Biology subjects

Mooney, S. D.

Publications and source records attributed to Mooney, S. D..

10 recordsLinked to original sources

bioRxiv2026

Advances in protein function prediction from the fifth CAFA challenge

The Critical Assessment of Functional Annotation (CAFA) is a long-standing community effort to independently assess computational methods for protein function prediction, to highlight well-performing methodologies, to identify bottlenecks in the field, and to provide a forum for the dissemination of results and exchange of ideas. In its fifth round (CAFA5) of triennial challenges, a partnership with Kaggle Inc. facilitated participation from a large community of data scientists and computational biologists through a competitive prospective challenge on the crowdsourcing platform. In this work, we present an in-depth analysis of the submitted predictions and report improvements in accuracy over all methods from the previous CAFA challenges. We further introduce a new evaluation setting for proteins with pre-existing (incomplete) annotations and identify the need for methods that better leverage existing annotations to predict those that will be discovered later. Finally, we characterize the prospective evaluation framework by examining performance on a strict set of unpublished annotations and across intermediate database releases. Our results indicate that recent developments in the field, such as the availability of protein language models and accurately predicted 3D structures, as well as the growth of experimental annotations through biocuration, have all contributed to performance improvements. 1

bioinformatics↗

bioRxiv2026

Gene- and domain-aware calibration increases the clinical utility of variant effect predictors

The utility of clinical genetic testing is limited because around 90% of missense variants in ClinVar remain of uncertain clinical significance. Variant effect predictors (VEPs) can score any missense variant, potentially empowering variant classification. Realizing this potential requires calibration to translate predictor scores into evidence. However, genome-wide calibration ignores predictor heterogeneity across genes, causing evidence misassignment. We developed an automated, data-adaptive framework that optimizes two complementary approaches: gene-specific calibration for genes with enough variants for calibration, and domain-aggregate calibration for other disease-associated genes, which groups variants from protein domains with similar predictor score distributions for calibration. Applied to three predictors across 2,769 genes, this framework assigned evidence to 10.6% more variants on average while generally improving evidence accuracy compared to genome-wide calibration. These calibrations and the resulting calibrated computational evidence are available through the PredictMD portal. Our framework substantially increases the clinical utility of VEPs for variant classification.

genetics↗

bioRxiv2023

Transcriptomic Characterization Reveals Disrupted Medium Spiny Neuron Trajectories in Huntington's Disease and Possible Therapeutic Avenues

Huntingtons disease (HD) is a neurodegenerative disorder caused by an expansion of CAG repeats in exon 1 of the HTT gene, ultimately resulting in the generation of a mutant HTT (mHTT) protein. Although mHTT is expressed in various tissues, it significantly affects medium spiny neurons (MSNs) in the striatum, resulting in their loss and the subsequent motor function impairment in HD. While HD symptoms typically emerge in midlife, disrupted MSN neurodevelopment has an important role. To explore the effects of mHTT on MSN development, we differentiated HD induced pluripotent stem cells (iPSC) and isogenic controls into neuronal stem cells, and then generated a developing MSN population encompassing early, intermediate progenitors, and mature MSNs. Single-cell RNA sequencing revealed that the developmental trajectory of MSNs in our model closely emulated the trajectory of fetal striatal neurons. However, in the HD MSN cultures, the differentiation process downregulated several crucial genes required for proper MSN maturation, including Achaete-scute homolog 1 and members of the DLX family of transcription factors. Our analysis also uncovered a progressive dysregulation of multiple HD-related pathways as the MSNs matured, including the NRF2-mediated oxidative stress response and mitogen-activated protein kinase signaling. Using the transcriptional profile of developing HD MSNs, we searched the L1000 dataset for small molecules that induce the opposite gene expression pattern. Our analysis pinpointed numerous small molecules with known benefits in HD models, as well as previously untested novel molecules. A top novel candidate, Cerulenin, partially restored the DARPP-32 levels and electrical activity in HD MSNs, and also modulated genes involved in multiple HD-related pathways.

neuroscience↗

bioRxiv2022

Evidence-based calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for clinical use of PP3/BP4 criteria

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as Supporting level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (Supporting, Moderate, Strong, Very Strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tools scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved Supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying Moderate and several reached Strong evidence levels. One tool reached Very Strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

bioinformatics↗

bioRxiv2020

Transcriptional and epigenetic characterization of early striosomes identifies Foxf2 and Olig2 as factors required for development of striatal compartmentation and neuronal phenotypic differentiation

