Biology subjects

Ochoa, D.

Publications and source records attributed to Ochoa, D..

14 recordsLinked to original sources

bioRxiv2026

Turning Domain Expertise into Multi-Dimensional Evaluation of Biomedical AI with Karenina

Language models and agents are increasingly used in biomedicine, but current benchmarks reward correct answers even when the underlying reasoning is flawed. Here we introduce Karenina, an open-source framework that turns expert knowledge into multi-dimensional evaluations of questions, conversations and autonomous agents. Illustrated in Question-Answer pairs, multi-turn conversations and autonomous data-analysis, these dimensions together moves evaluation beyond scoring, enabling trustworthy decision-making with AI in biomedicine.

bioinformatics↗

bioRxiv2026

The Human Pleiotropic Map of GWAS Associations and Therapeutic Implications

Genetic support for drug targets substantially increases clinical success rates, establishing genome-wide association studies (GWAS) as central to therapeutic hypothesis generation. However, the same genetic evidence that reveals causal gene-disease relationships simultaneously exposes organism-level safety liabilities--a dimension requiring principled, genome-wide quantification. Here we systematically analyse 100,526 GWAS to yield 789,453 credible sets and gene prioritisations for 15,641 genes, with discovery showing no saturation as GWAS expand and increase diversity. We find that 64% of GWAS-implicated genes are pleiotropic, associated with traits across multiple diseases and showing a non-linear relationship between the degree of pleiotropy and clinical success. Highly pleiotropic genes--concentrated in immune, inflammatory, and oncogenic signalling programmes--are enriched in safety-terminated clinical programmes, mouse lethal knockouts, and cancer driver genes, establishing gene-level pleiotropy as a potential measure of genetically-informed organism-level safety liability. Protein-altering variant (PAV) support amplifies therapeutic signal (OR = 6.0), yet PAV targets show higher average pleiotropy, introducing a competing safety liability. Combining PAV support with intermediate pleiotropy (2-5 therapeutic areas) resolves this tension, yielding OR = 10.3 and relative success = 4.8--a profile already satisfied by 52 approved therapies. As GWAS continue to expand in scale and resolution, these findings lay the groundwork for increasingly sophisticated target discovery strategies that yield safer and more effective therapeutic hypotheses.

genomics↗

bioRxiv2026

Chemical Probes in Scientific Literature: Expanding and Validating Target-Disease Evidence

Chemical probes are indispensable tools for validating therapeutic hypotheses, yet their broader impact on early-stage drug discovery remains unquantified. To our knowledge, this study represents the first systematic, large-scale investigation of the chemical probe literature. By screening over 18 million articles using a high-quality dictionary of 561 chemical probes, we identified 20,000 articles mentioning a chemical probe which resulted in 5,558 unique target-disease (T-D) associations. Our analysis yields four principal findings that redefine the utility of these chemicals: First, we show that chemical probe evidence typically precedes the appearance of structured data in major knowledge bases by 1-7 years, providing a crucial lead time for target prioritisation. Second, we identified 353 T-D pairs (6.4%) with no prior evidence in the Open Targets Platform, highlighting the approachs discovery potential. Third, the application of strict novelty filters uncovered 135 new high-confidence associations between targets and diseases, revealing distinct opportunities for therapeutic repurposing in non-oncological, rare autoimmune diseases, and diseases without effective therapies due to complex biology or high treatment resistance. Finally, we demonstrate that chemical probes are essential for strengthening evidence, providing functional validation for associations previously supported only by weaker, correlative data such as RNA expression or animal models. Collectively, these findings illustrate that chemical probes catalyse early therapeutic discovery, emphasising the importance of cataloguing existing probes and identifying new ones.

bioinformatics↗

bioRxiv2024

Associations on the Fly, a new feature aiming to facilitate exploration of the Open Targets Platform evidence

