Autism Spectrum Disorders study

MST

Autism Spectrum disorders (ASD) still do not have unifying etiology. Current studies are showing that mechanisms of ASD are linked to a complex of interconnected epigenetic and genetic factors that change the dynamics of organism development. Our group together with Dr. Tsygelny from UCSD has been involved in the studies of such inter-players responsible for ASD- a pair of proteins neuroligin-neurexin and others.

In this Driving Project genome-wide association studies of more than 400 unrelated ASD patients showing changes in various regions of human chromosomes related to ASD was used to analyze hierarchical clusters of small number of ASD related genes and transcription factors working on each stage of brain development. It was shown that the genetic controls of brain development are significantly different from such controls in other organs development.

Thus, elucidation of possible mechanisms of ASD with recommendation for cures has to be addressed using a multi-level strategy that will show corresponding changes in parameters on different levels during the brain development.

Projects

NF-kB regulation and signaling

DEMO_nfkb_m

NF-kB plays critical roles in the development of the immune system, in the inflammatory and immune response, and in controlling cell survival and proliferation. As such, it has broad and critical roles in multiple aspects of carcinogenesis and cancer pathogenesis, in determining chemoresistance and cancer progression. Read More

Transcriptional Regulation Database

Experimental data on gene transcriptional regulation are distributed throughout many and various databases and datasets. Currently, no resource exists that would automatically integrate these data and provide a one-stop shop experience for the user seeking for retrieving experimental and predicted information on transcription factors and gene regulatory regions — information essential for deciphering and modeling gene regulatory networks. Read More

How to become a Driving Project

Driving Projects are research projects that are selected for their scientific merit in answering important biological questions and that represent a broad range of research endeavors, advancing their disciplines. Driving Projects stimulate the BiologicalNetworks project to improve its technologies and provide feedback on our work. The list our current driving projects is here.

If you would like to become a Driving Project please fill in the form below, carefully explaining the details of your project in the Message field. What kind of analysis you are interested in and what kind of data you are dealing with.

Thank you.

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* Field Required

Contacts, Help, Feature Requests, Licensing

If you need help in using BiologicalNetworks platform or IntegromeDB database or would like to get in touch with someone from BiologicalNetworks-IntegromeDB team please use the form below . To make sure your questions are dealt with as quickly as possible please fill the Message field carefully explaining the details of your problem or request.

You can contact BiologicalNetworks-IntegromeDB project PI Michael Baitaluk directly at: baitaluk at-symbol sdsc dot-symbol edu.

 

We also recommend you to read:

 

 

 

Thank you.

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API XML-RPC

API XML-RPC

Common Data Types

Common Structures

Integrome DB functions

Keyword search

find list of objects by keyword(s)

find object(s) by keyword, organism

find object(s) by keyword, organism, type

find publication(s)by keyword(s)

Curated pathways search

list gene names by KEGG pathway

list gene names by KEGG map and organism

list gene objects by KEGG pathway

list gene objects by KEGG map and organism

find KEGG pathways by keyword(s)

find other curated pathways by keyword(s)

Object functions

list top properties for object

list all properties for object

list top synonyms for object

list all synonyms for object

find neighbors (objects) by object

list experiments for object

find co-expressed objects for object

list annotated terms by object and ontology

Two and more objects functions

find direct interactions between two objects

find direct interactions inside group of objects

find direct interactions between two groups of objects

find shortest path between two objects

find shortest path inside group of objects

find shortest path between two groups of objects

find neighbors for group of objects

list experiments by group of objects

find experiments by two co-expressed objects

find experiments by group of objects where co-expressed

Sequence DB functions

list chromosomes for organism name

get resources by organism name and chromosome name

get resource by resource_id

get sequence by resource, start, end

find features by keyword(s)

find features corresponding to integrome DB object

list feature types for resource

get all features by resource[, start, end[ and type]]

list of all features inside or overlapping interval of given feature[of specified type]

