Mikaela Cashman

Microbial carbon utilisation shapes ecological niches, community assembly, and nutrient cycling, yet remains difficult to predict from genomes despite well-characterised pathways. Genome-based machine-learning models often generalise poorly across datasets, a limitation commonly attributed to small or taxonomically biased training sets. We integrated binary growth phenotypes for 819 strains across 240 carbon sources from four datasets, focusing on 15 shared sources. Cross-dataset evaluation reduced balanced accuracy from 0.86 (within datasets) to 0.62, and restricting evaluation to in-clade relatives recovered only 0.03 of this loss. Because phylogeny alone could not explain the transfer gap, we restricted training to samples with coherent genotype-phenotype relationships, defined by agreement between experimental labels and annotated pathways. Concordance filtering doubled the number of carbon sources with reproducible genomic signals across datasets (from 6 to 12 of 15), often recovering known pathway genes, while matched random subsets remained at the unfiltered level. The smaller concordant set also approached the cross-dataset performance of models augmented with up to 1800 BacDive genomes per phenotype, demonstrating greater efficiency per sample. Concordance-trained models remained bounded specialists, but separated growers from non-growers among genomes the pathway rules called negative, mostly generalists with broad-specificity transporters. Model confidence made these bounds actionable, since abstention improved accuracy where models transferred and retraining on newly labelled low-confidence genomes gave the largest gains for weaker phenotypes. Collectively, these findings recast generalisation as a problem of label-mechanism agreement and demonstrate how genome-based predictions can be deployed within explicit applicability domains and improved through targeted phenotyping.

Constructing multi-step bioinformatics workflows, from read quality control through genome assembly to functional annotation, requires expertise in both biology and computational tool selection, creating a bottleneck for scalable and reproducible analysis. We present the KBase Research Agent, a multi-agent system for automating such workflows within the DOE Systems Biology Knowledgebase (KBase). Given a set of sequencing reads and a research objective, the agent constructs an analysis plan grounded in KBase documentation and a Knowledge Graph (KG) of the KBase application catalog, then selects, parameterizes, validates and executes appropriate KBase applications to carry out the workflow. The resulting analysis is preserved as a reproducible KBase Narrative. We evaluate the system's planning and execution quality against ground truth constructed from reference workflows derived from peer-reviewed Microbiology Resource Announcements. We further apply the agent to 100 previously unanalyzed bacterial isolate genomes from the JGI IMG/M database, where it autonomously performed read quality control, genome assembly, taxonomic classification with GTDB-Tk, and downstream analysis producing annotated genomes, reproducible Narratives, and draft manuscripts without human intervention. Across these experiments, the KBase Research Agent demonstrates the feasibility of domain-grounded, end-to-end scientific workflow automation in a production bioinformatics platform.

We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

While the proliferation of data-driven omics technologies has continued to accelerate, methods of identifying relationships among large-scale changes from omics experiments have stagnated. It is therefore imperative to develop methods that can identify key mechanisms among one or more omics experiments in order to advance biological discovery. To solve this problem, here we describe the network-based algorithm MENTOR - Multiplex Embedding of Networks for Team-Based Omics Research. We demonstrate MENTOR’s utility as a supervised learning approach to successfully partition a gene set containing multiple ontological functions into their respective functions. Subsequently, we used MENTOR as an unsupervised learning approach to identify important biological functions pertaining to the host genetic architectures in Populus trichocarpa associated with microbial abundance of multiple taxa. Moreover, as open source software designed with scientific teams in mind, we demonstrate the ability to use the output of MENTOR to facilitate distributed interpretation of omics experiments.

Leveraging the use of multiplex multi-omic networks, key insights into genetic and epigenetic mechanisms supporting biofuel production have been uncovered. Here, we introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelisation on high performance cluster systems, which enables high throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

Opioid addiction constitutes a public health crisis in the United States and opioids cause the most drug overdose deaths in Americans. Yet, opioid addiction treatments have limited efficacy. To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies (GWAS) of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex (dlPFC) in opioid overdose victims. We identified 211 highly interrelated genes identified by GWAS or dysregulation in the dlPFC of individuals with opioid overdose victims that implicated the Akt, BDNF, and ERK pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. This included drugs used to treat other substance use disorders and antidepressant drugs. Our synthesis of multi-omics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.

In this work we identify changes in high-resolution zones across the globe linked by environmental similarity that have implications for agriculture, bioenergy, and zoonosis. We refine exhaustive vector comparison methods with improved similarity metrics as well as provide multiple methods of amalgamation across 744 months of climatic data. The results of the vector comparison are captured as networks which are analyzed using static and longitudinal comparison methods to reveal locations around the globe experiencing dramatic changes in abiotic stress. Specifically we (i) incorporate updated similarity scores and provide a comparison between similarity metrics, (ii) implement a new feature for resource optimization, (iii) compare an agglomerative view to a longitudinal view, (iv) compare across 2-way and 3-way vector comparisons, (v) implement a new form of analysis, and (vi) demonstrate biological applications and discuss implications across a diverse set of species distributions by detecting changes that affect their habitats. Species of interest are related to agriculture (e.g., coffee, wine, chocolate), bioenergy (e.g., poplar, switchgrass, pennycress), as well as those living in zones of concern for zoonotic spillover that may lead to pandemics (e.g., eucalyptus, flying foxes).

Current methods for defining SARS-CoV-2 lineages ignore the vast majority of the SARS-CoV-2 genome. We develop and apply an exhaustive vector comparison method that directly compares all known SARS-CoV-2 genome sequences to produce novel lineage classifications. We utilize data-driven models that (i) accurately capture the complex interactions across the set of all known SARS-CoV-2 genomes, (ii) scale to leadership-class computing systems, and (iii) enable tracking how such strains evolve geospatially over time. We show that during the height of the original Omicron surge, countries across Europe, Asia, and the Americas had a spatially asynchronous distribution of Omicron sub-strains. Moreover, neighboring countries were often dominated by either different clusters of the same variant or different variants altogether throughout the pandemic. Analyses of this kind may suggest a different pattern of epidemiological risk than was understood from conventional data, as well as produce actionable insights and transform our ability to prepare for and respond to current and future biological threats.