Biology subjects

Foley, G.

Publications and source records attributed to Foley, G..

7 recordsLinked to original sources

bioRxiv2026

Systematic Regional Bias is Widespread in ChIP-seq

Robust and reproducible results are essential for confident scientific analysis. We demonstrate that transcription factor (TF) Chromatin Immunoprecipitation coupled with sequencing (ChIP-seq) suffers from systematic bias that may threaten its reproducibility: 80% of 200+ condition-matched, dual-replicate experiments in ENCODE contain genomic regions of systematic bias. We observe this regional bias even between replicates produced within the same experiment, resulting in thousands of unreplicated peaks, which often contain valuable biological data. We provide evidence that regional bias may lead to qualitative differences in TF biology inferred by different experiments; we discovered eight TFs with binding activity in compact chromatin that was identified by one experiment, yet systematically absent from others. To mitigate the effects of bias, we derive simple but effective metrics to quantify the quality of data within biased regions and demonstrate that they can be used for the robust integration of data from multiple experiments.

bioinformatics↗

bioRxiv2025

Multilingual model improves zero-shot predictionof disease effects on proteins

Predicting variant effects remains a central challenge in genomics. Protein language models (PLMs) capture amino-acid-level sequence constraints, whereas codon language models operate on coding sequences and may retain information that is lost upon translation. Here, we tested whether scores from the codon language model CaLM provide predictive information beyond protein-level representations for missense-variant interpretation. Across 71,436 ClinVar missense variants from 11,554 genes, adding CaLM to PLM baselines produced modest but reproducible improvements under gene-held-out cross-validation. PLM-only ensemble controls and explicit mutational-context analyses indicated that this improvement could not be explained solely by generic ensembling or simple codon-substitution features. Aggregating CaLM probabilities across synonymous codons attenuated codon-degeneracy-associated discordance while preserving most of the broader differences between CaLM and PLM scores. Gene-level analyses further showed that CaLM contribution varied continuously across genes and depended partly on the protein-model background. Across ClinMAVE functional assays, however, improvements were less consistent, indicating that codon-protein complementarity is context dependent rather than universal. Together, these results identify a modest but reproducible component of variant-effect information in CaLM-derived codon-level scores that is not fully captured by protein-level language-model representations.

bioinformatics↗

bioRxiv2024

Learning the Language of Phylogeny with MSA Transformer

Classical phylogenetics assumes site independence, potentially overlooking epistasis. Protein language models capture dependencies in conserved structural and functional domains across the protein universe. Here, we ask whether MSA Transformer, which takes a multiple sequence alignment (MSA) as input, captures evolutionary distance and to what extent its representations reflect epistasis in protein sequence evolution, neither of which are explicitly available during training. Systematic shuffling of natural and simulated MSAs demonstrates the model exploits column-wise conservation to distinguish phylogenetic relationships. Using internal embeddings, we reconstruct trees that are markedly consistent with trees generated by maximum likelihood inference. Applying this approach to both the RNA-dependent RNA polymerase of RNA viruses and the nucleo-cytoplasmic large DNA virus domain, we recover both established and novel evolutionary relationships. We conclude that MSA Transformer complements, rather than replaces, classical inference for more accurate histories of protein families.

bioinformatics↗

bioRxiv2024

Do Protein Language Models Learn Phylogeny?

Deep machine learning demonstrates a capacity to uncover evolutionary relationships directly from protein sequences, in effect internalising notions inherent to classical phylogenetic tree inference. We connect these two paradigms by assessing the capacity of protein-based language models (pLMs) to discern phylogenetic relationships without being explicitly trained to do so. We evaluate ESM2, ProtTrans and MSA-Transformer relative to classical phylogenetic methods, while also considering sequence insertions and deletions (indels) across 114 Pfam datasets. The largest ESM2 model tends to outperform other pLMs (including the multimodal ESM3) by recovering phylogenetic relationships among homologous protein sequences in both low- and high-gap settings. pLMs agree with conventional phylogenetic methods in general, but more so for protein families with fewer implied indels, highlighting indels as a key factor differentiating classical phylogenetics from pLMs. We find that pLMs preferentially capture broader as opposed to finer evolutionary relationships within a specific protein family, where ESM2 has a sweet spot for highly divergent sequences, at remote distance. Less than 10% of neurons are sufficient to broadly recapitulate classical phylogenetic distances; when used in isolation the difference between the paradigms is further diminished. We show these neurons are polysemantic, shared among different homologous families but never fully overlapping. We highlight the potential of ESM2 as a complementary tool for phylogenetic analysis, especially when extending to remote homologs that are difficult to align and imply complex histories of insertions and deletions.