Java integration example

Source code

Common Data Types

  • array — Array of values, storing no keys
  • string — string of characters, must follow XML encoding
  • struct — associative array
  • base64 — base64 encoded binary data
  • boolean — logical value (0 or 1)
  • date/time — Date and time in ISO 8601 format
  • double — double precision floating point number
  • integer — Whole number, integer
  • nil — discriminative null value

Common Structures

  1. struct object (

string id

string name

string type

)

 

  1. struct publication (

string publication_id

string contents

)

 

  1. struct pathway (

string pathway_id

string pathway_name

string pathway_type

)

 

  1. struct property (

string name

string value

)

 

  1. struct experiment (

string id

string name

)

 

  1. struct edge (

struct object start

struct object connector

struct object end

)

 

  1. struct seq_resource (

string resource_id

string resource_name

string resource_length

)

 

  1. struct seq_feature (

string feature_id

string feature_name

string feature_type

string resource_id

string start

string end

)

 

All structures described above are represented by HashMap container. Please use property name as a key to access value in map for clients written in Java.

Integrome DB functions

Keyword search

find list of objects by keyword(s)

This function retrieves all objects (e.g. proteins, genes, small molecules, experiments, etc.) for the set of terms/keywords. Terms/keywords can be gene/protein names (e.g. “p53”) as well as general terms (e.g. “apoptosis”)

Method signature

array Integrome.find ( string keywords )

Parameters

string keywords – comma separated list of keywords

Result

array of objects

find object(s) by keyword, organism

This function retrieves all objects (e.g. proteins, genes, small molecules, experiments, etc.) for the set of terms/keywords in particular organism. Terms/keywords can be gene/protein names (e.g. “p53”) as well as general terms (e.g. “apoptosis”)

Method signature

array Integrome.find ( string keywords, string organism_name )

Parameters

string keywords – comma separated list of keywords

string organism_name – canonical organism name, nil value removes this filter,

Result

array of objects

find object(s) by keyword, organism, type

This function retrieves all objects of a specified type (e.g. ‘proteins’) only for the set of terms/keywords in particular organism(s). Terms/keywords can be gene/protein names (e.g. “p53”) as well as general terms (e.g. “apoptosis”)

Method signature

array Integrome.find ( string keywords, string organism_name, string object_type )

Parameters

string keywords – comma separated list of keywords

string organism_name – canonical organism name, nil value removes this filter,

string object_type – type (from predefined list) , nil value removes this filter

Result

array of objects

find publication(s) by keyword(s)

This function retrieves all publications for the set of terms/keywords. Terms/keywords can be gene/protein names (e.g. “p53”) as well as general terms (e.g. “apoptosis”)

Method signature

array Integrome.find _publication ( string keywords )

Parameters

string keywords – comma separated list of keywords

Result

array of publications

Canonical pathways search

list gene names by KEGG pathway

This function returns a list of genes that are containing in a specified KEGG pathway. KEGG pathway is specified by KEGG pathway ID (e.g. “hsa05213”)

Method signature

array Integrome.get_genes_by_pathway ( string pathway )

Parameters

string pathway pathway name

Result

string array of gene names

list gene names by KEGG map and organism

This function returns a list of genes that are containing in a specified KEGG pathway. KEGG pathway is specified by KEGG pathway map ID (e.g. “map00010”) and organism (e.g. “homo sapiens”)

Method signature

array Integrome.get_genes_by_pathway ( string keggmap, string organism )

Parameters

string keggmap kegg map name (like map00010)

string organism – canonical organism name

Result

string array of gene names

list gene objects by KEGG pathway

This function returns a list of gene objects that are containing in a specified KEGG pathway. KEGG pathway is specified by KEGG pathway ID (e.g. “hsa05213”)

Method signature

array Integrome.get_genes_by_pathway ( string pathway )

