Biology subjects

Conard, A. M.

Publications and source records attributed to Conard, A. M..

9 recordsLinked to original sources

bioRxiv2026

EPIC: An open community challenge for sequence-based prediction of transcription initiation in five non-model metazoans

Predicting gene expression from DNA sequence is a central problem with critical biological and clinical implications. Recent sequence-to-function models are reported to achieve improved performance, yet it remains unclear how much of what they learn reflects genuine principles of eukaryotic transcription initiation and to what extent they are able to generalize beyond humans and other primary model species. A fair and blind benchmark has likewise been missing. Here we introduce EPIC, the Eukaryotic Promoter and transcription Initiation prediction Challenge. Teams receive strand-specific, single-nucleotide-resolution initiation profiles for 80-95% of the genome and are asked to predict the remainder from DNA sequence alone. To level the field, the challenge relies on understudied animals spanning three phyla: octopus, oyster, milkweed bug, Indian meal moth, and shark. EPIC is open to everyone and closes on December 31, 2026. All teams clearing the dinucleotide precision baseline are invited to join the consortium authorship of the post-challenge publication, and winners are invited for personal authorship.

bioinformatics↗

bioRxiv2026

Predicting evolutionary rate as a pretraining task improves genome language model representations

Genome language models (gLM) have the potential to further understanding of regulatory genomics without requiring labeled data. Most gLMs are pretrained using sequence reconstruction tasks inspired by natural language processing, but recent studies have shown that these gLMs often fail to capture biological signal. To overcome this, we introduce pretraining tasks that predict the rate of evolution. These tasks are designed so that they can be composed with sequence reconstruction, enabling a controlled comparison of predicting sequence only, evolutionary rate only, or both. To address gaps in existing evaluations, we developed a suite of biologically grounded benchmarks. Across these tasks, and for established variant effect prediction benchmarks, models pretrained on both sequence and evolutionary rate outperform those trained on sequence alone, and training on evolutionary rate can make the even the relatively small models in our work competitive with much larger existing gLMs for some tasks. These results establish evolution as a key training target for genome-scale models.

genomics↗

bioRxiv2025

Evidence Aggregator: AI reasoning applied to rare disease diagnostics

Variant assessment of rare disease diagnostics depends on using domain knowledge in the time- consuming process of retrieving, reviewing, and synthesizing clinical and technical information. To address these challenges, we developed the Evidence Aggregator (EvAgg), an open-source, generative-AI-based tool designed for rare disease diagnosis that systematically extracts relevant information from the scientific literature for any human gene. EvAgg provides a thorough and current summary of observed genetic variants and their associated clinical features, enabling rapid synthesis of evidence concerning gene-disease relationships. We constructed an expert-curated dataset and evaluated EvAggs performance. EvAgg achieves 92% recall in identifying relevant papers, 96% recall in detecting instances of genetic variation within those papers, and [~]80% accuracy in extracting individual case and variant-level content (e.g. zygosity, inheritance, variant type, and phenotype). Further, EvAgg complemented the process of manual literature review by identifying substantial additional relevant information. When tested with analysts in rare disease case analysis, EvAgg reduced review time by 34% (p-value < 0.002) and increased the number of papers, variants, and cases evaluated per unit time. These savings have the potential to reduce diagnostic latency and increase solve rates for challenging rare disease cases.

genomics↗

bioRxiv2024

Dual DNA/RNA-binding factor regulates dynamics of hnRNP splicing condensates

How RNA splicing events are targeted to the correct genomic locations in specific cellular contexts to generate context-specific transcript diversity and prevent deleterious cryptic splicing remains very poorly understood. We show that a functionally conserved GA-rich DNA-binding transcription factor (TF), CLAMP, targets distinct RNA molecules in male and female cells to precisely regulate sex-specific splicing events through physical and functional interactions with RNA and RNA-binding proteins (RBPs). The prion-like domain of CLAMP (PrLD) and a stem-loop region in the target RNA are important for CLAMP-RNA interaction. Moreover, the CLAMP PrLD domain regulates sex-specific splicing by modulating the dynamics of an hnRNPA2/B1 family protein that regulates alternative splicing. Thus, we demonstrate that a TF targets co-transcriptional splicing to the correct genomic locations by directly linking DNA binding sites to RNA targets and modulating the dynamics of RBP partners that drive alternative splicing.

genomics↗

bioRxiv2023

Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks

In this paper, we aim to build a tool that will help bridge the gap between high-dimensional computation and wet-lab experimentation by allowing users to interrogate genomic signatures at multiple molecular levels and identify best next actionable steps for downstream decision making. We introduce Multioviz: a publicly accessible R package and web application platform to easily perform in silico hypothesis testing of generated gene regulatory networks. We demonstrate the utility of Multioviz by conducting an end-to-end analysis in a statistical genetics application focused on measuring the effect of in silico perturbations of complex trait architecture. By using a real data set from the Wellcome Trust Centre for Human Genetics, we both recapitulate previous findings and propose hypotheses about the genes involved in the percentage of immune CD8+ cells found in heterogeneous stocks of mice. Source code for the Multioviz R package is available at https://github.com/lcrawlab/multio-viz and an interactive version of the platform is available at multioviz.ccv.brown.edu.

