Biology subjects

Pollock, D. D.

Publications and source records attributed to Pollock, D. D..

9 recordsLinked to original sources

bioRxiv2025

EcoKMER: One-stop shop for spatio-temporal metagenomic exploration using DataFed

Spatially distributed environmental sampling generates highly complex and multidimensional datasets illuminating key insights into microbial diversity, evolutionary-coevolutionary processes, and host-pathogen interactions. While these sampling methods generate high value datasets, dataset size, the dataset integration, visualization, analysis, and provenance tracking present significant bottlenecks to scientific discovery. To address this bottleneck, we developed EcoKMER, an R-Shiny front-end application designed to streamline metagenomic data accessibility and provide geospatial context to data in support of hypothesis-driven investigations into environmental sampling, supported by DataFed as its back-end data management platform. EcoKMER enables interactive visualization and filtering of harmonized metagenomic data and metadata using an interoperable approach, allowing users to extract spatially distributed sample-based metadata on top of environmental parameters such as geolocation, temperature, pH, for investigating ecological changes across time and space enhancing sample processing methods. As an example, we deployed this tool to track analysis of metagenomes from the organisms in the Salish Sea Estuary, consistent with existing community-accepted standards. Built on top of DataFed, a flexible and robust scientific data management system built for data lakehouse architectures, EcoKMER is positioned as a powerful tool to improve sampling strategy decision making, accelerate new insights for collaborative biological and environmental research, and fostering AI-ready analyses designed to enhance discovery and guidance for bioeconomic engineering. The published manuscript is available at https://www.osti.gov/biblio/3412938

bioinformatics↗

bioRxiv2025

Hydroplane I: one-shot probabilistic evolutionary analysis for scalable organizational identification.

Applying evolutionary genomics to microbial and viral community sequence information presents significant challenges. Metagenomic sequences are typically stored in large databases of short-read fragments with unknown relationships. Available tools for analysis are often slow, rely on incomplete subsets of data, or focus on narrowly defined sub-problems. Moreover, existing tools often depend on simplistic model assumptions or treat inferences as empirical data, which can distort downstream analyses. In this work, we present theory and initial validation of the first stage of a fast, one-shot Bayesian approach to metagenomic evolutionary analysis. Our primary result here demonstrates effective use of simple models in experimental design using collections of short proximal short oligonucleotide sequences (kmers) to detect probable homologs, and validates the speed, sensitivity and specificity of this approach in a test set of whole Prochlorococcus genome sequences. Significance StatementThe Hydroplane algorithm provides an efficient framework for analyzing co-evolutionary relationships among microbial and viral genomes using metagenomic data. Hydroplane guides the identification of homologous regions, estimates lineage diversity, and reveals evolutionary events such as mutation, divergence, selection, and horizontal gene transfer. Its computationally lightweight design supports scalable genome clustering and adaptation analyses, enabling the study of rare microbial and viral genomes across diverse ecological contexts. With an accessible implementation, Hydroplane broadens the scope of evolutionary genomics research, offering critical insights into host-pathogen dynamics and supporting biopreparedness through the study of microbial and viral genetic relationships.

evolutionary biology↗

bioRxiv2024

Synergy and antagonism in a genome-scale model of metabolic hijacking by bacteriophage

Bacteriophage auxiliary metabolic genes (AMGs) alter host metabolism by hijacking reactions. Previous studies used functional annotations to infer AMG impacts but neglected propagation effects on global metabolism and phage production. We demonstrate the first integration of AMGs and phage assembly into a genome-scale metabolic model, using a general method applied to the infection of Prochloroccocus marinus MED4 by P-HM2. We experimentally validate our approach to predicting AMG impact on growth using cp12 mutations in Syne-chococcus elongatus. We predict that 17 directly hijacked reactions substantially impact over 30% of the metabolism, including carbon fixation, photosynthesis, and nucleotide synthesis. We find that indirect impacts are synergistically and antagonistically coupled and are either phage-aligned--shifting feasible reaction velocities in accordance with maximal phage production--or phage-antialigned. Pareto optimization reveals that phage-aligned reactions limit host growth, while phage-antialigned reactions do not. We provide systems-level insight into AMG perturbations, highlighting how nontrivial cascading effects shape microbial functions.

