Abstract | ||
---|---|---|
PathBank (www.pathbank.org) is a new, comprehensive, visually rich pathway database containing more than 110 000 machine-readable pathways found in 10 model organisms ( Homo sapiens, Bos taurus, Rattus norvegicus, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Saccharomyces cere-visiae, Escherichia coli and Pseudomonas aerugi-nosa). PathBank aims to provide a pathway for every protein and a map for every metabolite. This resource is designed specifically to support pathway elucidation and pathway discovery in transcrip-tomics, proteomics, metabolomics and systems biology. It provides detailed, fully searchable, hyper-linked diagrams of metabolic, metabolite signaling, protein signaling, disease, drug and physiological pathways. All PathBank pathways include information on the relevant organs, organelles, subcellular compartments, cofactors, molecular locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to the rich data contained in public chemical databases such as HMDB or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All PathBank pathways are accompanied with references and detailed descriptions which provide an overview of the pathway, condition or processes depicted in each diagram. Every PathBank pathway is downloadable in several machine-readable and image formats including BioPAX, SBML, PWML, SBGN, RXN, PNG and SVG. PathBank also supports community annotations and submissions through the web-based Path-Whiz pathway illustrator. The vast majority of Path-Bank's pathways (>95%) are not found in any other public pathway database. |
Year | DOI | Venue |
---|---|---|
2020 | 10.1093/nar/gkz861 | NUCLEIC ACIDS RESEARCH |
DocType | Volume | Issue |
Journal | 48 | D1 |
ISSN | Citations | PageRank |
0305-1048 | 1 | 0.34 |
References | Authors | |
0 | 17 |
Name | Order | Citations | PageRank |
---|---|---|---|
David S. Wishart | 1 | 2586 | 179.98 |
Carin Li | 2 | 91 | 4.07 |
Ana Marcu | 3 | 102 | 5.78 |
Hasan Badran | 4 | 16 | 1.34 |
Allison Pon | 5 | 508 | 25.07 |
Zachary Budinski | 6 | 1 | 0.68 |
Jonas Patron | 7 | 1 | 0.34 |
Debra Lipton | 8 | 1 | 0.34 |
Xuan Cao | 9 | 1 | 1.02 |
Eponine Oler | 10 | 1 | 0.68 |
Krissa Li | 11 | 1 | 0.34 |
Maïlys Paccoud | 12 | 1 | 0.34 |
Chelsea Hong | 13 | 1 | 0.34 |
An Chi Guo | 14 | 96 | 4.29 |
Christopher Chan | 15 | 1 | 0.34 |
William Wei | 16 | 1 | 0.34 |
Miguel Ramirez-Gaona | 17 | 1 | 0.34 |