The basal ganglia, best known for processing information required for multiple aspects of movement, is also part of a network which regulates reward and cognition. The major output nucleus of the basal ganglia is the striatum, and its functions are dependent on neuronal compartmentation, including striosomes and matrix, which are selectively affected in disease. Striatal projection neurons are GABAergic medium spiny neurons (MSNs), all of which share basic molecular signatures but are subtyped by selective expression of receptors, neuropeptides, and other gene families. Neurogenesis of the striosome and matrix occurs in separate waves, but the factors regulating terminal neuronal differentiation following migration are largely unidentified. We performed RNA- and ATAC-seq on sorted murine striosome and matrix cells at postnatal day 3. Focusing on the striosomal compartment, we validated the localization and role of transcription factors and their regulator(s), previously not known to be associated with striatal development, including Irx1, Foxf2, Olig2 and Stat1/2. In addition, we validated the enhancer function of a striosome-specific open chromatin region located 15Kb downstream of the Olig2 gene. These data and data bases provide novel tools to dissect and manipulate the networks regulating MSN compartmentation and differentiation and thus provide new approaches to establishing MSN subtypes from human iPSCs for disease modeling and drug discovery.

neuroscience↗

bioRxiv2019

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

bioinformatics↗

bioRxiv2019

Leaf -- An open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research

ObjectiveAcademic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of data that originate from multiple sources. Enterprise Data Warehouses (EDWs) have emerged as central resources for these data, but they often require an informatician to extract meaningful information, thereby limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying and extracting clinical data from heterogeneous data sources.\n\nMaterials and MethodsLeaf utilizes a flexible biomedical concept system to define hierarchical items and ontologies. Each Leaf concept contains both textual representations and associated SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries.\n\nResultsLeaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an EDW with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org) and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf.\n\nDiscussionLeaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication from existing databases.\n\nConclusionLeaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly integrate with existing enterprise user authentication systems and clinical databases in situ. We demonstrate its unique technical strengths and success alongside its friendly user interface. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks and clinical studies involving large patient cohorts.

bioinformatics↗

bioRxiv2018

New Drosophila long-term memory genes revealed by assessing computational function prediction methods.

A major bottleneck to our understanding of the genetic and molecular foundation of life lies in the ability to assign function to a gene and, subsequently, a protein. Traditional molecular and genetic experiments can provide the most reliable forms of identification, but are generally low-throughput, making such discovery and assignment a daunting task. The bottleneck has led to an increasing role for computational approaches. The Critical Assessment of Functional Annotation (CAFA) effort seeks to measure the performance of computational methods. In CAFA3 we performed selected screens, including an effort focused on long-term memory. We used homology and previous CAFA predictions to identify 29 key Drosophila genes, which we tested via a long-term memory screen. We identify 11 novel genes that are involved in long-term memory formation and show a high level of connectivity with previously identified learning and memory genes. Our study provides first higher-order behavioral assay and organism screen used for CAFA assessments and revealed previously uncharacterized roles of multiple genes as possible regulators of neuronal plasticity at the boundary of information acquisition and memory formation.

neuroscience↗

bioRxiv2017

MutPred2: inferring the molecular and phenotypic impact of amino acid variants

We introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. While its prioritization performance is state-of-the-art, a novel and distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited diseases have the potential to significantly accelerate the discovery of clinically actionable variants.\n\nAvailability: http://mutpred.mutdb.org/

bioinformatics↗

bioRxiv2017

EXPLANe: An Extensible Framework for Poster Annotation with Mobile Devices

SummaryScientific posters tend to be brief, unstructured, and generally unsuitable for communication beyond a poster session. This paper describes EXPLANe, a framework for annotating posters using optical text recognition and web services on mobile devices. EXPLANe is demonstrated through an interface to the MyVariant.info variant annotation web services, and provides users a list of biological information linked with genetic variants (as found via extracted RSIDs from annotated posters). This paper delineates the architecture of the application, and includes results of a five-part evaluation we conducted. Researchers and developers can use the existing codebase as a foundation from which to generate their own annotation tabs when analyzing and annotating posters.\n\nAvailabilityAlpha EXPLANe software is available as an open source application at https://github.com/ngopal/EXPLANe\n\nContactSean D. Mooney (sdmooney@uw.edu)

scientific communication and education↗