Biology subjects

Munn, P. R.

Publications and source records attributed to Munn, P. R..

8 recordsLinked to original sources

bioRxiv2026

From Prompt to Pipeline: A Comparative Evaluation of Large Language Model Coding Agents for Reproducible Bioinformatics Pipeline Construction

Agentic coding systems are increasingly presented as a way to reduce the engineering burden of scientific software development. Bioinformatics is a strong test case for this claim because useful pipelines must combine domain-specific analysis choices, command-line software, sample metadata, workflow orchestration, container or HPC execution, and interpretable quality-control reporting. We evaluated three agentic systems - Biomni, Claude Code, and Codex - on the same task: constructing a Nextflow DSL2 pipeline for paired-end CUT&Tag data that included read QC, trimming, alignment, filtering, duplicate removal, signal track generation, per-sample and group-level peak calling, control-aware group merging, annotation, FRiP calculation, deepTools visualizations, and final MultiQC reporting. Each system received the same detailed CRAFT-style prompt and was assessed against a hand-coded reference pipeline developed by the authors. All three systems produced pipeline implementations that appeared plausible at the level of documentation and file structure, but none fully satisfied the requested analysis. The most consequential failure was shared: the agent-generated pipelines performed some form of group-level merging but did not produce the requested merged-group reporting outputs. Sample-level MultiQC reports also disagreed with the reference report. Codex was closest to the reference for primary mapped-read counts, although its total-read accounting and report structure still differed. Claude Code produced the broadest final report, but its mapping summary mixed stages and therefore could not be treated as numerically correct. Biomni produced the strongest subjective documentation, but its read-count agreement with the reference report was poor and several failures required substantial Nextflow expertise to diagnose. These results suggest that current coding agents can accelerate scaffolding, documentation, and routine implementation, but they do not eliminate the need for expert review in bioinformatics workflow construction. For complex sequencing workflows, prompts must specify not only the biological intent, but also the exact stage semantics, acceptance tests, metadata contracts, expected report sections, resource propagation rules, and failure criteria needed to distinguish a plausible pipeline from a correct one.

bioinformatics↗

bioRxiv2026

BLink-seq delivers population-scale haplotypes without long reads: a scalable framework for non-model genomics

Information about segregating haplotypes and structural variation (SV) can be extremely rich for a variety of applications in population genomics but remains largely inaccessible for many non-model species. Of the available methods, linked-read sequencing is especially promising for its low cost and scalability, but its adoption remains limited. One existing linked-read method is Haplotagging, which barcodes sequencing reads to reconstruct long molecules that encode haplotype information, with the potential to generate phased whole-genome data and detect structural variants. In this study, we present BLink-seq, a novel Haplotagging method that is compatible with standard short-read next-generation sequencing platforms, is locally reproducible with low-cost reagents, and is scalable for high-throughput sample processing. We optimized library preparation parameters, explored their relationship to linked-read library metrics, and validated phasing performance and structural variant detection in two evolutionary extremes: an experimental Drosophila melanogaster cross of inbred lines carrying known inversions, and four Atlantic silverside (Menidia menidia) parent-offspring trios sourced from highly outbred, wild-caught populations. We then applied our protocol to a cohort of 376 silversides to demonstrate its scalability and potential for SV detection and genotype imputation. Using BLink-seq, we generated chromosome-scale phased blocks and identified known inversions in both validation datasets. We discovered previously uncharacterized structural complexity within a known adaptive inversion on silverside chromosome 11, demonstrating that linked-read data can refine our understanding of SV architecture beyond what short reads alone can resolve. Finally, we provide a user guide for researchers interested in using BLink-seq.

genomics↗

bioRxiv2025

Accurate de novo transcription unit annotation from run-on and sequencing data

Functional element annotations are critical tools used to provide insight into the molecular processes governing cell development, differentiation, and disease. Run-on and sequencing assays measure the production of nascent RNAs and can provide an effective data source for discovering functional elements. However, the accurate inference of functional elements from run-on sequencing data remains an open problem because the signal is noisy and challenging to model. Here we investigated computational approaches that convert run-on and sequencing data into annotations representing transcription units, including genes and non-coding RNAs. We developed a convolutional neural network, called convolutional discovery of gene anatomy using PRO-seq (CGAP), trained to identify different anatomical features of a transcription unit, which were then stitched together into transcript annotations using a hidden Markov model (HMM). Comparison with existing methods showed a significant performance improvement using our novel CGAP-HMM approach. We developed a voting system that ensembles the top three annotation strategies, resulting in large and significant improvements in transcription unit annotation accuracy over the best performing individual method. Finally, we also report a conditional generative adversarial network (cGAN) as a generative approach to transcription unit annotation that shows promise for further development. Collectively our work provides novel tools for de novo transcription unit annotation from run-on and sequencing data that are accurate enough to be useful in many applications.

bioinformatics↗

bioRxiv2024

Single-cell multi-omic analysis of fibrolamellar carcinoma reveals rewired cell-to-cell communication patterns and unique vulnerabilities

