Biology subjects

Rutherford, K. M.

Publications and source records attributed to Rutherford, K. M..

5 recordsLinked to original sources

bioRxiv2025

RNAcentral in 2026: Genes and literature integration

RNAcentral was founded in 2014 to serve as a comprehensive database of non-coding RNA sequences. It began by providing a single unified interface to more specialised resources, and now contains 45 million sequences. It has grown beyond providing a single interface to many specialised resources and now provides several services and analyses. These include secondary structure prediction with R2DT, sequence search, and analysis with Rfam. Since its last publication in 2021, RNAcentral has developed two major features. First, literature integration with the development of LitScan and LitSumm. LitScan automatically identifies and links relevant publications to RNA entries, while LitSumm uses natural language processing to generate functional summaries from the literature. Together, these tools address the critical challenge of connecting sequence data with scattered functional knowledge across thousands of publications. Secondly, RNAcentral has created gene level entries. Gene level entries represent a large structural change to RNAcentral. While RNAcentral previously organized data exclusively at the sequence level, we now group related transcripts into gene-centric views. This allows researchers to explore all isoforms, splice variants, and related sequences for a gene in a unified interface, better reflecting biological organization and facilitating comparative analyses. RNAcentral is freely available at: https://rnacentral.org.

bioinformatics↗

bioRxiv2021

JaponicusDB: Rapid deployment of a model organism database for an emerging model species

The fission yeast Schizosaccharomyces japonicus has recently emerged as a powerful system for studying the evolution of essential cellular processes, drawing on similarities as well as key differences between S. japonicus and the related, well-established model Schizosaccharomyces pombe. We have deployed the open-source, modular code and tools originally developed for PomBase, the S. pombe model organism database (MOD), to create JaponicusDB (www.japonicusdb.org), a new MOD dedicated to S. japonicus. By providing a central resource with ready access to a growing body of experimental data, ontology-based curation, seamless browsing and querying, and the ability to integrate new data with existing knowledge, JaponicusDB supports fission yeast biologists to a far greater extent than any other source of S. japonicus data. JaponicusDB thus enables S. japonicus researchers to realise the full potential of studying a newly emerging model species, and illustrates the widely applicable power and utility of harnessing reusable PomBase code to build a comprehensive, community-maintainable repository of species-relevant knowledge.

bioinformatics↗

bioRxiv2021

Fission stories: Using PomBase to understand Schizosaccharomyces pombe biology

PomBase (www.pombase.org), the model organism database (MOD) for the fission yeast Schizosaccharomyces pombe, supports research within and beyond the S. pombe community by integrating and presenting genetic, molecular, and cell biological knowledge into intuitive displays and comprehensive data collections. With new content, novel query capabilities, and biologist-friendly data summaries and visualisation, PomBase also drives innovation in the MOD community.

bioinformatics↗

bioRxiv2020

Term Matrix: A novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally, and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes likely reflects errors in literature curation, ontology structure, or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g., amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 2.5 million automatically propagated annotations across all taxa.

bioinformatics↗

bioRxiv2018

Stress, novel sex genes and epigenetic reprogramming orchestrate socially-controlled sex change

Bluehead wrasses undergo dramatic, socially-cued female to male sex change. We apply transcriptomic and methylome approaches in this wild coral reef fish to identify the primary trigger and subsequent molecular cascade of gonadal metamorphosis. Our data suggest that the environmental stimulus is exerted via the stress axis, that repression of the aromatase gene (encoding the enzyme converting androgens to estrogens) triggers a cascaded collapse of feminizing gene expression, and identifies notable sex-specific gene neofunctionalization. Furthermore, sex change involves distinct epigenetic reprogramming and an intermediate state with altered epigenetic machinery expression akin to the early developmental cells of mammals. These findings reveal at a molecular level how a normally committed developmental process remains plastic and is reversed to completely alter organ structures.\n\nOne Sentence SummaryOvary to testis transformation in a sex-changing fish involves transcriptomic and epigenomic reprogramming.

molecular biology↗