Biology subjects

Pavlidis, P.

Publications and source records attributed to Pavlidis, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Gene function prediction from bulk coexpression is bounded by cell-type-level signal

It is widely accepted in genomics that coexpression of RNA transcripts suggests a commonality of function. This intuition is explicitly leveraged in machine learning methods that predict gene function, where it is often combined with other features such as protein interactions and sequence similarity. For example, including coexpression data from human tissue expression boosts performance for predicting Gene Ontology annotations. However, the biological underpinnings of this observation have not been well-investigated. Building on earlier results from our group, in this work we show that gene function is predictable from coexpression substantially because it reflects differences in expression between cell types, and these differences are also intrinsic to the ground truth labels. Using simulations and analyses of real data, we show that variance in the cellular composition of bulk samples impacts function learnability and attribute this to cell type marker gene content in the GO terms. We further show that cell type profiles, where the relationship between gene expression and cell type is made transparent, are effective for predicting gene function while increasing interpretability. These results indicate that function prediction models trained on bulk coexpression are largely limited to cell-type-level resolution rather than fine-grained biochemical function, with direct consequences for how such predictions should be interpreted.

bioinformatics↗

bioRxiv2026

Single-Cell Analytics for Dose Response (SCADR) discriminates PTEN missense variants by lipid and protein phosphatase dysfunction

The proliferation of sequencing efforts has revealed a vast and expanding catalog of single nucleotide gene variants, many associated to, but with unclear roles in disease. Fully charactering variant impacts and linking specific protein dysfunctions to disease are challenging due to the multi-functional nature of many proteins and varying degree of variant effects on these functions. Lagging are sensitive approaches to empirically assess the impact of missense variant-induced single amino acid changes on a wide range of protein functions. To address these issues, we have developed an open-source computational analysis tool called SCADR (Single-Cell Analytics for Dose Response) for simultaneously measuring and comparing impacts of exogenously-expressed variants on multiple signaling pathways using multiplex phospho-antibody spectral flow cytometry in human cell lines. SCADR retains and correlates single-cell measures of signal protein activity states along with expression levels of exogenously-expressed variants, providing rich characterization of multiple protein functions, signaling protein interactions, and enhanced discrimination of variant impacts on different signaling pathways, highlighting each variants unique dysfunction profile. Here, we apply SCADR for analyses of the impact of 6 variants of the tumor-suppressor protein PTEN (P38H, C124S, G129E, Y138L, D268E, 4A) expressed in HEK293 cells on the phosphorylation states of the canonical and noncanonical downstream signaling proteins Akt, S6, CREB, ERK, and p38 detected with fluorophore-conjugated phospho-antibodies, along with an antibody detecting an N-terminal HA tag on PTEN variants allowing measures of dose-response effects of each variants expression on signaling cascades. Results identify variant-specific impacts on downstream signaling cascades.

genomics↗

bioRxiv2026

Detailed curation of biological samples and experimental designs for genomics using LLM-supported agentic workflows

We describe an automated software tool to accomplish data curation tasks previously performed by humans for the Gemma genomics data re-analysis resource. Gemma is a hand-curated database of reprocessed transcriptomic studies, currently covering over 23,000 human, mouse and rat data sets largely drawn from the Gene Expression Omnibus (GEO). We developed a pipeline that uses both traditional (mechanical) and large-language models to produce detailed ontology-anchored, sample- and experiment-level annotations in accordance with our established curation guidelines. In this report, we describe benchmarking the pipeline and investigations aimed at evaluating readiness of the v1.1 Gemma curation agent for production use. Overall, performance is near that of human curators, at approximately 1/20th the cost and at least 100 times the speed. We also present preliminary exploration of triage methods for identifying agent curations that are more likely to contain errors, and thus can be forwarded for human review. We discuss the potential place of such curation approaches in bioinformatics ecosystems. Besides the software, our deliverables include the benchmark set of 500 studies and an evaluation framework that can be used to further develop the pipeline or compare to other approaches.

