Biology subjects

Shields, D. C.

Publications and source records attributed to Shields, D. C..

4 recordsLinked to original sources

bioRxiv2026

Detecting genomic regions enriched for reciprocal recombination in autism spectrum disorder

Meiotic recombination is an important means of increasing genetic diversity by generating novel haplotypes in a population. Recombination separates linked loci extremely slowly in some regions, therefore genetic variants in high linkage disequilibrium may become co-adapted. Reciprocal recombination that separates co-adapted variants may generate a deleterious de novo haplotype that contributes to disease. We developed statistical methods to detect genomic regions of recombination excess in two different family-based study designs. We identified recombination in the Simons Simplex Collection in 273 simplex families with one child with autism spectrum disorder (ASD) and at least two unaffected children, in which recombinations can be mapped to the proband and contrasted with the recombination counts in unaffected siblings; and in 1,802 families with two children, where the number of recombinations identified can be contrasted with the expectation from a reference recombination map. Both strategies revealed a tail of low p-values for loci of interest that contrasted with the rest of the distribution. Permutation and bootstrap tests did not identify genome-wide primary findings in either cohort, but the most significant three-child cohort locus of recombination excess (between cadherin genes CDH4 and CDH26) replicated in the two-child cohort (p=0.01). While this replication strategy was not defined a priori, five of the most recombination enriched bins identified candidate ASD genes (p=0.02; WWOX, ADAMTS16, INSR, ADARB2, and HS6ST1). Since the six identified loci were not identified as regions of high de novo copy number variation in the study cohort and no CNVs were detected in any of the recombinant probands in the identified regions, they represent candidates for reciprocal recombinations generating unfavourable haplotypes for these genes. This study highlights a previously unidentified source of clinical genetic variability contributing to the molecular aetiology of ASD. AUTHOR SUMMARYAutism spectrum disorder (ASD) is a constellation of neurodevelopmental disabilities characterised by deficits in social communication and repetitive patterns of behaviour. While ASD is highly heritable, its genetic basis is complex and poorly understood. While some highly penetrant types of genetic variation have been identified, most people with ASD carry a large number of variants that each contribute a small amount to their overall phenotype. In addition to mutations in individual genes, changes in the configuration of genes along a chromosome may contribute to ASD. Here, we describe a method for identifying regions where such new configurations have occurred through recombination and attempt to find regions where such changes are more common in autistic children than in their non-autistic siblings. We explore recombination as a source of genetic variation contributing to autism, which has potential to inform clinicians in providing services to autistic people and their families.

genetics↗

bioRxiv2024

Effect of dataset partitioning strategies for evaluating out-of-distribution generalisation for predictive models in biochemistry

AO_SCPLOWBSTRACTC_SCPLOWQuantifying model generalization to out-of-distribution data has been a longstanding challenge in machine learning. Addressing this issue is crucial for leveraging machine learning in scientific discovery, where models must generalize to new molecules or materials. Current methods typically split data into train and test sets using various criteria -- temporal, sequence identity, scaffold, or random cross-validation -- before evaluating model performance. However, with so many splitting criteria available, existing approaches offer limited guidance on selecting the most appropriate one, and they do not provide mechanisms for incorporating prior knowledge about the target deployment distribution(s). To tackle this problem, we have developed a novel metric, AU-GOOD, which quantifies expected model performance under conditions of increasing dissimilarity between train and test sets, while also accounting for prior knowledge about the target deployment distribution(s), when available. This metric is broadly applicable to biochemical entities, including proteins, small molecules, nucleic acids, or cells; as long as a relevant similarity function is defined for them. Recognizing the wide range of similarity functions used in biochemistry, we propose criteria to guide the selection of the most appropriate metric for partitioning. We also introduce a new partitioning algorithm that generates more challenging test sets, and we propose statistical methods for comparing models based on AU-GOOD. Finally, we demonstrate the insights that can be gained from this framework by applying it to two different use cases: developing predictors for pharmaceutical properties of small molecules, and using protein language models as embeddings to build biophysical property predictors.

bioinformatics↗

bioRxiv2023

AutoPeptideML: Automated Machine Learning for Building Trustworthy Peptide Bioactivity Predictors

MotivationAutomated machine learning (AutoML) solutions can bridge the gap between new computational advances and their real-world applications by enabling experimental scientists to build their own custom models. We examine different steps in the development life-cycle of peptide bioactivity binary predictors and identify key steps where automation can not only result in a more accessible method, but also more robust and interpretable evaluation leading to more trustworthy models. ResultsWe present a new automated method for drawing negative peptides that achieves better balance between specificity and generalisation than current alternatives. We study the effect of homology-based partitioning for generating the training and testing data subsets and demonstrate that model performance is overestimated when no such homology correction is used, which indicates that prior studies may have overestimated their performance when applied to new peptide sequences. We also conduct a systematic analysis of different protein language models as peptide representation methods and find that they can serve as better descriptors than a naive alternative, but that there is no significant difference across models with different sizes or algorithms. Finally, we demonstrate that an ensemble of optimised traditional machine learning algorithms can compete with more complex neural network models, while being more computationally efficient. We integrate these findings into AutoPeptideML, an easy-to-use AutoML tool to allow researchers without a computational background to build new predictive models for peptide bioactivity in a matter of minutes. AvailabilitySource code, documentation, and data are available at https://github.com/IBM/AutoPeptideML and a dedicated webserver at http://peptide.ucd.ie/AutoPeptideML.

bioinformatics↗

bioRxiv2019

Subcellular localization of the immunoregulatory protein MACIR by TNPO1 is mediated by a C terminal PY motif

Expression of the macrophage immunometabolism regulator gene (MACIR) is associated with severity of autoimmune disease pathology and the regulation of macrophage biology through unknown mechanisms. The 206 amino acid protein lacks homology to any characterized protein sequence and is a disordered protein according to structure prediction algorithms. Here we identify specific interactions of MACIR using a fragment complementation-based affinity pull down of cellular proteins prepared with a membrane solubilization buffer. Quantitative mass spectrometry showed enrichment of nuclear and mitochondrial proteins and of 63 significant interacting proteins, binding to the nuclear transport receptor TNPO1 and trafficking proteins UNC119 homolog A and B were validated by immunoprecipitation. Analysis of mutations in two candidate recognition motifs in the MACIR amino acid sequence confirmed TNPO1 binds via a PY-NLS motif (aa98-117). Characterizing nuclear MACIR activity in macrophage and fibroblasts is a priority with respect to developing strategies for treatment of autoimmune disease.

biochemistry↗