Biology subjects

Neely, C. J.

Publications and source records attributed to Neely, C. J..

4 recordsLinked to original sources

bioRxiv2026

KBase Research Agent: Automated Multi-Agent Workflow Construction for Reproducible Genome Analysis

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 systems 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.

bioinformatics↗

bioRxiv2021

The high-throughput gene prediction of more than 1,700 eukaryote genomes using the software package EukMetaSanity

Gene prediction and annotation for eukaryotic genomes is challenging with large data demands and complex computational requirements. For most eukaryotes, genomes are recovered from specific target taxa. However, it is now feasible to reconstruct or sequence hundreds of metagenome-assembled genomes (MAGs) or single-amplified genomes directly from the environment. To meet this forth-coming wave of eukaryotic genome generation, we introduce EukMetaSanity, which combines state-of-the-art tools into three pipelines that have been specifically designed for extensive parallelization on high-performance computing infrastructure. EukMetaSanity performs an automated taxonomy search against a protein database of 1,482 species to identify phylogenetically compatible proteins to be used in downstream gene prediction. We present the results for intron, exon, and gene locus prediction for 112 genomes collected from NCBI, including fungi, plants, and animals, along with 1,669 MAGs and demonstrate that EukMetaSanity can provide reliable preliminary gene predictions for a single target taxon or at scale for hundreds of MAGs. EukMetaSanity is freely available at https://github.com/cjneely10/EukMetaSanity.

bioinformatics↗

bioRxiv2021

Eukaryotic genomes from a global metagenomic dataset illuminate trophic modes and biogeography of ocean plankton

Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are not designed to consider eukaryotes, nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome assembled genomes (MAGs) from large-scale metagenomic datasets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8-2000{micro}m) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally-relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand the known marine phylogenetic diversity through the increase in coverage of likely particle- and host-associated taxa. We also demonstrate an approach to infer the putative functional mode of the recovered eukaryotic MAGs. A global survey of the TOPAZ MAGs enabled the identification of ecological cohorts, driven by specific environmental factors, and putative host-microbe associations. ImportanceDespite the ecological importance of single-celled eukaryotic organisms in marine environments, the majority are difficult to cultivate in the lab. Sequencing genetic material extracted from environmental samples enables researchers to document naturally-occurring protistan communities. However, conventional sequencing methodologies cannot separate out the genomes of individual organisms. To more completely capture the entire genomic content of mixed protistan community, we can create bins of sequences that represent the same organism. We developed a pipeline that enables scientists to bin individual organisms out of metagenomic reads, and show results that provide exciting insights into what protistan communities are present in the ocean and what roles they play in the ecosystem. Here, a global survey of both eukaryotic and prokaryotic MAGs enabled the identification of ecological cohorts, driven by specific environmental factors, and putative host-microbe associations. Accessible and scalable computational tools, such as EukHeist, are likely to accelerate the identification of meaningful genetic signatures from large datasets, ultimately expanding the eukaryotic tree of life.

microbiology↗

bioRxiv2019

MetaSanity: An integrated, customizable microbial genome evaluation and annotation pipeline

SummaryAs the importance of microbiome research continues to become more prevalent and essential to understanding a wide variety of ecosystems (e.g., marine, built, host-associated, etc.), there is a need for researchers to be able to perform highly reproducible and quality analysis of microbial genomes. MetaSanity incorporates analyses from eleven existing and widely used genome evaluation and annotation suites into a single, distributable workflow, thereby decreasing the workload of microbiologists by allowing for a flexible, expansive data analysis pipeline. MetaSanity has been designed to provide separate, reproducible workflows, that (1) can determine the overall quality of a microbial genome, while providing a putative phylogenetic assignment, and (2) can assign structural and functional gene annotations with varying degrees of specificity to suit the needs of the researcher. The software suite combines the results from several tools to provide broad insights into overall metabolic function and putative extracellular localization of peptidases and carbohydrate-active enzymes. Importantly, this software provides built-in optimization for \"big data\" analysis by storing all relevant outputs in an SQL database, allowing users to query all the results for the elements that will most impact their research.\n\nAvailability and implementationMetaSanity is provided under the GNU General Public License v.3.0 and is available for download at https://github.com/cjneely10/MetaSanity. This application is distributed as a Docker image. MetaSanity is implemented in Python3/Cython and C++.\n\nSupplementary informationSupplementary data are available below.

bioinformatics↗