MotivationThe Open Targets Platform (https://platform.opentargets.org) is a unique, comprehensive, open-source resource supporting systematic identification and prioritisation of targets for drug discovery. The Platform combines, harmonises and integrates data from >20 diverse sources to provide target-disease associations, covering evidence derived from genetic associations, somatic mutations, known drugs, differential expression, animal models, pathways and systems biology. An in-house target identification scoring framework weighs the evidence from each data source and type, contributing to an overall score for each of the 7.8M target-disease associations. However, the previous infrastructure did not allow user-led dynamic adjustments in the contribution of different evidence types for target prioritisation, a limitation frequently raised by our user community. Furthermore, the previous Platform user interface did not support navigation and exploration of the underlying target-disease evidence on the same page, occasionally making the user journey counterintuitive. ResultsHere, we describe "Associations on the Fly" (AOTF), a new Platform feature - developed as part of a wider product refactoring project - to enable formulation of more flexible and impactful therapeutic hypotheses through dynamic adjustment of the weight of contributing evidence from each source, altering the prioritisation of targets. Availability and implementationAll Open Targets code is available as open source: [https://github.com/opentargets]. This tool was implemented using React v18 and its code is accessible here: [https://github.com/opentargets/ot-ui-apps]. The tools described in the paper are accessible through the Open Targets Platform web interface [https://platform.opentargets.org/] and GraphQL API (https://platform-docs.opentargets.org/data-access/graphql-api). Data is available for download here: [https://platform.opentargets.org/downloads] and from the EMBL-EBI FTP: [https://ftp.ebi.ac.uk/pub/databases/opentargets/platform/]. Supplementary informationAdditional information on this tool can be found on the Platform documentation pages [https://platform-docs.opentargets.org/web-interface, https://platform-docs.opentargets.org/web-interface/associations-on-the-fly, https://platform-docs.opentargets.org/target-prioritisation] and training video [https://youtu.be/2A9bksboAag]. ContactAnnalisa Buniello, EMBL-EBI, buniello@ebi.ac.uk

bioinformatics↗

bioRxiv2024

Lit-OTAR Framework for Extracting Biological Evidences from Literature

The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionise drug discovery by extracting evidence from scientific literature for drug target identification and validation. This novel framework combines Named Entity Recognition (NER) for identifying gene/protein (target), disease, organism, and chemical/drug within scientific texts, and entity normalisation to map these entities to databases like Ensembl, Experimental Factor Ontology (EFO), and ChEMBL. Continuously operational, it has processed over 39 million abstracts and 4.5 million full-text articles and preprints to date, identifying more than 48.5 million unique associations that significantly help accelerate the drug discovery process and scientific research (> 29.9m distinct target-disease, 11.8m distinct target-drug and 8.3m distinct disease-drug relationships). The results are made accessible through the Open Targets Platform (https://platform.opentargets.org/) as well as Europe PMC website (SciLite web app) and annotations API (https://europepmc.org/annotationsapi).

bioinformatics↗

bioRxiv2020

Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published GWAS trait-associated loci

Genome-wide association studies (GWAS) have identified many variants robustly associated with complex traits but identifying the gene(s) mediating such associations is a major challenge. Here we present an open resource that provides systematic fine-mapping and protein-coding gene prioritization across 133,441 published human GWAS loci. We integrate diverse data sources, including genetics (from GWAS Catalog and UK Biobank) as well as transcriptomic, proteomic and epigenomic data across many tissues and cell types. We also provide systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues and identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine mapped genetics and functional genomics data using 445 gold standard curated GWAS loci to distinguish causal genes from background genes at the same loci, outperforming a naive distance based model. Genes prioritized by our model are enriched for known approved drug targets (OR = 8.1, 95% CI: [5.7, 11.5]). These results will be regularly updated and are publicly available through a web portal, Open Targets Genetics (OTG, http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

genetics↗

bioRxiv2020

Metabolic effects of aripiprazole and olanzapine multiple-dose treatment in healthy volunteers. Association with pharmacogenetics.

