Biology subjects

Mumey, B.

Publications and source records attributed to Mumey, B..

3 recordsLinked to original sources

bioRxiv2026

PanACRpred: Predicting Accessible Chromatin Regions in Pangenomes using Motif Chaining

In this work, we investigate using motif subsequence features to predict whether a genomic region is accessible to regulatory proteins, i.e. an accessible chromatin region (ACR), enabling transcription of associated genes. We focus on plants, whose agricultural and ecological importance make them interesting and important organisms to study, and whose complex genomes provide important stress tests for our algorithm. We show that motif sequence similarity as found by co-linear chaining can be used in combination with machine learning models to effectively predict ACRs in genome assemblies.

bioinformatics↗

bioRxiv2025

EssentCell: Discovering Essential Evolutionary Relations in Noisy Single-Cell Data

Single-cell sequencing (SCS) enables investigating tumor evolution at a single cell resolution. A common type of analysis to investigate evolutionary structure from an SCS experiment is to determine a phylogenetic tree structure from the data. This problem has been well-studied under the assumption that mutations only accumulate in the evolution of cancer and there is a simple characterization of when the data is compatible with a perfect phylogeny based on the absence of a special "conflict" submatrix. SCS data can be represented as a binary matrix, where the ij-th entry indicates whether cell i has mutation j. In practice, SCS data is noisy, so a natural question is what is the minimum number of entries to flip in the data matrix, in order that the matrix becomes "conflict-free" and thus compatible with a perfect phylogeny. Furthermore, the false positive rate is orders of magnitude smaller than the false negative rate, so that at most a few false positives occur with high probability. We consider a variation of the minimum-flip problem in which the number of false positives in the solution is a parameter. Often, there can be multiple optimal solutions, so a natural question is what relations are true among all optimal solutions for a small range of possible false positives values; we call such relations essential. In this work, we propose an efficient algorithm based on integer linear programming to determine all essential relations in the data. We test our software tool, EssentCell, on several data sets and discuss the results found.

cancer biology↗

bioRxiv2018

Physiological Dynamic Compression Regulates Central Energy Metabolism in Primary Human Chondrocytes

Chondrocytes use the pathways of central metabolism to synthesize molecular building blocks and energy for cartilage homeostasis. An interesting feature of the in vivo chondrocyte environment is the cyclical loading generated in various activities (e.g. walking). However, it is unknown if central metabolism is altered by mechanical loading. We hypothesized that physiological dynamic compression alters central metabolism in chondrocytes to promote production of amino acid precursors for matrix synthesis. We measured the expression of central metabolites (e.g. glucose, its derivatives, and relevant co-factors) for primary human osteoarthritic chondrocytes in response to 0-30 minutes of compression. To analyze the data, we used principal components analysis and ANOVA simultaneous components analysis, as well as metabolic [fl]ux analysis. Compression induced metabolic responses consistent with our hypothesis. Additionally, these data show that chondrocyte samples from different patient donors exhibit different sensitivity to compression. Most important, we [fi]nd that grade IV osteoarthritic chondrocytes are capable of synthesizing non-essential amino acids and precursors in response to mechanical loading. These results suggest that further advances in metabolic engineering of chondrocyte mechanotransduction may yield novel translational strategies for cartilage repair.

systems biology↗