Parameters

string pathway pathway name

Result

array of objects

list gene objects by KEGG map and organism

This function returns a list of gene objects that are containing in a specified KEGG pathway. KEGG pathway is specified by KEGG pathway map ID (e.g. “map00010”) and organism (e.g. “homo sapiens”)

Method signature

array Integrome.get_genes_by_pathway ( string keggmap, string organism )

Parameters

string keggmap kegg map name (like map00010)

string organism – canonical organism name

Result

array of objects

find KEGG pathways by keyword(s)

This function returns a list of KEGG pathways that are most relevant to provided keywords/terms. The keywords can be gene names as well as general keywords from pathways description (e.g. “apoptosis”)

Method signature

array Integrome.find _kegg_pathways ( string keywords )

Parameters

string keywords – comma separated list of keywords

Result

array of pathways

find other canonical pathways by keyword(s)

This function returns a list of canonical pathways from NCI/Nature, Reactome, Wiki, Imaging pathways databases that are most relevant to provided keywords/terms.
The keywords can be gene names as well as general keywords from pathways description (e.g. “apoptosis”)

Method signature

array Integrome.find _curated_pathways ( string keywords, string type )

Parameters

string keywords – comma separated list of keywords

string type – type (from the list of NCU/Nature, Reactome, Wiki, Imaging pathways), nil value removes this filter

Result

array of pathways

Object functions

list top properties for object

This function returns most popular properties for an object (gene, protein, drug, etc.). Most popular properties are defined as properties most widely used in public databases (e.g. Description, Function, Synonyms)

Method signature

array Integrome.get_top_properties( string object_id)

Parameters

string object_id object id

Result

array of properties

list all properties for object

This function returns ALL properties for an object (gene, protein, drug, etc.).

Method signature

array Integrome.get_all_properties( string object_id)

Parameters

string object_id object id

Result

array of properties

list top synonyms for object

This function returns most popular synonyms for an object (gene, protein, drug, etc.). Most popular synonyms are defined as synonyms most widely used in public databases (e.g. “TP53” for a “p53”)

Method signature

array Integrome.get_top_synonyms ( string object_id)

Parameters

string object_id object id

Result

array of string

list all synonyms for object

This function returns ALL synonyms for an object (gene, protein, drug, etc.).

Method signature

array Integrome.get_all_synonyms ( string object_id)

Parameters

string object_id object id

Result

array of string

find neighbors (objects) for an object

This functions returns a list of objects (genes, proteins, small molecules, etc.) that have at least one relation (e.g. protein-protein interaction, co-citation, genetic interaction) with a given object.

Method signature

array Integrome.find_neighbors ( string object_id)

Parameters

string object_id object id

Result

array of objects

list of experiments for an object

This function returns a list of experiments (e.g. microarray gene expression) that contain a given object (gene, protein, metabolite, etc.)

Method signature

array Integrome.find_experiments ( string object_id)

Parameters

string object_id object id

Result

array of experiments

find co-expressed objects for an object

This function returns “co-expressed” (or correlating in time) genes, proteins, metaboites retrieved from integrated gene expression, proteomics and metabolomics experiments for an object (gene, protein, metabolite)

Method signature

array Integrome.find_coexpressed_objects ( string object_id)

Parameters

string object_id object id

Result

array of objects

list annotated ontology terms for an object

This function returns a list of GeneOnotlogy annotations for a gene/protein

Method signature

array Integrome.get_terms( string object_id)

Parameters

string object_id object id

Result

array of string

Two and more objects functions

find direct interactions between two objects

This function returns a list of direct interactions (protein-protein, protein-dna, genetic, etc) for two objects (gene, protein, small molecule, etc.)

Method signature

array Integrome.find_direct_interactions (string object_1_id, string object_2_id)

Parameters

string object_1_id first object id

string object_2_id second object id

Result

array of edges

find direct interactions inside group of objects

This function returns a list of direct interactions (protein-protein, protein-dna, genetic, etc) for a group (>2) of objects (gene, protein, small molecule, etc.)

