The alliance of genome resources: transforming comparative genomics

Abstract

Comparing genomic and biological characteristics across multiple species is essential to using model systems to investigate the molecular and cellular mechanisms underlying human biology and disease and to translate mechanistic insights from studies in model organisms for clinical applications. Building a scalable knowledge commons platform that supports cross-species comparison of rich, expertly curated knowledge regarding gene function, phenotype, and disease associations available for model organisms and humans is the primary mission of the Alliance of Genome Resources (the Alliance). The Alliance is a consortium of seven model organism knowledgebases (mouse, rat, yeast, nematode, zebrafish, frog, fruit fly) and the Gene Ontology resource. The Alliance uses a common set of gene ortholog assertions as the basis for comparing biological annotations across the organisms represented in the Alliance. The major types of knowledge associated with genes that are represented in the Alliance database currently include gene function, phenotypic alleles and variants, human disease associations, pathways, gene expression, and both protein–protein and genetic interactions. The Alliance has enhanced the ability of researchers to easily compare biological annotations for common data types across model organisms and human through the implementation of shared programmatic access mechanisms, data-specific web pages with a unified “look and feel”, and interactive user interfaces specifically designed to support comparative biology. The modular infrastructure developed by the Alliance allows the resource to serve as an extensible “knowledge commons” capable of expanding to accommodate additional model organisms.

Characterization of the Pristionchus pacificus “epigenetic toolkit” reveals the evolutionary loss of the histone methyltransferase complex PRC2

“…2018 to predict genes in P. pacificus and C. elegans with putative epigenetic function. As input, we used the proteomes of 17 species, including six well-studied model organisms ( C. elegans , Drosophila melanogaster , Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae , Schyzosaccharomyces pombe) ( Davis et al 2022 ; Bult and Sternberg 2023 ), two emerging model systems for developmental plasticity ( Acyrthosiphon pisum, Apis mellifera ), six additional nematodes ( P. pacificus , Pristionchus exspectatus , Pristionchus mayeri , Brugia malayi , Strongyloides ratti , Trichinella spiralis ), plus three additional species—an animal ( Danio rerio ), fungus ( Leptosphaeria maculans), and plant ( Selaginella moellendorffii)— included to help improve phylogenetic resolution. Proteome sources and accession numbers are indicated in Supplementary Table 1 ( Athanasouli et al .…”

Genes in Humans and Mice: Insights from Deep learning of 777K Bulk Transcriptomes

“…For Alliance disease association analysis, we obtained the annotation dataset from the Alliance of Genome Resources website, Version: 6.0.0 (https://www.alliancegenome.org/downloads) 44 . Specifically, we used two compressed tab-separated values (tsv) files: ‘DISEASE-ALLIANCE_HUMAN.tsv.gz’ for human genes and ‘DISEASE-ALLIANCE_MGI.tsv.gz’ for mouse genes.…”

Differences in Protein Capture by SP3 and SP4 Demonstrate Mechanistic Insights of Proteomics Clean-up Techniques

“…The count was set to 50 and ease to 0.05 with a Benjamini correction applied to the options tab. Cellular components output from DAVID were manually grouped into cytoplasmic, membranous, nuclear, cytoskeletal, or nonspecific categories using Uniprot subcellular localization, 5 Alliance of Genome Resources (v.5.4.0), 6 and the Gene Ontology Resource (Release 2023-06-11 http://geneontology.org/). 7, 8 Nonspecific-cellular components include ‘cell’, ‘organelle’, or any cellular component that could not fit in one of the other categories.…”