ResNet Mammalian Database
ResNet Mammalian includes pathways and molecular interactions uniquely representing the entire PubMed (14,000,000 abstracts) and 47 full text journals. It contains information and knowledge on over 1.25 million relations for human, rat and mouse, linked to their original literature sources. Click here for detailed Statistics.
Features
- Over 1.25 million relations for:
- 106,749 proteins
- 47,852 small molecules
- 1094 cell processes
- 2,301 diseases
- Pathways:
- 192 manually curated pathways, a sampling of these is available in our Pathway Collection
- 555 Signaling Line Pathways automatically generated as the most likely regulatory path from receptor to transcription factor
- Ontologies:
- Gene Ontology
- Ariadne protein function ontology
- Annotations:
- Entrez Gene annotations for proteins
- PubChem annotations for small molecules
- MedScan dictionary annotations for cell processes and diseases
CANONICAL PATHWAYS
Canonical pathways are manually curated by Ariadne Scientists based on review articles. All canonical pathways are editable and can be easily updated, expanded, and arranged according to cellular localization with the images provided in Pathway Studio or imported by user. A sampling is available in our Pathway Collection.
Signaling line pathways
555 “Signaling Line Pathways” have been manually built using ResNet Mammalian data. Each constructed “signaling line" contains exactly one receptor and one transcription factor, a line of intracellular effectors transmitting the signal, and a set of ligands activating the receptor. For this purpose, all proteins have been classified into the following five groups:
- Extracellular ligands
- Receptors
- Transcription factors
- Nuclear receptors
- Effectors (all others)
The resulting 555 “Signaling Line Pathways” (SLP) connect 165 individual receptors to 88 individual transcription factors and represent a set of pathways indispensable for interpretation of gene expression experiments. The SLP data set now allow any set of genes differentially expressed under specific experimental conditions to be connected to the most appropriate transcription factors involved in their transcriptional regulation, and further to possible cellular receptors and extracellular signaling molecules (ligands), thus providing an enhanced means for analyzing and deciphering the complexity of microarray gene expression data.