bioinformatics↗

bioRxiv2026

Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue

Coexpression of transcripts presents the most common means of computational inference of transcription factor regulation, and is often combined with other data types to infer regulatory networks. With the growing popularity of single-cell approaches, there are questions about how best to extract coexpression information from the data. Recently we reported a simulation study that explored the differences among coexpression performed at different levels: across single cells (xCell, per cell type), across subjects from pseudobulked single-cell data (xSubject, per cell type), or across subjects using bulk tissue samples (xBulk). Here we test predictions made by those models using real data. We consider both preservation (consistency of coexpression findings across different levels of analysis of the same data) and replicability across independent studies, as well as biological interpretability. We find that preservation across levels is limited, indicating the choice of analysis level will affect outcomes. We show that xCell coexpression is more replicable across studies compared to xSubject. xBulk coexpression is dominated by patterns driven by variability in cellular composition and fails to capture much coexpression that is reliably detected at finer resolutions. While all modes of analysis exhibit some enrichment for known regulatory relationships, it was highest with the xCell mode. Finally, we present a case study of the effect of analysis modes on a schizophrenia-associated pattern, reinforcing the importance of analytic choices in the interpretation and replicability of coexpression analyses. Together with our modeling study, this work emphasizes the importance of understanding sources of expression covariation as they relate to the goals of the analysis, and recommend single-cell-based data with biological replicates should be the focus of attempts to infer dynamic regulatory interactions that are more likely to be replicable by others.

bioinformatics↗

bioRxiv2026

Spatially-resolved integration of microglia morphological diversity and gene expression using Visium with protein co-detection

Microglia exhibit a dynamic range of morphologies that actively shift in response to local environmental cues. There has been a concerted effort as a field to move away from dualistic characterization of all microglia as resting or activated, which is often described in terms of their morphological differences alone, towards more integrated definitions of microglial states based on multiple data types. Recent advances in spatially-resolved transcriptomics offer new ways to understand microglial gene expression in situ. Here, we related microglia morphology to gene expression within a published, spatially-resolved proteogenomics (Visium-SPG) dataset of the human dorsolateral prefrontal cortex as proof of principle to combine the contributions of microglia morphology to regional transcriptomics differences in the brain. The published dataset uniquely combined spatial transcriptomics with immunofluorescent staining for four major brain cell types, including microglia, which enabled us to link microglial morphologies to gene expression in a spatially-resolved manner. Using a computational toolset, MicrogliaMorphology and MicrogliaMorphologyR, we classified individual microglia into morphological subtypes, assigned microglia to spots, and integrated these data with transcriptomic profiles for each Visium-SPG spot. We applied non-negative matrix factorization to account for contributions from multiple cell types, morphological features, and regional context in downstream differential expression modeling. Our approach offers a methodological framework for investigating the relationship between microglial morphology and gene expression in larger cohorts and within disease contexts where changes in microglia morphology are anticipated. Author summaryRecent advances in spatially-resolved transcriptomics and the development of methods to deconvolve cell type signals has offered new ways to understand microglial gene expression in various tissue contexts. In this work, we related microglia morphologies to gene expression within a published spatial transcriptomics dataset of the human dorsolateral prefrontal cortex to assess whether morphologically different microglia are transcriptomically different in the healthy human brain. This dataset uniquely combines spatial transcriptomics with immunofluorescent staining for four major brain cell types, including microglia, enabling the linkage of microglial morphologies to gene expression in a spatially-resolved manner. Using a computational toolset for microglia morphology analysis, we classified individual microglia into morphological subtypes and integrated these data with spatial transcriptomic profiles while accounting for contributions from multiple cell types, morphological features, and regional context. This allowed for the identification of microglia morphology-related genes that are differentially expressed between microglial subtypes and relevant to human brain disorders. Findings together provide proof of principle for exploring the relationship between microglial morphology and gene expression in larger cohorts and diverse experimental contexts.

neuroscience↗

bioRxiv2026

Application of large language models to the annotation of cell lines and mouse strains in genomics data