BackgroundAripiprazole and olanzapine are atypical antipsychotics. Both drugs can induce metabolic changes, however, the metabolic side effects produced by aripiprazole are more benign. ObjectivesTo evaluate if aripiprazole and olanzapine alter prolactin levels, lipid and glucose metabolism and hepatic, hematological, thyroid and renal function. MethodsTwenty-four healthy volunteers received 5 daily oral doses of 10 mg aripiprazole and 5 mg olanzapine in a crossover randomized clinical trial and were genotyped for 51 polymorphisms in 17 genes by qPCR. Drug plasma concentrations were measured by LC-MS. The biochemical and hematological analyses were performed by enzymatic methods. ResultsOlanzapine induced hyperprolactinemia but not aripiprazole. DRD3 Ser/Gly and ABCB1 rs10280101, rs12720067 and rs11983225 polymorphisms and CYP3A phenotype had an impact on plasma prolactin levels. C-peptide concentrations were higher after aripiprazole administration and were influenced by COMT rs4680 and rs13306278 polymorphisms. Olanzapine and the UGT1A1 rs887829 polymorphism were associated with elevated glucose levels. CYP3A poor metabolizers had increased insulin levels. Triglyceride concentrations were decreased due to olanzapine and aripiprazole treatment and were variable based on CYP3A phenotypes and the APOC3 rs4520 genotype. Cholesterol levels were also decreased and depended on HTR2A rs6314 polymorphism. All hepatic enzymes, platelet and albumin levels and prothrombin time were altered during both treatments. Additionally, olanzapine reduced the leucocyte count, aripiprazole increased free T4 and both decreased uric acid concentrations. ConclusionsShort term treatment with aripiprazole and olanzapine had a significant influence on the metabolic parameters. However, it seems that aripiprazole provokes less severe metabolic changes.

pharmacology and toxicology↗

bioRxiv2019

Towards a systematic map of the functional role of protein phosphorylation

Phosphorylation is a critical post-translational modification involved in the regulation of almost all cellular processes. However, less than 5% of thousands of recently discovered phosphorylation sites have a known function. Here, we devised a chemical genetic approach to study the functional relevance of phosphorylation in S. cerevisiae. We generated 474 phospho-deficient mutants that, along with the gene deletion library, were screened for fitness in 102 conditions. Of these, 42% exhibited growth phenotypes, suggesting these phosphosites are likely functional. We inferred their function based on the similarity of their growth profiles with that of gene deletions, and validated a subset by thermal proteome profiling and lipidomics. While some phosphomutants showed loss-of-function phenotypes, a higher fraction exhibited phenotypes not seen in the corresponding gene deletion suggestive of a gain-of-function effect. For phosphosites conserved in humans, the severity of the yeast phenotypes is indicative of their human functional relevance. This study provides a roadmap for functionally characterizing phosphorylation in a systematic manner.

systems biology↗

bioRxiv2019

The functional landscape of the human phosphoproteome

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. While tens of thousands of phosphorylation sites have been identified in human cells to date, the extent and functional importance of the phosphoproteome remains largely unknown. Here, we have analyzed 6,801 publicly available phospho-enriched mass spectrometry proteomics experiments, creating a state-of-the-art phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, 59 features indicative of proteomic, structural, regulatory or evolutionary relevance were integrated into a single functional score using machine learning. We demonstrate how this prioritization identifies regulatory phosphosites across different molecular mechanisms and pinpoint genetic susceptibilities at a genomic scale. Several novel regulatory phosphosites were experimentally validated including a role in neuronal differentiation for phosphosites present in the SWI/SNF SMARCC2 complex member. The scored reference phosphoproteome and its annotations identify the most relevant phosphorylations for a given process or disease addressing a major bottleneck in cell signaling studies.

genomics↗

bioRxiv2019

Copy number aberrations drive kinase re-wiring leading to genetic vulnerabilities in cancer