systems biology↗

bioRxiv2022

Detecting macroevolutionary genotype-phenotype associations using error-corrected rates of protein convergence

On macroevolutionary timescales, extensive mutations and phylogenetic uncertainty mask the signals of genotype-phenotype associations underlying convergent evolution. To overcome this problem, we extended the widely used framework of nonsynonymous-to-synonymous substitution rate ratios and developed the novel metric{omega} C, which measures the error-corrected convergence rate of protein evolution. While{omega} C distinguishes natural selection from genetic noise and phylogenetic errors in simulation and real examples, its accuracy allows an exploratory genome-wide search of adaptive molecular convergence without phenotypic hypothesis or candidate genes. Using gene expression data, we explored over 20 million branch combinations in vertebrate genes and identified the joint convergence of expression patterns and protein sequences with amino acid substitutions in functionally important sites, providing hypotheses on undiscovered phenotypes. We further extended our method with a heuristic algorithm to detect highly repetitive convergence among computationally nontrivial higher-order phylogenetic combinations. Our approach allows bidirectional searches for genotype-phenotype associations, even in lineages that diverged for hundreds of millions of years.

evolutionary biology↗

bioRxiv2021

A fast, general synteny detection engine

The increasingly widespread availability of genomic data has created a growing need for fast, sensitive and scalable comparative analysis methods. A key aspect of comparative genomic analysis is the study of synteny, co-localized gene clusters shared among genomes due to descent from common ancestors. Synteny can provide unique insight into the origin, function, and evolution of genome architectures, but methods to identify syntenic patterns in genomic datasets are often inflexible and slow, and use diverse definitions of what counts as likely synteny. Moreover, the reliable identification of putatively syntenic regions (i.e., whether they are truly indicative of homology) with different lengths and signal to noise ratios can be difficult to quantify. Here, we present Mology, a fast, flexible, alignment-free, nonparametric method to detect regions of syntenic elements among genomes or other datasets. The core algorithm operates on consecutive, rank-ordered elements, which could be genes, operons, motifs, sequence fragments, or any other orderable element. It is agnostic to the physical distance between distinct elements and also to directionality and order within syntenic regions, although such considerations can be addressed post hoc. We describe the underlying statistical theory behind our analysis method, and employ a Monte Carlo approach to estimate the false positive rate and positive predictive values for putative syntenic regions. We also evaluate how varying amounts of noise affect recovery of true syntenic regions among Saccharomycetaceae yeast genomes with up to ~100 million years of divergence. We discuss different strategies for recursive application of our method on syntenic regions with sparser signal than considered here, as well as the general applicability of the core algorithm.

evolutionary biology↗

bioRxiv2020

Population genomic analyses of schistosome parasites highlight critical challenges facing end-game elimination efforts

Schistosomiasis persists in some Asian regions despite targeted end-game elimination measures. To determine the causes of this persistence, we performed reduced representation genome sequencing on Schistosoma japonicum miracidia collected across multiple years from transmission hotspots in Sichuan, China. We discovered strong geographic structure, suggesting that local rather than imported reservoirs are key sources of infection persistence. At the village level, parasites collected after praziquantel treatment are closely related to local pre-treatment populations. Schistosomes within villages are also highly related, suggesting that only a few parasites from a limited number of hosts drive re-infection. The close familial relationships among miracidia from different human hosts also implicates short transmission routes among humans. At the individual host level, genetic evidence indicates that multiple humans retain infections following referral for treatment. Our findings suggest that end-game schistosomiasis control measures should focus on completely extirpating local parasite reservoirs and confirming successful treatment of infected human hosts. One Sentence SummaryLocal reservoirs are the source for persistent schistosomiasis infections despite continuous efforts aimed at complete elimination.