Method signature

array Integrome.find_direct_interactions (array string object_ids)

Parameters

array string object_ids first group of object ids

Result

array of edges

find direct interactions between two groups of objects

This function returns a list of direct interactions (protein-protein, protein-dna, genetic, etc) for two groups of objects (gene, protein, small molecule, etc.)

Method signature

array Integrome.find_direct_interactions (array string object_1_ids, array string object_2_ids)

Parameters

array string object_1_ids first group of object ids

array string object_2_ids second group of object ids

Result

array of edges

find shortest path between two objects

This function finds shortest interaction path between two objects (genes, proteins, small molecules, etc.)

Method signature

array Integrome.find_direct_interactions (string object_1_id, string object_2_id)

Parameters

string object_1_id first object id

string object_2_id second object id

Result

array of edges

find shortest paths inside group of objects

This function finds shortest interaction paths among group (>2) of objects (genes, proteins, small molecules, etc.)

Method signature

array Integrome.find_direct_interactions (array string object_ids)

Parameters

array string object_ids first group of object ids

Result

array of edges

find shortest paths between two groups of objects

This function finds shortest interaction path between two groups of objects (genes, proteins, small molecules, etc.)

Method signature

array Integrome.find_direct_interactions (array string object_1_ids, array string object_2_ids)

Parameters

array string object_1_ids first group of object ids

array string object_2_ids second group of object ids

Result

array of edges

find neighbors for group of objects

This function finds objects (genes, proteins, small molecules, etc.) that have at least one relation/interaction (e.g. protein-protein interaction, co-citation) with a group of objects

Method signature

array Integrome.find_direct_interactions (array string object_ids)

Parameters

array string object_ids first group of object ids

Result

array of edges

list experiments by group of objects

This function returns a list of experiments (e.g. gene expression, metabolomics) that contain at least one object from a group of objects

Method signature

array Integrome.find_coexpressed_objects (array string object_ids)

Parameters

array string object_ids group of object ids

Result

array of experiments

find experiments by two co-expressed objects

This function returns a list of experiments (e.g. gene expression, metabolomics) in which given objects are coexpressed (corellating in time)

Method signature

array Integrome.find_experiments_of_coexpressed_objects (string object_1_id, string object_2_id)

Parameters

string object_1_id first object id

string object_2_id second object id

Result

array of experiments

find experiments by group of objects where co-expressed

This function returns a list of experiments (e.g. gene expression, metabolomics) that contain at least one pair of co-expressed (coreelating in time values) objects from a group of objects

Method signature

array Integrome.find_coexpressed_objects (array string object_ids)

Parameters

array string object_ids group of object ids

Result

array of experiments

Sequence DB functions

list chromosomes for organism name

This function returns a list of chromosomes for an organism (e.g. “homo sapiens”)

Method signature

array sequence.list_chromosomes(string organism_name)

Parameters

string organism_name canonical organism name

Result

array of strings

get resources by organism name and chromosome name

This function returns a list of resources (e.g. “Selera”, “NCBI, “Ensembl”) for an organism (e.g. “homo sapiens”) and chromosome (e.g. “chr15”)

Method signature

array sequence.find_resources(string organism_name, string chromosome)

Parameters

string organism_name canonical organism name

string chromosome canonical organism name, nil value removes the filter

Result

array of resources

get resource by resource_id

This function returns a list of resources (e.g. “Selera”, “NCBI, “Ensembl”) by resource id

Method signature

struct resource sequence.get_resources(string resource_id)

Parameters

string resource_id sequence db internal resource identifier

Result

struct resource

get sequence by resource, start, end

This function returns sequence for resource (specific chromosome in a specific genome) specified by ID by Start and End positions

Method signature

string sequence.get_sequence(string resource_id, int start, int end)

