Biology subjects

Hallett, M. T.

Publications and source records attributed to Hallett, M. T..

6 recordsLinked to original sources

bioRxiv2026

An agentic AI environment to support biomedical research in collaborative academic environments

Agentic artificial intelligence (AAI) is increasingly used by biomedical scientists, where it has substantially lowered the difficulty of integrating computational, statistical and data-science approaches into day-to-day research activities, assisting with project design, analysis and manuscript preparation. Significant barriers remain, however, which are unlikely to be removed solely by releasing more capable AI models. Meaningful use still demands a technical fluency that many biomedical researchers lack: a PI and their lab members must learn how to use the AAI system, establish standard operating procedures (SOPs) for managing, sharing and curating data, and enforce rules that address data privacy and security. Existing commercial AAI systems, which were designed primarily for software engineering purposes, need to be significantly re-purposed for biomedical-related data science. We present Murmurent, shared software that sits beneath the agentic AI and provides the skills, procedures and protections to address the concerns above, removing the need for a lab to develop them independently. This includes (1) infrastructure to support projects and so-called choreographies} involving multiple group members; (2) a set of specialized agents each dedicated to typical biomedical data science tasks including software and statistical development, data visualization, literature search, equity, diversity, inclusion, and decolonization (EDID) review, and others; (3) a tiered memory which can retain important research decisions, intermediate, derived outputs and sensitive data, and which can exploit this information to automatically build better contexts in AAI sessions; (4) traceability records which are used by the AAI session to better plan and execute data analyses and software builds by, for example, helping to avoid repeating decisions that lead to dead-ends; (5) enforcement of SOPs for data maintenance and data governance across all lab members; and (6) multi-user capacity to allow groups to interact and collaborate. We use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), a protein with a shallow catalytic site making it difficult to target. We describe the construction of several approaches to identify Pin1 inhibitors and the results they yield. Murmurent is open source.

bioinformatics↗

bioRxiv2025

Generative and integrative modeling for transcriptomics with formalin fixed paraffin embedded material

Formalin-fixed paraffin embedded (FFPE) samples are challenging to profile using existing high-throughput sequencing technologies, including RNA-seq. This difficulty primarily arises from the degradation of nucleic acids, a problem that becomes particularly acute with samples stored for extended periods. FFPE-derived RNA-seq (fRNA-seq) data have a high rate of transcript dropout, a property shared with single cell RNA-seq. Transcript counts also have high variance and are prone to extreme values. We introduce the PaRaffin Embedded Formalin-FixEd Cleaning Tool (PREFFECT), a probabilistic framework for the analysis of fRNA-seq data. PREFFECT uses generative models to fit distributions to observed expression counts while adjusting for technical and biological variables. The framework can exploit multiple expression profiles generated from matched tissues for a single sample (e.g., a tumor and morphologically normal tissue) in order to stabilize profiles and impute missing counts. PREFFECT can also leverage sample-sample adjacency networks that assist graph attention mechanisms to identify the most informative correlations in the data. We demonstrate how PREFFECT uses this information to more accurately compute downstream analyses such as sample clustering in several datasets.

bioinformatics↗

bioRxiv2022

Candida albicans exhibits heterogeneous and adaptive cytoprotective responses to anti-fungal compounds

Candida albicans is an opportunistic human pathogen which represents a significant threat to human health and is associated with substantial socio-economic burden. Current antifungal treatments fail at least in part because C. albicans can initiate a strong drug tolerance response, allowing cells to grow at concentrations above their minimal inhibitory concentration. Our goal is to better characterize this cytoprotective tolerance program at the molecular single cell level. We present here a nano-liter droplet-based fungal single cell transcriptomics platform capable of profiling thousands of individual C. albicans SC5314 cells in an efficient manner. Profiles of untreated cells partition into three transcriptional clusters with each highlighting a cell cycle checkpoint coupled with specific metabolic and stress responses, as perhaps expected. After just two days post-treatment with fluconazole, surviving cells bifurcate into two distinct subpopulations: the so-called response involving upregulation of protein translation, rRNA processing and mitochondrial cellular respiration, and the {beta} response involving processes and stress responses that assist damaged cells. By extending our time series to six days and profiling with other antifungals and bioactive compounds, we provide evidence that surviving cells transition from the to {beta} responses mediated by the Ribosome Assembly Stress Response (RASTR).

microbiology↗

bioRxiv2021

A deep learning approach to capture the essence of Candida albicans morphologies

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen C. albicans. Our system entitled Candescence automatically detects C. albicans cells from Differential Image Contrast microscopy, and labels each detected cell with one of nine vegetative, mating-competent or filamentous morphologies. The software is based upon a fully convolutional one-stage object detector and exploits a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple vegetative forms to more complex filamentous architectures. Candescence achieves very good performance on this difficult learning set which has substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology, we develop models using generative adversarial networks and identify subcomponents of the latent space which control technical variables, developmental trajectories or morphological switches. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology.

microbiology↗

bioRxiv2021

A metagenomic-based stufy of two sites from the Barbadian reef system

We study the microbiome of sea water collected from two locations of the Barbadian coral reefs. The two sites differ in several environmental and ecological variables including their endogenous benthic community in addition to their proximity to urban development and runoffs from inland watersheds. The composition of the microbial community was estimated using whole genome DNA shotgun sequencing. Although both sites exhibit a similar degree of richness, the less urbanized site (Maycocks reef at Hangmans Bay) is strongly concentrated with phototrophs. In comparison, the more urbanized location (Bellairs Research Institute) is enriched for copiotrophs, macroalgal symbionts and marine-related disease-bearing organisms from taxa scattered across the tree of life. Overall, our samples and associated measurements of chemical and environmental qualities of the water are in line with previous marine microbiome profiles of warm ocean surface waters. This suggests our approach captures salient information regarding the state of each coral reef site and sets the stage for larger longitudinal studies of coral reef dynamics in Barbados.

microbiology↗

bioRxiv2020

Mysterious disappearances of a large mammal in Neotropical forests

The drivers of periodic population cycling by some animal species in northern systems remain unresolved1. Mysterious disappearances of populations of the Neotropical, herdforming white-lipped peccary (Tayassu pecari, henceforth "WLP") have been anecdotally documented and explained as local events resulting from migratory movements or overhunting2,3,4, or as disease outbreaks5,6, and have not been considered in the context of large-scale species-specific population dynamics. Here we present evidence that WLP disappearances represent troughs in population cycles that occur with regular periodicity and are synchronized at regional and perhaps continent-wide spatial scales. Analysis of 43 disappearance events and 88 years of commercial and subsistence harvesting data reveals boom - bust population cycles lasting from 20 to 30 years, in which a rapid population crash occurring over 1 to 5 years is followed by a period of absence of 7 to12 years and then a slow growth phase. Overhunting alone cannot explain the crashes, but as in northern systems dispersal during the growth phase appears to play a key role. This is the first documentation of population cycling in a tropical vertebrate.

ecology↗