evolutionary biology↗

bioRxiv2019

Finding and extending ancient simple sequence repeat-derived regions in the human genome

BackgroundPreviously, 3% of the human genome has been annotated as simple sequence repeats (SSRs), similar to the proportion annotated as protein coding. The origin of much of the genome is not well annotated, however, and some of the unidentified regions are likely to be ancient SSR-derived regions not identified by current methods. The identification of these regions is complicated because SSRs appear to evolve through complex cycles of expansion and contraction, often interrupted by mutations that alter both the repeated motif and mutation rate. We applied an empirical, kmer-based, approach to identify genome regions that are likely derived from SSRs.\n\nResultsThe sequences flanking annotated SSRs are enriched for similar sequences and for SSRs with similar motifs, suggesting that the evolutionary remains of SSR activity abound in regions near obvious SSRs. Using our previously described P-clouds approach, we identified SSR-clouds, groups of similar kmers (or oligos) that are enriched near a training set of unbroken SSR loci, and then used the SSR-clouds to detect likely SSR-derived regions throughout the genome.\n\nConclusionsOur analysis indicates that the amount of likely SSR-derived sequence in the human genome is 6.77%, over twice as much as previous estimates, including millions of newly identified ancient SSR-derived loci. SSR-clouds identified poly-A sequences adjacent to transposable element termini in over 74% of the oldest class of Alu (roughly, AluJ), validating the sensitivity of the approach. Poly-As annotated by SSR-clouds also had a length distribution that was more consistent with their poly-A origins, with mean about 35 bp even in older Alus. This work demonstrate that the high sensitivity provided by SSR-Clouds improves the detection of SSR-derived regions and will enable deeper analysis of how decaying repeats contribute to genome structure.

genomics↗

bioRxiv2018

Amalgamated cross-species transcriptomes indicate organ-specific preadaptation for functional shifts in gene expression

The origins of multicellular physiology are tied to evolution of gene expression. Genes can shift expression as organisms evolve, but how ancestral expression influences altered descendant expression is not well understood. To examine this, we amalgamated 1,903 RNA-seq datasets from 182 research projects, including 6 organs in 21 vertebrate species. Quality control eliminated project-specific biases, and expression shifts were reconstructed using gene-family-wise phylogenetic Ornstein-Uhlenbeck models. Expression shifts following gene duplication result in more drastic changes in expression properties than shifts without gene duplication. The expression properties were tightly coupled with protein evolutionary rate, depending on whether and how gene duplication occurred. Fluxes in expression patterns among organs were nonrandom, forming modular connections which were reshaped by gene duplication. Thus, if expression shifted, ancestral expression in some organs induces a strong propensity for expression in particular organs in descendants. This supports a major role for what might be termed "preadaptive" pathways of gene expression evolution.

evolutionary biology↗

bioRxiv2017

PRDM9 and an Epidemic of Gene Conversion and Non-Homologous Recombination among Alu Elements in Ancestral Gorillas

We performed a genome-wide scan for recombination-mediated interlocus gene conversion and deletion events among a set of orthologous Alu loci in the Great Apes, and were surprised to discover an extreme excess of such events in the gorilla lineage versus other lineages. Gorilla events, but not events in other Great Apes, are strongly associated with a 15 bp motif commonly found in Alu sequences. This result is consistent with evolutionarily transient targeting of the motif by PRDM9, which induces double strand breaks and crossovers during meiosis at specific but rapidly changing sequence motifs. The motif is preferentially found in conversion recipients but not donors, and is substantially depleted in gorillas, consistent with loss of PRDM9 targets by meiotic drive. Recombination probability falls of exponentially with distance between loci, is reduced slightly by sequence divergence, and drops substantially with recipient divergence from the target motif. We identified 16 other high-copy motifs in human, often associated with transposable elements, with lineage-specific depletion and nearby gene conversion signatures, consistent with transient roles as PRDM9 targets. This work strengthens our understanding of recombination-mediated events in evolution and highlights the potential for interactions between PRDM9 and repetitive sequences to cause rapid change in the genome.

genomics↗