Accurate, consistent and comprehensive metadata are essential for the reuse of functional genomics data deposited in repositories such as the Gene Expression Omnibus (GEO), however, achieving this often requires careful manual curation that is time-consuming, costly and prone to errors. In this paper, we evaluate the performance of Large Language Models (LLMs), specifically OpenAIs GPT-4o, as an assistive tool for entity-to-ontology annotation of two commonly encountered descriptors in transcriptomic experiments - mouse strains and cell lines. Using over 9,000 manually curated experiments from the Gemma database and over 5,000 associated journal articles, we assess the models ability to identify relevant free-text entries and map them to appropriate ontology terms. Using zero-shot prompting and retrieval-augmented generation (RAG) to incorporate domain-specific ontology knowledge, GPT-4o correctly annotated 77% of mouse strain and 59% of cell line experiments, and uncovered manual curation errors in Gemma for over 200 experiments. GPT-4o substantially outperformed a regular expression-based string-matching method, which correctly annotated only 6% of mouse strain experiments due to low precision. Model errors often arose from typographical mistakes or inconsistent naming in the GEO record or publication, and resembled those made by human curators. Along with annotations, our approach requests that the model output supporting context and quotes from the sources. These were typically accurate and enabled rapid curator verification. These findings suggest that LLMs are not ready to fully replace manual curators, but can already effectively support them. A human-in-the-loop workflow, in which LLMs annotations are provided to human curators for validation, may improve the efficiency and quality of large-scale biomedical metadata curation.

bioinformatics↗

bioRxiv2026

A Cell-Based Assay to Categorize Variants of Human Succinate Semialdehyde Dehydrogenase Associated with Autism Spectrum Disorder

Many genes are implicated in autism spectrum disorder (ASD) as a result of sequencing the genomes of individuals with ASD, however in most cases it remains unclear which genes are playing causative roles. ALDH5A1, which encodes the enzyme succinate semialdehyde dehydrogenase (SSADH), an important regulator of GABA metabolism, is causative in the syndrome SSADH deficiency and is also implicated in ASD. However, it is unknown how variants found in ASD affect SSADH function. We developed a yeast growth assay that models SSADH deficiency to quantify the functional impact of seven ALDH5A1 variants found in ASD. In this assay, expression of human ALDH5A1 partially complemented the growth defect caused by deletion of the ALDH5A1 ortholog UGA2. Using growth rate measurements, we calculated functional scores for 27 variants divided into calibration and ASD test variant groups. Functional scores for benign and pathogenic calibration variants segregated accordingly, validating the assay, while ASD variants displayed a range of activities from complete loss to normal function. Comparisons with published enzymatic assays and computational predictions showed broad agreement, while also identifying some limitations of these approaches. SUMMARY STATEMENTWe introduce a simple yeast assay to measure how autism-linked gene variants in ALDH5A1, which encodes the enzyme succinate semialdehyde dehydrogenase, affect its function, helping clarifying its role in autism.

neuroscience↗

bioRxiv2025

Persistent hindrances to data re-use in single-cell genomics

We report on our experience attempting to re-use published and publicly available single-cell (or single-nucleus) RNA-sequencing studies (scRNA-seq) from the Gene Expression Omnibus (GEO). We screened GEO for human, mouse and rat scRNA-seq studies as potential candidates for inclusion in the Gemma database of re-annotated and re-analyzed transcriptome studies. Using semi-automated and manual curation, we assessed whether GEO datasets included cell-level expression count matrices and cell-type annotations. We found that there are steep challenges to data reuse. Only [~]40% of studies provided readily usable processed count data that could be reliably mapped to GEO metadata, and fewer than 10% included author-provided cell-type annotations. While raw sequencing data were available for the majority of studies, only a small proportion could be re-analyzed automatically without reliance on heuristics. Our findings show that existing practices for single-cell RNA-sequencing data distribution and sharing are insufficient for effective reuse, and highlight the urgent need for repositories to strengthen and enforce submission requirements, particularly for processed data and cell-type annotations.