Somatic DNA copy number variations (CNVs) are prevalent in cancer and can drive cancer progression albeit with often uncharacterized roles in altering cell signaling states. Here, we integrated genomic and proteomic data for 5598 tumor samples to identify CNVs leading to aberrant signal transduction. The resulting associations recapitulated known kinase-substrate relationships and further network analysis prioritized likely driver genes. A total of 44 robust pan-cancer gene-phosphosite associations were replicated in cell line samples. Of these, ARHGEF17, a predicted regulator of hippo-signaling, was further studied through (phospho)proteomics analysis where ARHGEF17 knockdown cells showed dys-regulation of hippo- and p38 signaling as well as immune related pathways. Using, RNAi, CRISPR and drug screening data we find evidence of kinase addiction in cancer cell lines identifying inhibitors for targeting of kinase-dependent cell lines. We propose copy number status of genes as useful predictors of differential impact of kinase inhibition, a strategy that may be of use in the future for anticancer therapies.

genomics↗

bioRxiv2018

Multi-omics characterization of interaction-mediated control of human protein abundance levels

Proteogenomic studies of cancer samples have shown that copy number variation can be attenuated at the protein level, for a large fraction of the proteome, likely due to the degradation of unassembled protein complex subunits. Such interaction mediated control of protein abundance remains poorly characterized. To study this we compiled genomic, (phospho)proteomic and structural data for hundreds of cancer samples and find that up to 42% of 8,124 analyzed proteins show signs of post-transcriptional control. We find evidence of interaction dependent control of protein abundance, correlated with interface size, for 516 protein pairs, with some interactions further controlled by phosphorylation. Finally, these findings in cancer were reflected in variation in protein levels in normal tissues. Importantly, expression differences due to natural genetic variation were increasingly buffered from phenotype differences for highly attenuated proteins. Altogether, this study further highlights the importance of post-transcriptional control of protein abundance in cancer and healthy cells.

genomics↗

bioRxiv2018

Conserved phosphorylation hotspots in eukaryotic protein domain families

Protein phosphorylation is the best characterized post-translational modification that regulates almost all cellular processes through diverse mechanisms such as changing protein conformations, interactions, and localization. While the inventory for phosphorylation sites across different species has rapidly expanded, their functional role remains poorly investigated. Here, we have combined 537,321 phosphosites from 40 eukaryotic species to identify highly conserved phosphorylation \"hotspot\" regions within domain families. Mapping these regions onto structural data revealed that they are often found at interfaces, near catalytic residues and tend to harbor functionally important phosphosites. Notably, functional studies of a phospho-deficient mutant in the C-terminal hotspot region within the Ribosomal S11 domain in the yeast ribosomal protein uS11 showed cold-sensitive phenotype and impaired 20S pre-rRNA processing. Altogether, our study identified phosphorylation hotspots for 162 protein domains suggestive of an ancient role for the control of diverse eukaryotic domain families.

cell biology↗

bioRxiv2018

Capturing variation impact on molecular interactions: the IMEx Consortium mutations data set

The current wealth of genomic variation data identified at the nucleotide level has provided us with the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that amino acid variation of a molecules sequence has on its molecular interactions is a key step towards connecting a full mechanistic characterization of nonsynonymous variation to cellular phenotype. Here we present an open access resource created by IMEx database curators over 14 years, featuring 28,000 annotations fully describing the effect of individual point sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data content is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website at www.ebi.ac.uk/intact/resources/datasets#mutationDs and is being enhanced with every monthly release.

bioinformatics↗

bioRxiv2016

Benchmarking substrate-based kinase activity inference using phosphoproteomic data

MotivationPhosphoproteomic experiments are increasingly used to study the changes in signalling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies.\n\nResultsWe curated public phosphoproteomic experiments to identify a gold standard dataset containing a total of 184 kinase-condition pairs where regulation is expected to occur. A list of kinase substrates was compiled and used to estimate changes in kinase activities using the following methods: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and a multiple linear regression model (MLR). We also tested weighted variants of the Z-test, and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the number of known substrates and the type of evidence (in vivo, in vitro or in silico) supporting these influence the predictions.\n\nConclusionsMost models performed well with the Z-test and the GSEA performing best as determined by the area under the ROc curve (Mean AUC=0.722). Weighting kinase targets by the kinase target sequence preference improves the results only marginally. However, the number of known substrates and the evidence supporting the interactions has a strong effect on the predictions.

bioinformatics↗