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Project Summary

Economic challenges imposed by climate change and disease on the aquaculture industry necessitate advances for improved animal welfare and resiliency. Biomarkers associated with environmental and disease resilience traits can be leveraged in breeding and management strategies. However, their discovery has been limited in part by the complexity of molecular systems and the cost of genomics tools used to understand them. Advances in computational approaches including machine learning algorithms, together with the wealth of genomic data that has amassed, enable powerful meta-analyses for improved biomarker discovery in aquaculture species.

This project aims to advance the discovery and characterization of biomarkers through mining publicly available shellfish genomic datasets from resilient populations.

The objectives are to:

1) Develop standardized open-access, user-friendly, reproducible bioinformatics pipelines for resilience biomarker discovery through systematic reanalysis, data integration and meta-analysis

2) Build a user-friendly open-access comprehensive database of candidate resilience biomarkers that is widely available for use by the aquaculture community.

The resulting database will enable improved molecular tool development for more efficient phenotype selection and health monitoring, implementation of selection methods that use a systems biology approach for simultaneous improvement of multiple traits, and ultimately increased animal fitness and improved animal welfare.

View more project details here.

a) Graphical summary of Objective 1. b) Example pipelines and software for systematic reanalysis and data integration. c) Example of resilience biomarker database (modeled after Tamborero et al. 201846).

Omics datasets processed

Species Data Type stress class stressor Phenotype Phenotpe Summary Reference DOI SRA links meta data file counts table DEG data
C. gigas T environment thermal thermotolerance different among lines resilience Arredondo-Espinoza et al. 2023 https://doi.org/10.1016/j.cbd.2023.101089 https://www.ncbi.nlm.nih.gov/biosample?LinkName=bioproject_biosample_all&from_uid=516210 SraRunTable.csv salmon.merged.gene_counts.tsv  
C. virginica T disease Perkinsus infection tolerance resilience Proestou et al. 2023 https://doi.org/10.3389/fgene.2023.1054558 https://www.ncbi.nlm.nih.gov/Traces/study/?query_key=5&WebEnv=MCID_679bbe17db915954f5c764fa&o=acc_s%3Aa SraRunTable.csv salmon.merged.gene_counts.tsv  
C. virginica E,T disease Perkinsus infected sensitivity Johnson et al. 2020 https://doi.org/10.3389/fmars.2020.00598 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP246310&o=acc_s%3Aa SraRunTable(1).csv salmon.merged.gene_counts.tsv  
C. gigas & virginica T disease Perkinsus infection tolerance resilience Chan et al. 2021 10.3389/fgene.2021.795706 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SAMN11031730&o=acc_s%3Aa SraRunTable(2).csv salmon.merged.gene_counts.tsv *Cgigas data here  
C. virginica T disease Perkinsus infection sensitivity Sullivan and Proestou 2021 https://doi.org/10.1016/j.aquaculture.2021.736831 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP301630&o=acc_s%3Aa SraRunTable(3).csv salmon.merged.gene_counts.tsv  
C. virginica T disease Perkinsus infection sensitivity Proestou and Sullivan 2020 https://doi.org/10.1016/j.fsi.2019.12.001 https://www.ncbi.nlm.nih.gov/Traces/study/?query_key=4&WebEnv=MCID_679bbe17db915954f5c764fa&o=acc_s%3Aa SraRunTable(4).csv salmon.merged.gene_counts.tsv  
C. virginica E environment OA   resilience Roberts et al. unpublished https://github.com/sr320/ceasmallr https://gannet.fish.washington.edu/seashell/bu-github/ceasmallr/data/ L18_larvae_meta.csv bismark.cov  

Data set analysis request form

If you have a data set you would like to see included in an omics meta-analysis, please Click here to sumbit your ideas.

Biomarker Database

ID Gene Species Condition Trait Effect Class Evidence Source Rank
BM001HSP70Crassostrea gigasThermal StressThermotoleranceUpregulatedGenomic FeatureSpecies-specific Arredondo-Espinoza et al. 20233
BM002HSP90Crassostrea gigasThermal StressThermotoleranceUpregulatedGenomic FeatureCross-species Arredondo-Espinoza et al. 20234
BM004SOD1Crassostrea virginicaPerkinsus marinus InfectionInfection ToleranceUpregulatedGenomic FeatureCross-species Proestou et al. 2023; Chan et al. 20215
BM006IL-17Crassostrea gigasPerkinsus marinus InfectionInfection ToleranceUpregulatedProtein Interaction NetworkCross-species Chan et al. 20214
BM014CLECCrassostrea virginicaPerkinsus marinus InfectionInfection ToleranceUpregulatedProtein Interaction NetworkCross-species Chan et al. 2021; Proestou et al. 20235

View Full Database →

People

Project Director:
Shelly Wanamaker, PhD (she/her)
Research Scientist
shelly.wanamaker@gmgi.org
https://github.com/shellywanamaker

Project Personnel:
Emma Strand, PhD (she/her)
Postdoctoral Scientist
emma.strand@gmgi.org
https://github.com/emmastrand

Steve Yost (he/him)
Bioinformatics Software Engineer
steve.yost+gmgi@gmail.com https://github.com/journeymansix

Funding:
This project is supported by the USDA Agriculture and Food Research Initiative Animal Health and Production and Animal Products program under the Animal Breeding, Genetics, and Genomics section award number 2024-67015-41794 to Dr. Shelly A. Wanamaker.