genomics↗

bioRxiv2025

Loss of brain insulin production impairs learning and memory in female mice

Diabetes is characterized by dysfunctional insulin release and action, and it is a risk factor for Alzheimers disease, the most common form of dementia. Alterations in brain insulin signalling and metabolism have been linked with Alzheimers disease. The ancestral insulin gene, Ins2, is transcribed locally within the brain. Here we demonstrate that hippocampal Ins2 mRNA is higher in females than males, and modulated by diet. To specifically determine how locally-produced insulin influences hippocampal function, we used mice with germline Ins2 knockout (Ins2-/-) and the normal complement of wildtype Ins1 alleles. Compensation from the Ins1 gene ensured normal glucose tolerance, normal insulin sensitivity, normal fasting insulin, and normal body weight under these conditions. Using the Morris water maze, we found that visuo-spatial learning and memory performance of female Ins2-/- mice was significantly impaired relative to wild-type mice, whereas the performance of male Ins2-/- and wild-type mice did not differ. We profiled isolated hippocampi from female Ins2-/- and littermate control mice using RNA sequencing to provide an unbiased analysis of gene expression differences that underlie these behavioural changes. Cyclin D1 (Ccnd1) was significantly reduced in Ins2-/- mice, prompting us to examine adult neurogenesis using exogenous mitotic marker EdU and the immature neuronal marker doublecortin (DCX). Our data demonstrate female-specific roles for brain-derived Ins2 on learning and memory function in mice. Research in contextO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIAlzheimers disease and type 2 diabetes share common risk factors and metabolic dysfunction. C_LIO_LIInsulin and insulin signalling are known to have positive effects on neuronal health and cognition. C_LIO_LILocal insulin production has been reported in the brain, including the hippocampus, which plays critical roles in learning and memory, as well as Alzheimers pathophysiology. C_LI What is the key question?O_LIWhat are the consequences of deleting the Ins2 gene, which selectively prevents local brain insulin production, on learning and memory in mice? C_LI What are the new findings?O_LIHippocampal insulin production via the Ins2 gene is sexually dimorphic and modulated by diet. C_LIO_LIKnocking out brain insulin production in Ins2-/- mice does not have significant effects on glucose homeostasis in these diet and housing conditions. C_LIO_LIKnocking out brain insulin production in Ins2-/- mice results in learning and memory impairments in female but not male mice. C_LI How might this impact on clinical practice in the foreseeable future?O_LIThese pre-clinical observations demonstrate a causal role for insulin in learning and memory, but will require additional human studies before they can impact clinical practice. C_LI

physiology↗

bioRxiv2025

Genetic affinities between an ancient Greek colony and its metropolis: the case of Amvrakia in western Greece

BackgroundDuring the Ancient Greek colonization, Corinth established a stable network of economic and political ties, by founding colonies connecting southern Greece with the mainland of Epirus and reaching as far as the east Adriatic coast. Amvrakia, one of the main Corinthian colonies founded during the 7th century BCE, was characterized by its strong dependence on its metropolis. Here, we aim to investigate the genetic relationships between the Corinthian metropolis and the Amvrakia colony, the contribution of the local population to the founding genetic pool, as well as the demography of Amvrakia in subsequent periods. ResultsDuring its foundation in the Archaic period, Amvrakia appears to have been shaped by genetic influences from at least two different sources. The first source migrated from the Corinth territory, represented by the Archaic Tenea population and is supported via an Identity By Descent (IBD) analysis. The second source shows a direct ancestry from Late Bronze Age (LBA) / Iron Age Greece, including a local LBA population represented by the Ammotopos site located in close proximity to Amvrakia, as shown by a plethora of independent population genomics analyses. During the subsequent Classical and Hellenistic periods, the population of Amvrakia appears to have slightly differentiated, yet evidence of genetic continuity over time is observed. ConclusionsThe migration of Corinthians to Amvrakia contributed to the initial genetic pool of the colony along with the local genetic pool, indicating that the Corinthian colonization included both genetic and cultural transmission between the metropolis and its colony.