Fibrolamellar carcinoma (FLC) is a rare malignancy disproportionately affecting adolescents and young adults with no standard of care. FLC is characterized by thick stroma, which has long suggested an important role of the tumor microenvironment. Over the past decade, several studies have revealed aberrant markers and pathways in FLC. However, a significant drawback of these efforts is that they were conducted on bulk tumor samples. Consequently, identities and roles of distinct cell types within the tumor milieu, and the patterns of intercellular communication, have yet to be explored. In this study we unveil cell-type specific gene signatures, transcription factor networks, and super-enhancers in FLC using a multi-omics strategy that leverages both single-nucleus ATAC-seq and single-nucleus RNA-seq. We also infer completely rewired cell-to-cell communication patterns in FLC including signaling mediated by SPP1-CD44, MIF-ACKR3, GDF15-TGFBR2, and FGF7-FGFR. Finally, we validate findings with loss-of-function studies in several models including patient tissue slices, identifying vulnerabilities that merit further investigation as candidate therapeutic targets in FLC.

cancer biology↗

bioRxiv2024

Deciphering gene regulatory programs underlying functionally divergent naive T cell subsets

Naive CD8+ T cells are a heterogeneous population, with different subsets possessing distinct functions and kinetics upon activation. However, the gene regulatory circuits differentiating these naive subsets are not well studied. In this work, we analyzed a large collection of public and newly generated RNA-seq and ATAC-seq profiles of different subsets of naive CD8+ T cells, revealing significant differences in the gene regulatory landscapes between subsets. We leveraged these data by employing a network inference algorithm, Inferelator, to identify the transcriptional regulatory circuits active in each subset. The predicted transcriptional network of the naive CD8+ T cell pool was validated by multiple orthogonal approaches, including CUT&Tag and Micro-C. Interestingly, our network analysis revealed a novel role for Eomes in promoting effector cell differentiation in specific cell subsets. Moreover, we uncovered multiple novel regulators across a variety of subsets and discovered several modules of genes that were co-regulated by shared sets of transcription factors in distinct subsets. Collectively, our data defines the gene regulatory programs differentiating naive CD8+ T cells and facilitates the identification of novel transcription factors that may alter the propensity of naive CD8+ T cells to become effector or memory cells after infection.

systems biology↗

bioRxiv2022

Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation

ME/CFS is a serious and poorly understood disease. To understand immune dysregulation in ME/CFS, we used single-cell RNA-seq (scRNA-seq) to examine immune cells in cohorts of patients and controls. Post-exertional malaise (PEM), an exacerbation of symptoms following strenuous exercise, is a characteristic symptom of ME/CFS. Thus, to detect changes coincident with PEM, we also performed scRNA-seq on the same cohorts following exercise. At baseline, ME/CFS patients displayed dysregulation of classical monocytes suggestive of inappropriate differentiation and migration to tissue. We were able to identify both diseased and more normal monocytes within patients, and the fraction of diseased cells correlated with metrics of disease severity. Comparing the transcriptome at baseline and post-exercise challenge, we discovered patterns indicative of improper platelet activation in patients, with minimal changes elsewhere in the immune system. Taken together, these data identify immunological defects present at baseline in patients and an additional layer of dysregulation following exercise. HighlightsME/CFS is a debilitating disease with unknown causes. Here, we provide, for the first time, an extensive single cell resolution dataset detailing the gene expression programs of circulating immune cells of ME/CFS cases at baseline and after symptom provocation. We were able to detect robust dysregulation in certain immune cells from patients, with dysregulation of classical monocytes manifesting the strongest signal. Indeed, the fraction of aberrant monocytes in ME/CFS patients correlated with the degree of disease severity. Surprisingly, platelet transcriptomes were also altered in ME/CFS, and they were the only component of the immune system that showed large-scale changes following symptom provocation.

immunology↗

bioRxiv2020

Multiple stages of evolutionary change in anthrax toxin receptor expression in humans

The advent of animal husbandry and hunting increased human exposure to zoonotic pathogens. To understand how a zoonotic disease influenced human evolution, we studied changes in human expression of anthrax toxin receptor 2 (ANTXR2), which encodes a cell surface protein necessary for Bacillus anthracis virulence toxins to cause anthrax disease. In immune cells, ANTXR2 was 8-fold down-regulated in all available human samples compared to non-human primates, indicating regulatory changes early in the evolution of modern humans. We also observed multiple genetic signatures consistent with recent positive selection driving a European-specific decrease in ANTXR2 expression in several non-immune tissues affected by anthrax toxins. Our observations fit a model in which humans adapted to anthrax disease following early ecological changes associated with hunting and scavenging, as well as a second period of adaptation after the rise of modern agriculture.

genetics↗

bioRxiv2019

Chromatin conformation remains stable upon extensive transcriptional changes driven by heat shock

Heat shock (HS) initiates rapid, extensive, and evolutionarily conserved changes in transcription that are accompanied by chromatin decondensation and nucleosome loss at heat shock loci. Here we have employed in situ Hi-C to determine how heat stress affects long-range chromatin conformation in human and Drosophila cells. We found that compartments, topologically-associated domains (TADs), and looping interactions all remain unchanged by an acute HS. Knockdown of Heat Shock Factor 1 (HSF1), the master transcriptional regulator of the HS response, identified HSF1-dependent genes and revealed that up-regulation is often mediated by distal HSF1 bound enhancers. HSF1-dependent genes were usually found in the same TAD as the nearest HSF1 binding site. However, the HSF1 binding sites and the target promoters did not exhibit a focal increase in contact frequencies compared with surrounding regions, nor did we find evidence of increased contact frequency following HS. Integrating information about HSF1 binding strength, RNA polymerase abundance at the enhancer, and contact frequency with a target promoter in the non-heat shock (NHS) condition accurately predicted which up-regulated genes were direct targets of HSF1 during HS. Our results suggest that the chromatin conformation necessary for a robust HS response is pre-established in normal (uninduced) cells of diverse metazoan species.

molecular biology↗