Biology subjects

Hastings, J.

Publications and source records attributed to Hastings, J..

5 recordsLinked to original sources

bioRxiv2026

ChEBI-N - Enrichment Analysis with the ChEBI Ontology

Introduction: Biological and chemical ontologies are structured representations that can be used to map entities such as metabolites onto broader chemical and biological categories. Enrichment analysis applied to such ontologies identifies classes whose representation within a set deviates from an expected background distribution, helping to interpret complex metabolomics measurements. Objectives: This study aimed to develop a tool for ChEBI (Chemical Entities of Biological Interest) ontology-based enrichment analysis, named ChEBI-N (ChEBI eNrichment). It is a successor of the previous tool BiNChE, with new features that broaden its applicability. Methods: We reimplemented BiNChE and extended it to accept SMILES or InChI strings as well as ChEBI IDs. These broaden applicability to datasets with entities that are not yet present in ChEBI. We also constructed organism-specific background populations. Results: We demonstrate the tool on three published metabolomics datasets covering wine, cheese, and human plasma samples. ChEBI-N correctly highlighted relevant enriched classes in all three datasets, such as flavonoids in the wine dataset and fatty acid classes in the cheese dataset. Comparing organism-specific and full-ChEBI backgrounds shows that restricted backgrounds reduce the inflated significance of generic classes, although the most enriched classes remain largely consistent. Conclusion: ChEBI-N is an extended successor to BiNChE for ontology-based enrichment analysis of metabolomics data, offering a hypothesis-generating complement to existing metabolomics analysis pipelines. Availability: ChEBI-N is freely available at chebin.hastingslab.org or as a Python package (pypi.org/project/chebin), with source code at github.com/ontology-tools/chebin, archived at Zenodo (doi:10.5281/zenodo.23264219) and Software Heritage (swh:1:rev:133a41792704eb7f1db746f36a5ae97e9247b2b7).

bioinformatics↗

bioRxiv2025

Ontology pre-training improves machine learning-based predictions for metabolites

Recent advances in the field of machine learning have shown that integration of expert knowledge improves performances, in particular for complex domains such as biology. Bio-ontologies offer a rich source of curated biological knowledge that can be harnessed to this end. Here, we describe an intuitive and generalisable approach to embed the knowledge contained in a classification hierarchy derived from a bio-ontology into a machine learning model as an intermediate training step between general-purpose pre-training and task-specific fine-tuning in a process that we call ontology pre-training. We show that this approach leads to an improvement in predictive performance and a reduction in training time for a broad range of prediction tasks relevant to understanding metabolite functions in living systems, using a range of datasets derived from MoleculeNet. We see the biggest improvement for regression tasks, e.g. prediction of lipophilicity and aqueous solubility of molecules, and a robust improvement for most classification tasks. Our approach can be adapted for a wide range of knowledge sources, models and prediction tasks.

bioinformatics↗

bioRxiv2023

Batch-effect correction in single-cell RNA sequencing data using JIVE

In single-cell RNA sequencing (scRNA-seq) data analysis, addressing batch effects -- technical artifacts stemming from factors such as varying sequencing technologies, equipment, and capture times -- is crucial. These factors cause unwanted variation in the data and often obfuscate the underlying biological signal of interest. The Joint and Individual Variation Explained (JIVE) method can be used to extract shared biological patterns from multi-source sequencing data while adjusting for individual non-biological variations (i.e., batch effect). However, its current implementation is originally designed for bulk sequencing data, making it computationally infeasible for large-scale single-cell sequencing datasets. In this study, we enhance JIVE for large-scale scRNA-seq data by boosting its computational efficiency and tailoring it to the single-cell context. Additionally, we introduce a novel application of JIVE which we use to perform batch-effect correction on multiple scRNA-seq datasets. Our enhanced JIVE method aims to decompose scRNA-seq datasets into a joint structure capturing the true biological variability and individual structures which capture technical variability within each batch. This joint structure is then suitable for use in downstream analyses. We employed four evaluation metrics and benchmarked the results against two other popular tools, Seurat v3 and Harmony, which were developed for this purpose. We found that JIVE performed best in metrics that consider local neighborhoods (kBET and LISI) and in scenarios in which the original data contained distinct differences between batches and cell types.

bioinformatics↗

bioRxiv2021

Non-monotonic fibril surface occlusion by GFP tags from coarse-grained molecular simulations

The pathological growth of amyloid fibrils in neurons underlies the progression of neurodegenerative diseases including Alzheimers and Parkinsons disease. Fibrils form when soluble monomers oligomerise in the cytoplasm. Their subsequent growth occurs via nucleated polymerization mechanisms involving the free ends of the fibrils augmented by secondary nucleation of new oligomers at their surface. Amyloid fibrils possess a complex interactome with diffusing cytoplasmic proteins that regulates many aspects of their growth, seeding capacity, biochemical activity and transition to pathological inclusions in diseased brains. Changes to their surface are also expected to modify their interactome, pathogenicity and spreading in the brain. Many assays visualise fibril formation, growth and inclusion formation by decorating monomeric proteins with fluorescent tags such as GFP. Recent studies from our group suggest that tags with sizes comparable to the fibril radius may modify the fibril surface accessibility and thus their PTM pattern, interactome and ability to form inclusions. Using coarse-grained molecular simulations of a single alpha synuclein fibril tagged with GFP we find that thermal fluctuations of the tags create a non-monotonic, size-dependent sieve around the fibril that perturbs its interactome with diffusing species. Our results indicate that experiments using tagged and untagged monomers to study the growth and interactome of fibrils should be compared with caution, and the confounding effects of the tags are more complex than a reduction in surface accessibility. The prevalence of fluorescent tags in amyloid fibril growth experiments suggests this has implications beyond the specific alpha synuclein fibrils we model here.

biophysics↗

bioRxiv2017

Identifiers for the 21st century:How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

In many disciplines, data is highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline ten lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers; we also outline important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

bioinformatics↗