Parameters

string resource_id sequence resource id

int start start of region

int end end of region

Result

string

find features by keyword(s)

This function for a list of keywords such as gene names, as well as general keyword (e.g. “apoptosis”) returns a list of genomic sequence features (start/end position, promoter position, regulatory region/binding sites, and others) for returned genes

Method signature

array sequence.find_features(string keywords, string type)

Parameters

string keywords comma separated list of keywords

string feature_type feature type, nil value to disable this filter

Result

array of features

find features corresponding to integrome DB object

This function for an ObjectID of the IntegromeDB object (e.g. gene) returns a list of genomic sequence features (start/end position, promoter position, regulatory region/binding sites, and others) for returned genes

Method signature

array sequence.find_corresponding_features(string object_id)

Parameters

string object_id object id from Integrome DB

Result

array of features

list feature types for resource

This function for a resource id (providing chromosome id in a organism) returns a list of genomic sequence features (start/end position, promoter position, regulatory region/binding sites, and others) for returned genes

Method signature

array sequence.list_feature_types(string resource_id)

Parameters

string resource_id sequence resource id

Result

array of string

get all features by resource[start, end and type]

This function for a resource id (providing chromosome id in a organism) and start, end positions of a genomic region returns a list of specified regions (e.g. binding sites) that are inside [start, end] region

Method signature

array sequence.list_features(string resource_id, int start, int end, string feature_type)

Parameters

string resource_id sequence resource id

int start start of region

int end end of region

string feature_type feature type, nil value to disable this filter

Result

array of features

list of all features inside or overlapping interval of given feature [of specified type]

This function for a resource id (providing chromosome id in a organism) and start, end positions of a genomic region returns a list of specified regions (e.g. binding sites) that are inside or overlapping with the [start, end] region

Method signature

array sequence.list_intersected_features(string feature_id, string feature_type, boolean inside_only)

Parameters

string resource_id sequence resource id

string feature_type feature type, nil value to disable this filter

boolean inside_only– return only containing features if true, overlapping otherwise

Result

array of features

 

 

Java integration example

This Java source code example shows the usage of find objects by keyword and organism and then list top synonyms for each object found. In order to let this example work you should have apache xml-rpc libraries linked to your project and available for download from http://ws.apache.org/xmlrpc/.

Source code

package xmlrpc_client;

import java.net.MalformedURLException;
import java.net.URL;
import java.util.Arrays;
import java.util.HashMap;
import java.util.logging.Level;
import java.util.logging.Logger;
import org.apache.xmlrpc.XmlRpcException;
import org.apache.xmlrpc.client.XmlRpcClient;
import org.apache.xmlrpc.client.XmlRpcClientConfigImpl;
import org.apache.xmlrpc.client.XmlRpcCommonsTransportFactory;

/**
* Test class to find objects by keyword and organism and then
* list top synonyms for each of them
* @author Sergey Kozhenkov
*/
public class Main {
public static void main(String[] args) {
try {
XmlRpcClientConfigImpl config = new XmlRpcClientConfigImpl();
config.setServerURL(new URL(“http://integromedb.org/integrome”));
config.setEnabledForExtensions(true);
XmlRpcClient client = new XmlRpcClient();
// use Commons HttpClient as transport
client.setTransportFactory(new XmlRpcCommonsTransportFactory(client));
// set configuration
client.setConfig(config);

Object[] params = new Object[]{“p53”, “Homo sapiens”};
Object[] result = (Object[]) client.execute(“Integrome.find”, params);
if (result != null) {
for (Object rec : result) {
HashMap<String, String> map = (HashMap<String, String>) rec;
long id = Long.parseLong(map.get(“id”));
System.out.println(“Object: ” + map.get(“name”) + ” (” + id + “), type: ” + map.get(“type”) + “, organism: ” + map.get(“organism”));

params = new Object[]{“” + id};
Object[] synonyms = (Object[]) client.execute(“Integrome.get_top_synonyms”, params);
if (synonyms != null) {
System.out.println(Arrays.toString(synonyms));
}
}
}
catch (MalformedURLException ex) {
Logger.getLogger(Main.class.getName()).log(Level.SEVERE, null, ex);
catch (XmlRpcException ex) {
Logger.getLogger(Main.class.getName()).log(Level.SEVERE, null, ex);
}
}
}