molecular biology↗

bioRxiv2024

Optimal Phylogenetic Reconstruction of Insertion and Deletion Events

Insertions and deletions (indels) influence the genetic code in fundamentally distinct ways from substitutions, significantly impacting gene product structure and function. Despite their influence, the evolutionary history of indels is often neglected in phylogenetic tree inference and ancestral sequence reconstruction, hindering efforts to comprehend biological diversity determinants and engineer variants for medical and industrial applications. We frame determining the optimal history of indel events as a single Mixed-Integer Programming (MIP) problem, across all nodes in a phylogenetic tree adhering to topological constraints, and all sites implied by a given set of aligned, extant sequences. By disentangling the impact on ancestral sequences at each branch point, this approach identifies the minimal indel events that jointly explain the diversity in sequences mapped to the tips of that tree. MIP can recover alternate optimal indel histories, if available. We evaluated MIP for indel inference on a dataset comprising 15 real phylogenetic trees associated with protein families ranging from 165 to 2000 extant sequences, and on 60 synthetic trees at comparable scales of data and reflecting realistic rates of mutation. Across relevant metrics, MIP outperformed alternative parsimony-based approaches and reported the fewest indel events, on par or below their occurrence in synthetic datasets. MIP offers a rational justification for indel patterns in extant sequences; importantly, it uniquely identifies global optima on complex protein data sets without making unrealistic assumptions of independence or evolutionary underpinnings, promising a deeper understanding of molecular evolution and aiding novel protein design.

bioinformatics↗

bioRxiv2020

Traffic-derived particulate matter and angiotensin-converting enzyme 2 expression in human airway epithelial cells

Background The mechanism for the association between traffic-derived particulate matter less than 10 microns (PM10) and cases of COVID-19 disease reported in epidemiological studies is unknown. To infect cells, the spike protein of SARS-CoV-2 interacts with angiotensin-converting enzyme 2 (ACE2) on host airway cells. Increased ACE2 expression in lower airway cells in active smokers, suggests a potential mechanism whereby PM10 increases vulnerability to COVID-19 disease.Objective To assess the effect of traffic-derived PM10 on human airway epithelial cell ACE2 expression in vitro.Methods PM10 was collected from Marylebone Road (London) using a kerbside impactor. A549 and human primary nasal epithelial cells were cultured with PM10 for 2 h, and ACE2 expression (median fluorescent intensity; MFI) assessed by flow cytometry. We included cigarette smoke extract as a putative positive control. Data were analysed by either Mann-Whitney test, or Kruskal-Wallis with Dunn’s multiple comparisons test.Results PM10 at 10 μg/mL, and 20 μg/mL increased ACE2 expression in A549 cells (P<0.05, 0.01 vs. medium control, respectively). Experiments using a single PM10 concentration (10 μg/mL), found increased ACE2 expression in both A549 cells (control vs. PM10, median (IQR) MFI; 470 (0.1 to 1114) vs 6217 (5071 to 8506), P<0.01), and in human primary epithelial cells (0 (0 to 591) vs. 4000 (2610 to 7853), P<0.05). Culture of A549 cells with 5% cigarette smoke extract increased ACE2 expression (n=4, 0 (0 to 28) vs. 9088 (7557 to 15831, P<0.05).Conclusion Traffic-related PM10 increases the expression of the receptor for SARS-CoV-2 in human respiratory epithelial cells.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2019

Identifying and engineering ancient variants of enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion ("indel") events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time. Author summaryMassive sequencing projects expose the extent of natural, genetic diversity. Here, we describe a method with capacity to perform ancestor sequence reconstruction from data sets in excess of 10,000 sequences, poised to recover ancestral diversity, including the evolutionary events that determine present-time biological function and structure. We introduce a novel strategy for suggesting "indel variants" that are distinct from, but can be explored alongside, substitution variants for creating ancestral libraries. We demonstrate how indels can be used as building blocks to form "hybrid ancestors"; based on this strategy, we synthesise ancestor variants, with varying enzymatic activities, for wide-ranging applications in the biotechnology sector.

bioinformatics↗