treatment upregulated Acta2 (SMA), while treatment with erlotinib inhibited the expression of Acta2. Similar expression results were observed in TGF-{beta}1 treated in vitro HSCs. Expression of MMPs and TIMPs, key regulators of fibrosis progression and regression, were also significantly altered under erlotinib treatment in PCLS. Expression changes under erlotinib treatment were also corroborated with PCLS from human cirrhosis samples. ConclusionThe responses to antifibrotic interventions can be detected and quantified with PCLS at the gene expression level. The antifibrotic effects of erlotinib are consistent between PCLS models of murine cirrhosis and those observed in vivo and in vitro. Similar effects were also reproduced in PCLS derived from patients with cirrhosis. PCLS is an excellent model to assess antifibrotic therapies that is aligned with the principles of Replacement, Reduction and Refinement (3Rs).

cell biology↗

bioRxiv2019

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimers Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes), and provide the results as a community resource. We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. In addition, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a common resource for use across the community in research programs. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

genetics↗