Biology subjects

Marjoram, P.

Publications and source records attributed to Marjoram, P..

5 recordsLinked to original sources

bioRxiv2026

Beyond Pathway Boundaries: A Degree-Aware Network Clustering Test for Gene Sets

Over-representation analysis (ORA) is the most commonly used interpretation tool for gene lists despite well-documented limitations: pathway boundaries are fixed, genes are assumed independent, and results depend on the background set. Network-based methods address these using interaction-network modularity, but introduce hub bias: highly connected genes appear clustered under naive nulls because curated networks overrepresent well-studied genes. Existing corrections are imperfect: edge permutation destroys the topology the test should condition on, and propagation methods hide the confound in parameter tuning. We introduce MANGO (Morans Autocorrelation for Network Gene Over-representation), which asks one conditional question: does a gene sets spatial autocorrelation on a fixed biological network exceed what its degree composition alone would predict? MANGO computes Global Morans I under a null that conditions on both the network and the binned degree distribution of the gene set, then decomposes significant signals at the component and gene level. In benchmarks, uniform nulls produce a false positive rate of 1.0 on hub-enriched gene sets with no real clustering; ten-bin degree-stratified nulls bring that to 0.0 with no power loss (AUC [≥] 0.98; on degree-typical signals, |{Delta}AUC| [≤] 0.004). Pathway-spiking simulations confirm detection of real biological clustering across diverse pathway sizes and degree profiles. Applied to the FIGI colorectal cancer GWAS (204 SNPs), the set is degree-typical (KS p = 0.83), yet Morans I is highly significant (p < 0.001). Component-level jackknife localizes the entire signal to a single 24-gene module spanning TGF-{beta}, Wnt/cadherin, and related pathways, with four bottlenecks (SMAD3, MYC, CTNNB1, PTPN1) matching established CRC driver biology. eTOC blurbMANGO tests whether a gene sets spatial autocorrelation on a biological network exceeds what its degree composition predicts, by conditioning Global Morans I on the binned degree distribution with the network held fixed. Significant signals are decomposed to modules, bottleneck genes, and statistical drivers through component jackknife, articulation-point, and gene-jackknife analysis.

bioinformatics↗

bioRxiv2021

Functional Human Genes Typically Exhibit Epigenetic Conservation

Recent DepMap CRISPR-Cas9 single gene disruptions have identified genes more essential to proliferation in tissue culture. It would be valuable to translate these finding with measurements more practical for human tissues. Here we show that DepMap essential genes and other literature curated functional genes exhibit cell-specific preferential epigenetic conservation when DNA methylation measurements are compared between replicate cell lines and between intestinal crypts from the same individual. Culture experiments indicate that epigenetic drift accumulates through time with smaller differences in more functional genes. In NCI-60 cell lines, greater targeted gene conservation correlated with greater drug sensitivity. These studies indicate that two measurements separated in time allow normal or neoplastic cells to signal through conservation which human genes are more essential to their survival in vitro or in vivo.

systems biology↗

bioRxiv2020

On the automatic annotation of gene functions using observational data and phylogenetic trees

MotivationGene function annotation is important for a variety of downstream analyses of genetic data. Yet experimental characterization of function remains costly and slow, making computational prediction an important endeavor. In this paper we use a probabilistic evolutionary model built upon phylogenetic trees and experimental Gene Ontology functional annotations that allows automated prediction of function for unannotated genes. ResultsWe have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out validation, and we further validated some of the predictions in the experimental scientific literature. AvailabilityOur method has been implemented as an R package and it is available online at https://github.com/USCBiostats/aphylo. Code needed to reproduce the tables and figures can be found in https://github.com/USCbiostats/aphylo-simulations. Author summaryUnderstanding the individual role that genes play in life is a key issue in biomedical-sciences. While information regarding gene functions is continuously growing, the number of genes with unknown biological purpose is yet greater. Because of this, scientists have dedicated much of their time to build and design tools that automatically infer gene functions. In this paper, we present yet another attempt to do such. While very simple, our model of gene-function evolution has some key features that have the potential to generate an impact in the field: (a) compared to other methods, ours is highly-scalable, which means that it is possible to simultaneously analyze hundreds of what are known as gene-families, compromising thousands of genes, (b) supports our biological intuition as our models data-driven results coherently agree with what theory dictates regarding how gene-functions evolved, (c) notwithstanding its simplicity, the models prediction accuracy is comparable to other more complex alternatives, and (d) perhaps most importantly, it can be used to both support new annotations and to suggest areas in which existing annotations show inconsistencies that may indicate errors or controversies.

bioinformatics↗

bioRxiv2019

Tracking somatic drift reveals small effective population size of stem cells and high somatic mutation rate in asexual planaria

Planarian flatworms have emerged as highly promising models of body regeneration due to the many stem cells scattered through their bodies. Currently, there is no consensus as to the number of stem cells active in each cycle of regeneration or the equality of their relative contributions. We approached this problem with a population genetic model of somatic genetic drift. We modeled the fissiparous life cycle of asexual planarians as an asexual population of cells that goes through repeated events of splitting into two subpopulations followed by population growth to restore the original size. We sampled a pedigree of obligate asexual clones of Girardia cf. tigrina at multiple time points encompassing 14 generations. Effective population size of stem cells was inferred from the magnitude of temporal fluctuations in the frequency of somatic variants and under most of the examined scenarios was estimated to be in the range of a few hundreds. Average genomic nucleotide diversity was 0.00398. Assuming neutral evolution and mutation-drift equilibrium, the somatic mutation rate was estimated in the 10-5 - 10-7 range. Alternatively, we estimated Ne and somatic from temporal changes in nucleotide diversity{pi} without the assumption of equilibrium. This second method suggested even smaller Ne and larger . A key unknown parameter in our model on which estimates of Ne and depend is g, the ratio of cellular to organismal generations determined by tissue turnover rate. Small effective number of propagating stem cells might contribute to reducing reproductive conflicts in clonal organisms.

genetics↗

bioRxiv2019

HiLDA: a statistical approach to investigate differences in mutational signatures

We propose a hierarchical latent Dirichlet allocation model (HiLDA) for characterizing somatic mutation data in cancer. The method allows us to infer mutational patterns and their relative frequencies in a set of tumor mutational catalogs and to compare the estimated frequencies between tumor sets. We apply our method to somatic mutations in colon cancer with mutations classified by the time of occurrence, before or after tumor initiation. Applying the methods to 16 colon cancers, we found significant associations between the relative frequencies of mutational patterns and the time of occurrence of mutations. Our novel method provides higher statistical power for detecting differences in mutational signatures.

genomics↗