BiologicalNetworks integration with external site

Download as Microsoft word document

Description

To establish integration with BiologicalNetwork you must put special link(s) on your web page(s) with all the required parameters according to this document. Also BiologicalNetwork must be installed on client’s machine in order to launch required routine and show the result inside application.

 

Navigating to this link will initiate special text file downloading with all required parameters. Downloading procedure brings system “Save & open dialog” with associated BiologicalNetwork application to handle this file type.

Functions

Show Interactions for object

  • Predefined link parameters

open – value “pathway

type – value “interactions

  • Variable link parameters

string – object name

  • Example: show interactions for P53 protein.

http://integromedb.org/params2xml?open=pathway&type=interactions&string=p53

Show Interactions for object with specified organism

  • Predefined link parameters

open – value “pathway

type – value “interactions

  • Variable link parameters

string – object name

organism – canonical organism name, all spaces encoded as “%20”

  • Example: show interactions for P53 protein.

http://integromedb.org/params2xml?open=pathway&type=interactions&string=p53&organism=Homo%20sapiens

Show Multi Experiment View for object

  • Predefined link parameters

open – value “mev

  • Variable link parameters

string – object name

  • Example: show multi-experiment viewer for P53 protein.

http://integromedb.org/params2xml?open=mev&string=p53

Show Multi Experiment View for object with specified organism

  • Predefined link parameters

open – value “mev

  • Variable link parameters

string – object name

organism – canonical organism name, all spaces encoded as “%20”

  • Example: show multi-experiment viewer for P53 protein.

http://integromedb.org/params2xml?open=mev&string=p53&organism=Homo%20sapiens

Show Sequence View for object

  • Predefined link parameters

open – value “sequence

type – value “sequencedb

  • Variable link parameters

string – object name

  • Example: show sequence for P53 protein.

http://integromedb.org/params2xml?open=sequence&string=p53

Show Sequence View for object in with specified organism

  • Predefined link parameters

open – value “sequence

type – value “sequencedb

  • Variable link parameters

string – object name

organism – canonical organism name, all spaces encoded as “%20”

  • Example: show sequence for P53 protein.

http://integromedb.org/params2xml?open=sequence&string=p53&organism=Homo%20sapiens

 

Microbial metabolism

DEMO_NucleotideMetabolism_m

Several structural genomics groups from NIH sponsored Protein Structure Initiative (PSI), including the Joint Center for Structural Genomics (JCSG) from UCSD and Burnham Institute for Medical Research has compiled a large protein structure dataset, which was constructed very carefully and selectively; that is, the dataset contains only experimentally determined structures of proteins from one specific organism, the hyperthermophilic bacterium Thermotoga maritima, and those of close homologs from mesophilic bacteria. In contrast to the conclusions of previous studies, the analyses show that oligomerization order, hydrogen bonds, and secondary structure play minor roles in adaptation to hyperthermophily in bacteria. On the other hand, the data exhibit very significant increases in the density of salt-bridges and in compactness for proteins from T. maritima. The latter effect can be measured by contact order or solvent accessibility, and network analysis shows a specific increase in highly connected residues in this thermophile. These features account for changes in 96% of the protein pairs studied. The results provide a clear picture of protein thermostability in one species, and a framework for future studies of thermal adaptation.

