Biology subjects

Murdoch, R. W.

Publications and source records attributed to Murdoch, R. W..

4 recordsLinked to original sources

bioRxiv2026

Application of Computer Vision Tools to Maize Genomic Data for Trait Prediction and Gene Discovery

Artificial intelligence and machine learning for computer vision (CV) and image recognition is a rapidly evolving field with multiple potential applications in plant genomics. While CV has been widely adopted by the research community for plant phenotyping and disease surveillance, applications of CV tools to plant genome analysis are underrepresented. CV tools may complement traditional statistical classification tools used in plant genomics, since CV perceives problems holistically rather than granularly (in terms of pattern recognition), which is particularly applicable to analysis of large, complex eukaryotic genomes. In this study, we report on a new strategy to apply existing CV tools to classify plant genotypes and predict genotype-phenotype relationships. A technique was developed for converting maize genome resequencing data into a set of images reminiscent of a quick response (QR) code. Several hundred maize genomes were processed and it was demonstrated that CV models can successfully categorize genome images into heterotic groups (accuracy and recall > 0.8). Models for classifying genome images into phenotypic trait groups (such as short, medium, and high plant height) performed with moderate success for the most heritable trait analyzed (ear height; accuracy and recall > 0.5). Querying model results permitted identification of genome regions that were important for model classification predictions. The CV model results revealed enriched metabolic pathways consistent with traits under consideration. Overall, our initial application of CV tools to plant genome analysis highlights its applicability to genomic data. Design of new CV architectures optimized for genome-derived images may further improve upon our initial results generated using only off-the-shelf CV tools optimized for unrelated image analysis tasks. Core ideasO_LIAI/ML computer vision (CV) tools were applied to encoded maize genomes C_LIO_LICV image classification tools were able to successfully classify encoded genomes into heterotic groups C_LIO_LITrait values of maize strain ear height could be predicted with moderate success C_LIO_LIGenome regions encoding plausible metabolic pathways used by the classifier were identified C_LIO_LIRecommendations for improved success of CV for genotype-to-phenotype are discussed C_LI

bioinformatics↗

bioRxiv2025

Generation of a global freshwater algal taxonomic database by application of PCR-free rbcL gene detection and machine-learning-based taxonomic classification to public metagenome datasets

While it has become increasingly evident that microalgae are critical components of aquatic ecosystems, comprehensive taxonomic analysis of the microalgal component of microbial communities has lagged behind recent innovations in prokaryotic sequencing and bioinformatics. Microalgae are sequenced alongside prokaryotes in metagenomic whole genome shotgun (WGS) sequencing efforts but remain overlooked. In this study, an analytic pipeline for detecting a key algal taxonomic barcode, rbcL, from WGS datasets. This approach allowed for near-perfect detection of rbcL sequences from eight major algal phyla, whereas in silico PCR utilizing popular phyla-specific rbcL primer sets for four algal phyla suggests that such approaches may miss much algal diversity. Further, a machine learning classification model was built and validated to allow for taxonomic classification of rbcL sequences of eight algal phyla to genus or species level with high accuracy and recall values (>90%). Benchmarking of this approach against microscopic algal identifications showed accurate identifications for algae with higher relative abundance values and may offer additional detections of algae not readily identified by microscopy. This pipeline was applied against over 4,000 freshwater WGS metagenomes to generate a global taxonomic occurrence and relative abundance database of freshwater algae composed of over 14,000 accessions. WGS-based algal detection offers broad, unbiased taxon-agnostic algal detection in a single sample analysis. Furthermore, it allows for simultaneous identification of the prokaryotic community, offering new opportunities for studying inter-domain relationships as evidenced by a case-study presented herein. The rbcL taxonomic classifier and metagenome-derived algal detection database are available for download and use at https://doi.org/10.6084/m9.figshare.29996962.

bioinformatics↗

bioRxiv2021

Global distribution of anaerobic dichloromethane degradation potential

Anthropogenic activities and natural processes release dichloromethane (DCM), a toxic chemical with substantial ozone-depleting capacity. Specialized anaerobic bacteria metabolize DCM; however, the genetic basis for this process has remained elusive. Comparative genomics of the three known anaerobic DCM-degrading bacterial species revealed a homologous gene cluster, designated the methylene chloride catabolism (mec) gene cassette, comprising eight to ten genes with predicted 79.6 - 99.7% amino acid identity. Functional annotation identified genes encoding a corrinoid-dependent methyltransferase system, and shotgun proteomics applied to two DCM-catabolizing cultures revealed high expression of proteins encoded on the mec gene cluster during anaerobic growth with DCM. In a DCM-contaminated groundwater plume, the abundance of mec genes strongly correlated with DCM concentrations (R2 = 0.71 - 0.85) indicating their value as process-specific bioremediation biomarkers. mec gene clusters were identified in metagenomes representing peat bogs, the deep subsurface, and marine ecosystems including oxygen minimum zones (OMZs), suggesting DCM turnover in diverse habitats. The broad distribution of anaerobic DCM catabolic potential suggests a relevant control function for emissions to the atmosphere, and a role for DCM as a microbial energy source in critical zone environments. The findings imply that the global DCM flux might be far greater than emission measurements suggest. ImportanceDichloromethane (DCM) is an increasing threat to stratospheric ozone with both anthropogenic and natural emission sources. Anaerobic bacterial metabolism of DCM has not yet been taken into consideration as a factor in the global DCM cycle. The discovery of the mec gene cassette associated with anaerobic bacterial DCM metabolism and its widespread distribution in environmental systems highlight a strong attenuation potential for DCM. Knowledge of the mec cassette offers new opportunities to delineate DCM sources, enables more robust estimates of DCM fluxes, supports refined DCM emission modeling and simulation of the stratospheric ozone layer, reveals a novel, ubiquitous C1 carbon metabolic system, and provides prognostic and diagnostic tools supporting bioremediation of groundwater aquifers impacted by DCM.

microbiology↗

bioRxiv2021

Whole genome 6-methyladenosine sequencing (6-mA-Seq) enables bacterial epigenomics studies.

Methylation sequencing using bisulfite treatment has revolutionized the field of molecular biology for eukaryotic systems, unveiling levels of intricacy in regulation of gene expression in response to different environmental conditions. While bacteria also utilize methylation to regulate gene expression, bisulfite sequencing does not work as well in bacteria as in eukaryotes, because bacteria methylate adenosine instead of cytosine. Therefore, global bacterial methylation patterns cannot be studied using common Illumina sequencers. In this work, we demonstrate 6mA-seq, a method that can be used to identify patterns in bacteria that methylate adenosines at GATC sites. Furthermore, this method was used on Escherichia coli cultured on four different carbon sources to demonstrate different methylation patterns due to carbon utilization. 6mA-seq can increase the speed in which epigenetic research is conducted in bacteria.

genomics↗