Biology subjects

Rutherford, E.

Publications and source records attributed to Rutherford, E..

3 recordsLinked to original sources

bioRxiv2026

A collaborative submission model for building high-quality data resources at scale through partnership

Community data resources that aggregate datasets across studies are critical infrastructure for modern biomedical research, accelerating discovery as rapidly advancing Artificial Intelligence (AI) models place ever-greater demand on large, well-described data corpora. However, building these resources involves a fundamental tension: comprehensive metadata is imperative for reuse, yet capturing this information systematically requires substantial effort that limits growth. We detail how CZ CELLxGENE Discover has used the collaborative submission model - where data contributors partner with dedicated resource curators - to become a large, widely used single-cell genomics resource for diverse analytical applications including AI model development and integrative analysis. This partnership leverages contributors intimate study knowledge and curators focus on data reuse and expertise in standardization to improve data quality, metadata accuracy, and richness. This is achieved by motivating researcher participation through tangible benefits while minimizing submission burden. We contrast this collaborative model with contributor-driven and resource-driven approaches, highlighting tradeoffs in scalability, consistency across submissions, and quality assurance. The principles and practices we describe provide a framework for building high-quality community data resources across diverse biological data types.

genetics↗

bioRxiv2023

CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

cell biology↗

bioRxiv2022

Topic modeling for multi-omic integration in the human gut microbiome and implications for autism

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial pro-cesses, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

bioinformatics↗