evolutionary biology↗

bioRxiv2025

Using semantic search to find publicly available gene-expression datasets

Millions of high-throughput, molecular datasets have been shared in public repositories. have been shared in public repositories. Researchers can reuse such data to validate their own findings and explore novel questions. A frequent goal is to find multiple datasets that address similar research topics and to either combine them directly or integrate inferences from them. However, a major challenge is finding relevant datasets due to the vast number of candidates, inconsistencies in their descriptions, and a lack of semantic annotations. This challenge is first among the FAIR principles for scientific data. Here we focus on dataset discovery within Gene Expression Omnibus (GEO), a repository containing 100,000s of data series. GEO supports queries based on keywords, ontology terms, and other annotations. However, reviewing these results is time-consuming and tedious, and it often misses relevant datasets. We hypothesized that language models could address this problem by summarizing dataset descriptions as numeric representations (embeddings). Assuming a researcher has previously found some relevant datasets, we evaluated the potential to find additional relevant datasets. For six human medical conditions, we used 30 models to generate embeddings for datasets that human curators had previously associated with the conditions and identified other datasets with the most similar descriptions. This approach was often, but not always, more effective than GEOs search engine. Our top-performing models were trained on general corpora, used contrastive-learning strategies, and used relatively large embeddings. Our findings suggest that language models have the potential to improve dataset discovery, perhaps in combination with existing search tools.

genomics↗

bioRxiv2024

Limits to the inference of gene regulation from bulk tissue expression data

MotivationThousands of studies have used co-expression analysis of bulk tissue samples to probe gene regulation. However, the extent that intracellular regulatory signals are present in these data is unclear. Specifically, we lack clarity of the factors that promote or impede the propagation of intracellular regulatory signals from the single cell level to the bulk tissue level. To bring these issues into focus, we developed a novel computational simulator, grounded in real data, to explore the theoretical relationship between events in single cells and bulk tissue expression profiles, and clarify the conditions required for the propagation of intracellular regulatory signals in complex tissues such as the brain. ResultsOur simulator first generates single cell expression profiles and subsequently samples and aggregates these single cells to produce bulk tissue expression profiles. Using this framework, we found that there are very specific and unlikely conditions under which intracellular dynamic regulatory signals can be propagated to the bulk tissue level. For the most part, such regulatory relationships, however strong at the single cell level, are unlikely to be detectable. Our results provide a quantitative explanation for why regulatory network inference from co-expression has proved challenging - even with the assistance of other data modalities - and gives the scientific community a set of tools to further explore these issues in both single-cell and bulk tissue data. Availability and implementationAll relevant data are within the manuscript and supplementary files. The code for all data analyses and generation of figures are available on GitHub (https://github.com/PavlidisLab/coex-simulation). A copy of the data has been deposited in Borealis, the Canadian Dataverse Repository (https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/2CWXY6).

bioinformatics↗

bioRxiv2024

A multi-model approach defines function altering MECP2 missense variants identified in individuals with autism spectrum disorder

Pathogenic variants in MECP2 commonly lead to Rett syndrome, where MECP2s function as a DNA cytosine methylation reader is believed critical. MECP2 variants are also catalogued in individuals with autism spectrum disorder (ASD), including nine missense variants which had no known clinical significance at the start of this study. To assess these nine variants as risk alleles for ASD, we developed MECP2 variant functional assays using budding yeast and Drosophila. We calibrated these assays with known pathogenic and benign variants. Our data predict that four ASD variants are loss of function and five are functional. Protein destabilization offers insight into the altered function of some of these variants. Notably, yeast and Drosophila lack DNA methylation, yet all Rett pathogenic and ASD variants located in the methyl DNA binding domain that we analyzed proved to be loss of function, suggesting a clinically-relevant role for non-methyl DNA-binding by MECP2.

neuroscience↗

bioRxiv2024

Read Length Dominates Phylogenetic Placement Accuracy of Ancient DNA Reads

A common problem when analyzing ancient DNA (aDNA) data is to identify the species which corresponds to the recovered aDNA sequence(s). The standard approach is to deploy sequence similarity based tools such as BLAST. However, as aDNA reads may frequently either stem from unsampled taxa due to extinction, it is likely that there is no exact match in any database. As a consequence, these tools may not be able to accurately place such reads in a phylogenetic context. Phylogenetic placement is a technique where a read is placed onto a specific branch of a phylogenetic reference tree, which allows for a substantially finer resolution when identifying reads. Prior applications of phylogenetic placement has deployed only on data from extant sources. Therefore, it is unclear how the aDNA damage affects phylogenetic placements applicability to aDNA data. To investigate how aDNA damage affects placement accuracy, we re-implemented a statistical model of aDNA damage. We deploy this model, along with a modified version of the existing assessment pipeline PEWO, to 7 empirical datasets with 4 leading tools: APPLES, EPA-ng, pplacer, and RAPPAS. We explore the aDNA damage parameter space via a grid search in order to identify the aDNA damage factors that exhibit the largest impact on placement accuracy. We find that the frequency of DNA backbone nicks (and consequently read length) has the by far largest impact on aDNA read placement accuracy, and that other factors, such as misincorporations, have a negligible effect on overall placement accuracy.