Adam Godzik’s lab at the Burnaham Institute of Medical Research is currently constructing a “big picture” of metabolic processes in Thermatoga and in othe bacterial genomes and assimilating protein architectural information flowing from Structural Genomics (SG) efforts. protein architectural information flowing from SG has not been assimilated into mainstream research as rapidly and as widely as that generated by traditional structural biology. We believe the reason for this unanticipated situation is that, unlike traditional structural biology, structure determination at SG centers is inevitably not always – nor even routinely – accompanied by a local stream of connected, synergistic biochemical and biological research. Consequently, the vast majority of protein structures determined by SG centers lack these complementary details and are not described in high impact, peer-reviewed manuscripts, the principal way by which scientists communicate. Instead, the end result of the work of a SG center is usually a set of coordinates deposited in the PDB, information that is not readily assimilated by a typical biologist and opportunities are likely often missed since the scientific application is not recognized. As a result, data from structural genomics is only very slowly absorbed into the wider research stream, largely as correlated experimental data arises.

BiologicalNetworks v. 2.0 betta

BiologicalNetworks v. 2.0 betta

01 April 2010

  • Several additional functions and search options introduced into BuildPathwayWizard for extensive network navigation.

 

  • In addition to GeneOntology annotations multiple additional ontology annotations were added:
    • Diseases
    • Cell Types
    • Tissues
    • Mammal Phenotypes
    • Human Anatomy
    • Mouse Anatomy
    • KEGG Pathways
    • Chemicals

Read More

PathSys

The PathSys System

PathSys is a graph-based system for creating a combined database of biological pathways, gene regulatory networks and protein interaction maps.

PathSys is a general-purpose, scalable warehouse of biological information, complete with a graph manipulation and a query language, a storage mechanism and a generic data-importing mechanism through schema-mapping.

In PathSys are integrated over 14 curated and publicly contributed data sources for the 8 representative organisms (see list below), as well as Gene Ontology, which is structured as an acyclic graph.

The organisms are:
  • Budding Yeast (Saccharomices cerevisiae)
  • Schizosaccharomyces pombe
  • Fly (Drosophila melanogaster)
  • Caenorhabditis elegans
  • Arabidopsis thaliana
  • Mouse (Mus musculis)
  • Human (Homo sapience)
  • Zebrafish (Danio rerio)
The data sources are:
  • Co-immunoprecipitation data (Gavin, A. C. et al. (2002) “Functional organization of the yeast proteome by systematic analysis of protein complexes”. Nature, 415, 141147.)
  • Co-immunoprecipitation data (Ho, Y. et al. (2002) “Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry”. Nature, 415, 180183.)
  • Yeast-two hybrid data (Ito, T. et al. (2001) “A comprehensive two-hybrid analysis to explore the yeast protein interactome”. Proc Natl Acad Sci U S A. 98, 4569-74 . )
  • Yeast-two hybrid data (Uetz, P. et al. (2000) “A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae”. Nature 403,623-7.)
  • Genetic interaction data (Tong, A. H. Y. et al. (2001) “Systematic genetic analysis with ordered arrays of yeast deletion mutants”. Science, 294, 23642368.)
  • Genetic interaction data (Tong, A.H. et al. “Global mapping of the yeast genetic interaction network”. Science 303, 808-13 (2004).)
  • MIT data http://web.wi.mit.edu/young/regulatory_network/, (Lee, T. et al. (2002) “Transcriptional regulatory networks in Saccharomyces cerevisiae”. Science, 298, 799804.)
  • UCSF localization data (Huh, W.K. et al. (2003), “Global analysis of protein localization in budding yeast”. Nature 425,686-91.)
  • MIPS data (http://mips.gsf.de/genre/proj/yeast/)
  • CSH data (http://rulai.cshl.edu/SCPD/)
  • Transfac (http://www.gene-regulation.com/)
  • BIND database (http://bind.ca/)
  • pre-BIND database (http://www.blueprint.org/products/prebind/)
  • KEGG (http://www.genome.ad.jp/kegg)