Biology subjects

Walsh, J. R.

Publications and source records attributed to Walsh, J. R..

5 recordsLinked to original sources

bioRxiv2026

Chromosome-arm-specific telomere length governs dual modes of structural genome evolution in IDH-mutant astrocytoma

IDH-mutant astrocytomas maintain telomeres through the alternative lengthening of telomeres (ALT) pathway, producing extreme inter-arm telomere length heterogeneity, yet how this heterogeneity shapes structural genome evolution remains unknown. Using Oxford Nanopore long-read sequencing of 20 IDH-mutant astrocytomas, we profiled structural variants (SVs), copy number variants, extrachromosomal DNA (ecDNA) and measured allele-specific telomere lengths from individual long reads. We identified pervasive complex rearrangements, including chromothripsis and foldback events consistent with breakage-fusion-bridge cycles, and widespread ecDNAs. SV breakpoints were enriched at telomeric and centromeric regions regardless of local telomere length, revealing constitutive structural fragility. Arm-level telomere length analysis uncovered a dual-mode model: arms with short telomeres preferentially harbored breakage-associated events, while arms with long ALT-maintained telomeres were enriched for ecDNA and amplification-associated events. These findings identify chromosome-arm-specific telomere length as a determinant of structural genome evolution in ALT-driven tumors.

cancer biology↗

bioRxiv2023

The aftermath of a trophic cascade: Increasedanoxia following species invasion of a eutrophiclake

Species invasions can disrupt aquatic ecosystems by re-wiring food webs. A trophic cascade triggered by the invasion of the predatory zooplankter spiny water flea (Bythotrephes cederstromii) resulted in increased phytoplankton due to decreased zooplankton grazing. Here, we show that increased phytoplankton biomass led to an increase in lake anoxia. The temporal and spatial extent of anoxia experienced a step change increase coincident with the invasion. Anoxia was driven by phytoplankton biomass and stratification changes, and anoxic factor increased by 10 days. In particular, anoxia established more quickly following spring stratification. A shift in spring phytoplankton phenology encompassed both abundance and community composition. Diatoms (Bacillaryophyta) drove the increase in spring phytoplankton biomass, but not all phytoplankton community members increased, shifting the community composition. We infer that increased phytoplankton biomass increased labile organic matter and drove hypolimnetic oxygen consumption. These results demonstrate how a species invasion can shift lake phenology and biogeochemistry. Scientific significance statementInvasive species can affect aquatic ecosystems, often by disrupting food webs. We investigated whether the invasive predatory zooplankton spiny water flea could additionally impact the biogeochemistry of a lake, specifically hypolimnetic anoxia dynamics. Using 24 years of observations spanning a spiny water flea invasion that triggered a food web-mediated increase in phytoplankton, we found that increased spring phytoplankton coincided with an earlier onset of anoxia, thereby drawing a connection between a species invasion and a shift in lake oxygen dynamics. Data availability statementAll data is publicly available through the Environmental Data Initiative via identifiers referenced in the methods. Scripts and data to reproduce the results are available on GitHub (https://github.com/robertladwig/spinyAnoxia) and in Rohwer et al. (2023). Author contributionsRRR and RL co-led the entire manuscript effort and contributed equally. RL and RRR came up with the research question and conducted the statistical and numerical analyses: RL analysed the anoxia dynamics and related water quality variables, RRR analysed the phytoplankton community dynamics. RL, RRR, and HAD created figures and visualizations. PCH, JW and JVZ provided essential feedback to the analyses and the discussion of ecosystem implications. RRR and RL co-wrote the paper.

ecology↗

bioRxiv2020

Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks

Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments. We use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to learn the relationships between covariates commonly used to characterize subjects and their disease progression in MS clinical trials. A CRBM is capable of generating digital twins, which are simulated subjects having the same baseline data as actual subjects. Digital twins allow for subject-level statistical analyses of disease progression. The CRBM is trained using data from 2395 subjects enrolled in the placebo arms of clinical trials across the three primary subtypes of MS. We discuss how CRBMs are trained and show that digital twins generated by the model are statistically indistinguishable from their actual subject counterparts along a number of measures.

bioinformatics↗

bioRxiv2019

Deep Learning of Representations for Transcriptomics-based Phenotype Prediction

The ability to predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. This task is complicated because expression data are high dimensional whereas each experiment is usually small (e.g.,[~] 20,000 genes may be measured for[~] 100 subjects). However, thousands of transcriptomics experiments with hundreds of thousands of samples are available in public repositories. Can representation learning techniques leverage these public data to improve predictive performance on other tasks? Here, we report a comprehensive analysis using different gene sets, normalization schemes, and machine learning methods on a set of 24 binary and multiclass prediction problems and 26 survival analysis tasks. Methods that combine large numbers of genes outperformed single gene methods, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses.

bioinformatics↗

bioRxiv2018

Who is this gene and what does it do? A toolkit for munging transcriptomics data in python

Transcriptional regulation is extremely complicated. Unfortunately, so is working with transcriptional data. Genes can be referred to using a multitude of different identifiers and are assigned to an ever increasing number of categories. Gene expression data may be available in a variety of units (e.g, counts, RPKMs, TPMs). Batch effects dominate signal, but metadata may not be available. Most of the tools are written in R. Here, we introduce a library, genemunge, that makes it easier to work with transcriptional data in python. This includes translating between various types of gene names, accessing Gene Ontology (GO) information, obtaining expression levels of genes in healthy tissue, correcting for batch effects, and using prior knowledge to select sets of genes for further analysis. Code for genemunge is freely available on Github.

bioinformatics↗