Sandra E. Orchard

The paper presents a manually curated dataset of protein binding domains, linking them to molecular interactions and genomic variations for benchmarking and disease mechanism studies.

This work analyzes CAFA5 submissions, showing improved accuracy in protein function prediction and highlights the role of protein language models and 3D structures in performance gains.

The paper introduces a method to select Reference Proteomes for UniProt, increasing coverage and reducing redundancy in the knowledgebase.

The paper identifies 25% of 7,264 non-canonical open reading frames (ncORFs) as producing translated gene products, supported by over 3,000 peptides from 3.8 billion mass spectra.

The paper expands and refines a reference catalog of human non-canonical open reading frames (ncORFs) by incorporating more datasets and introducing a translation signature score framework to prioritize high-quality ncORFs.

Machine learning integration of over 25,000 mass spectrometry experiments yields 15,326 human protein complexes covering 13,769 proteins, plus 511 AlphaFold-derived mutually exclusive subunit pairs.

The paper constructs a tissue-specific protein interaction atlas using proteomic data from 11 human tissues, identifying key drivers of inter-tissue variation and prioritizing disease genes in brain disorders.

The paper analyzes microRNA interactions in three rare disorders to assess data availability and annotation consistency across databases.

The paper uses network-based expansion of GWAS results to identify pleiotropic gene modules linked to multiple traits and demonstrates their application in studying inflammatory bowel disease.

The paper reannotates ligand binding sites in UniProtKB using ChEBI identifiers and improves search facilities for these annotations.

The paper curates and expands the yeast complexome, showing increased genetic interactions and functional links among complex members.

The paper assesses the value and sustainability of ELIXIR Core Data Resources, showing their critical role in preserving biological data and highlighting funding challenges.

The paper curates a dataset of physical molecular interactions between SARS-CoV-2, SARS-CoV-1, and other Coronaviridae proteins, providing a resource for studying virus-host interactions.

The paper updates the HPP MS Data Interpretation Guidelines to improve accuracy and address new workflows and data standards.

The paper introduces CausalTAB, an updated format for representing causal signaling interactions, enhancing the precision and coverage of signaling network modeling.

The paper presents a dataset of 28,000 annotations describing how amino acid mutations affect protein interactions, available through the IntAct website.

The paper provides best practice data life cycle approaches for life sciences researchers focusing on omics datasets and computational data analysis.