Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Muskan Kapoor ¹, Enrique Sapena Ventura ², Amy Walsh ³

¹Department of Animal Science, Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, United States.
²European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, Cambridgeshire, United Kingdom.
³Animal Science Research Center, Division of Animal Science and Division of Plant Science and Technology, University of Missouri-Columbia, Columbia, MO, United States.
⁴Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States.
⁵Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada.
⁶Lawrence Berkeley National Laboratory, DOE-Joint Genome Institute, Berkeley, CA, United States.
⁷Plant Science and Technology, University of Missouri, Columbia, MO, United States.
⁸The Broad Institute of MIT and Harvard, Data Sciences Platform, Cambridge, MA, United States.
⁹Division of Animal Science, University of Missouri-Columbia, Columbia, MO, United States.
¹⁰Earlham Institute, Norwich Research Park, Norwich, United Kingdom.
¹¹Medical School, University of East Anglia, Norwich Research Park, Norwich, United Kingdom.
¹²U.S. Department of Agriculture, Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY, United States.

⁰Department of Animal Science, Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, United States.

Frontiers in Genetics +

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December 16, 2024

View abstract on PubMed

Summary

This study integrates livestock single-cell RNA sequencing data into the Terra platform using FAANG standards. This enables advanced analysis and visualization, promoting FAIR data principles in agricultural genomics.

Area Of Science

Agricultural Genomics
Bioinformatics
Single-cell Genomics

Background

Existing agricultural genomics data submission standards are underdeveloped for single-cell (SC) data.
Human genomics infrastructure (Human Cell Atlas Data Portal, Terra) and EMBL-EBI's Single Cell Expression Atlas offer advanced capabilities.
The FAANG data portal lacks computational links to these resources, hindering agricultural genomics research.

Purpose Of The Study

To assess the feasibility of using FAANG metadata standards for ingesting livestock scRNA-seq data into Terra.
To develop and extend data tools for seamless integration and analysis of agricultural SC data.
To establish a FAIR data resource for agricultural species genomics.

Main Methods

Leveraged Human Cell Atlas Data Portal and Terra platform.
Utilized EMBL-EBI's Single Cell Expression Atlas for data ingestion.
Adapted FAANG metadata standards for scRNA-seq data.
Developed semi-automated metadata brokering and data transfer workflows.
Verified data ingestion into Terra and performed scRNA-seq analysis on porcine data.
Created prototype visualization tools using JBrowse.

Main Results

Demonstrated successful ingestion of scRNA-seq datasets into Terra using FAANG standards.
Implemented semi-automated metadata brokering for scRNA-seq data, reducing expert curation needs.
Validated data transfer from HCA Data Portal to Terra for analysis.
Executed a workflow for analyzing the first ingested porcine scRNA-seq dataset.
Developed JBrowse visualization tracks for comparing scRNA-seq and bulk RNA-seq data.

Conclusions

FAANG metadata standards can be adapted for ingesting livestock scRNA-seq data into Terra.
Developed tools facilitate data sharing, analysis, and visualization for agricultural genomics.
The project lays the foundation for a FAIR data ecosystem for agricultural species genomics.

Keywords:

FAANG FAIR HCA data portal data ingestion portal data resource and analysis data submission standards metadata single-cell RNA-seq

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