bioinformatics↗

bioRxiv2021

Sex-specific transcript diversity is regulated by a maternal pioneer factor in early Drosophila embryos

Co-transcriptional splicing coordinates the processes of transcription and splicing and is driven by transcription factors (TFs) and diverse RNA-binding proteins (RBPs). Yet the mechanisms by which specific TFs and RBPs function together in context-specific ways to drive precise co-transcriptional splicing at each of thousands of genomic loci remains unknown. Therefore, we have used sex-specific splicing in Drosophila as a model to understand how the function of TFs and RBPs is coordinated to transcribe and process specific RNA transcripts at the correct genomic locations. We show widespread sex-specific transcript diversity occurs much earlier than previously thought and present a new pipeline called time2splice to quantify splicing changes over time. We define several mechanisms by which the essential and functionally-conserved CLAMP TF functions with specific RBPs to precisely regulate co-transcriptional splicing: 1) CLAMP links the DNA of gene bodies of sex-specifically spliced genes directly to the RNA of target genes and physically interacts with snRNA and protein components of the splicing machinery; 2) In males, CLAMP regulates the distribution of the highly conserved RBP Maleless (MLE) (RNA Helicase A) to prevent aberrant sex-specific splicing; 3) In females, CLAMP modulates alternative splicing by directly binding to target DNA and RNA and indirectly through regulating the splicing of sex lethal, the master regulator of sex determination. Overall, we provide new insight into how TFs function specifically with RBPs to drive alternative splicing.

genomics↗

bioRxiv2021

Neuromolecular and behavioral effects of ethanol deprivation in Drosophila

Alcohol use disorder (AUD) is characterized by loss of control in limiting alcohol intake. This may involve intermittent periods of abstinence followed by alcohol seeking and, consequently, relapse. However, little is understood of the molecular mechanisms underlying the impact of alcohol deprivation on behavior. Using a new Drosophila melanogaster repeated intermittent alcohol exposure model, we sought to identify how ethanol deprivation alters spontaneous behavior, determine the associated neural structures, and reveal correlated changes in brain gene expression. We found that repeated intermittent ethanol-odor exposures followed by ethanol-deprivation dynamically induces behaviors associated with a negative affect state. Although behavioral states broadly mapped to many brain regions, persistent changes in social behaviors mapped to the mushroom body and surrounding neuropil. This occurred concurrently with changes in expression of genes associated with sensory responses, neural plasticity, and immunity. Like social behaviors, immune response genes were upregulated following three-day repeated intermittent ethanol-odor exposures and persisted with one or two days of ethanol-deprivation, suggesting an enduring change in molecular function. Our study provides a framework for identifying how ethanol deprivation alters behavior with correlated underlying circuit and molecular changes.

neuroscience↗

bioRxiv2020

The transcription factor CLAMP is required for neurogenesis in Drosophila melanogaster.

Neural stem cell (NSC) differentiation is controlled by cell-intrinsic and external signals from the stem cell niche including niche surface glia (SG). However, the mechanisms by which transcription factors drive NSC differentiation within the niche remain largely unknown. Here, we show that the Drosophila melanogaster transcription factor Chromatin-linked adaptor for MSL proteins (CLAMP) is required for regulation of stemness and proliferation of NSCs, especially in the optic lobe (OL). Using a validated clamp null mutant, we show that CLAMP promotes transcription of genes involved in stemness, proliferation, and glial development and represses transcription of genes involved in neurogenesis and niche survival. Consistent with transcriptional changes, CLAMP promotes NSC proliferation and niche SG production, while lack of CLAMP severely and specifically impacts OL development. To identify potential mechanisms by which CLAMP may regulate brain development, we examined CLAMP binding site motifs and available CLAMP ChIP-seq data to determine which genes may be direct versus indirect targets. We found that genes encoding Notch and tailless, both master regulators of brain development, are directly bound by CLAMP, indicating that they are key direct targets of CLAMP. Moreover, Notch and Tailless are critical for OL development, providing a mechanistic link to the severe deficits observed in OL development in the clamp null mutants. Overall, our results suggest that CLAMP regulates a core transcriptional program which drives NSC proliferation and differentiation via cell-intrinsic and niche-dependent mechanisms involving niche glia and transcriptional regulation of Notch and Tailless.

neuroscience↗

bioRxiv2020

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Uncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.

bioinformatics↗