The structure and behavior of many biological (and other) systems
can be effectively represented as networks. We have several projects
related to this topic. Below, we provide links to the various software
developed in our group for declaring, visualizing, and analyzing
networks. We also provide access to our databases containing network
representations of several systems. Finally, links to our
network-related publications are provided.
|Software and Servers
Yale Network Analyzer is our primary networks software. It can be used
to manage networks in several formats, calculate various statistical
properties, perform network operations, and interact with networks
visually. tYNA can be used online, downloaded, and also connects to Cytoscape.
flexible system for visualizing literature-derived networks. Input a
PubMed query and PubNet outputs a network showing a variety of
relationships, such as the degree to which two authors collaborate or
the MeSH Term relatedness of publications with PDB id's.
collection of scripts and binaries for predicting protein-protein
interactions. The central idea is to find defective cliques (nearly
complete complexes of pairwise interacting proteins), and predict the
interactions that complete them.
lightweight semantic web data warehouse for integrating RDF-formatted
yeast data. Register datasets and then peform queries through
predefined templates or compose one manually.
|Databases and Datasets
|ENCODE human regulatory network
we provide the human transcriptional regulatory network constructed from
over 400 ChIP-Seq experiments (~120 transcription factors).
|Comparing Linux call graph and E. coli regulatory network
||The networks of E. coli transcriptional regulatory network and the call graph of the Linux operating system.
we provide a dataset to relate the usage of particular pathways and
subnetworks in recent metagenomics studies to environmental features.
We have shown such changes may reflect the adaptation of microbial
communities to these different conditions -i.e., how network dynamics
relates to environmental features.
Interaction Network. In contrast to the conventional nodes-and-edge
view of networks, we provide an atomic resolution view, making
extensive use of 3D protein structures and homology mapping. Our
network reveals many hitherto unknown trends and evolutionary insight.
between transcription factors (TFs) and their target genes have an
extensive pyramid-shaped hierarchical structure. We provide results for
E. coli and S. cerevisiae.
approach for predicting protein-protein interactions genome-wide in
yeast. Our method integrates noisy, experimental interaction data sets,
and, at given levels of sensitivity, we observe that our predictions
are more accurate than the existing high-throughput experimental data
||2002, 2003, 2005
||Static network analysis has been used widely in biology. Sandy extends this with an approach to analyze network dynamics.
is valuable to map large-scale network information from one organism to
another using comparative genomics. We have assessed the degree to
which this can be done reliably as a function of sequence similarity.
Here we provide results using interaction information from C. elegans, D. melanogaster, and H. pylori.
||Using proteome chip technology, we describe the in vitro substrates recognized by most S. cerevisiae
protein kinases. Over 4,000 phosphorylation events involving 1,325
different proteins are identified. The results are assembled into a
first-generation phosphorylation map.
predicted map for the transcriptional regulatory network in yeast is
presented. It is based on reconstructions from expression correlations.
for a complete list of network-related papers published in our group.
Individual references to some of these have also been provided above
under the relevant software or database.