bioinformatics↗

bioRxiv2024

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics

The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TRs coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use. Author SummaryA common way to analyze gene expression (transcriptomics) data is to correlate gene transcript levels across samples for every pair of genes (coexpression). Coordinated expression between genes may imply a shared biological function, though this warrants cautious interpretation given assumptions about cellular processes inferred from RNA abundances alone. Still, coexpression inference is often used to nominate genes whose expression may be controlled by transcription regulators (TRs). The rapid generation of diverse single cell transcriptomics data has unlocked our ability to discover coexpression patterns across individual cells -- though these signals are often noisy. Reproducible patterns across studies can help distinguish meaningful biological relationships from spurious correlations. We used this study to analyze a broad collection of single cell data spanning numerous tissues in human and mouse to infer global TR coexpression patterns. We aimed to learn which interactions were generally observable, to better potentiate future examinations of reproducible coexpression in specific contexts. We evaluate the predictive performance of these global single cell coexpression rankings using independent gene regulation evidence, and highlight TR-gene pairs that are supported across data modalities as well as species. By disseminating these rankings, we hope that other researchers can extract insight for their own TRs of interest.

bioinformatics↗

bioRxiv2023

Development of a high-throughput pipeline to characterize microglia morphological states at a single-cell resolution

As rapid responders to their environments, microglia engage in functions that are mirrored by their cellular morphology. Microglia are classically thought to exhibit a ramified morphology under homeostatic conditions which switches to an ameboid form during inflammatory conditions. However, microglia display a wide spectrum of morphologies outside of this dichotomy, including rod-like, ramified, ameboid, and hypertrophic states, which have been observed across brain regions, neurodevelopmental timepoints, and various pathological contexts. We used dimensionality reduction and clustering approaches to consider contributions of multiple morphology measures together to define a spectrum of microglial morphological states. Using ImageJ tools, we first developed a semi-automated approach to characterize 27 morphology features from hundreds to thousands of individual microglial cells in a brain subregion-specific manner. Within this pool of morphology measures, we defined distinct sets of highly correlated features that describe different aspects of morphology, including branch length, branching complexity, territory span, and cell circularity. When considered together, these sets of features drove different morphological clusters. Furthermore, our analysis toolset captured morphological states similarly and robustly when applied to independent datasets and using different immunofluorescent markers for microglia. We have compiled our morphology analysis pipeline into an accessible, easy to use, and fully open-source ImageJ macro and R package that the neuroscience community can expand upon and directly apply to their own analyses. Outcomes from this work will supply the field with new tools to systematically evaluate the heterogeneity of microglia morphological states across various experimental models and research questions.

neuroscience↗

bioRxiv2022

Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets

Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. While research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we demonstrate a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR-target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR-target listings, as well as transparent experiment-level gene summaries for community use.

bioinformatics↗

bioRxiv2022

Balancing selection on genomic deletion polymorphisms in humans

A key question in biology is why genomic variation persists in a population for extended periods. Recent studies have identified examples of genomic deletions that have remained polymorphic in the human lineage for hundreds of millennia, ostensibly owing to balancing selection. Nevertheless, genome-wide investigations of ancient and possibly adaptive deletions remain an imperative exercise. Here, we used simulations to show an excess of ancient allele sharing between modern and archaic human genomes that cannot be explained solely by introgression or ancient structure under neutrality. We identified 63 deletion polymorphisms that emerged before the divergence of humans and Neanderthals and are associated with GWAS traits. We used empirical and simulation-based analyses to show that the haplotypes that harbor these functional ancient deletions have likely been evolving under time- and geography-dependent balancing selection. Collectively, our results suggest that balancing selection may have maintained at least 27% of the functional deletion polymorphisms in humans for hundreds of thousands of years.

evolutionary biology↗