title: Genic insights from integrated human proteomics in GeneCards.
abstract: GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite's next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein-RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome. Database URL:http://www.genecards.org/.
authors.affiliation: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel simon.fishilevich@weizmann.ac.il.
authors.forename: Simon
authors.initials: S
authors.lastname: Fishilevich
authors.affiliation: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
authors.forename: Shahar
authors.initials: S
authors.lastname: Zimmerman
authors.affiliation: LifeMap Sciences Ltd., Tel Aviv 69710, Israel.
authors.forename: Asher
authors.initials: A
authors.lastname: Kohn
authors.affiliation: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
authors.forename: Tsippi
authors.initials: T
authors.lastname: Iny Stein
authors.affiliation: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
authors.forename: Tsviya
authors.initials: T
authors.lastname: Olender
authors.affiliation: CDO Analytics, Seattle Children's Hospital, Seattle, WA 98101 USA Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, WA 98101 USA Data-Enabled Life Sciences Alliance (DELSA), Seattle, Washington, 98101, USA Departments of Biomedical Informatics and Medical Education and Pediatrics, University of Washington School of Medicine, Seattle, WA 98109, USA Department of Chemistry and Chemical Biology, Northeastern University College of Science, Boston, MA 02115 USA.
authors.forename: Eugene
authors.initials: E
authors.lastname: Kolker
authors.affiliation: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
authors.forename: Marilyn
authors.initials: M
authors.lastname: Safran
authors.affiliation: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
authors.forename: Doron
authors.initials: D
authors.lastname: Lancet
mesh_terms: D016000:Cluster Analysis; D057225:Data Mining; D030562:Databases, Protein; D005796:Genes; D006801:Humans; D025341:Principal Component Analysis; D020543:Proteome / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D012313:RNA / Q000378:metabolism
chemical_list: D020543:Proteome; D012313:RNA