Integrated Genomics ERGO

Category Cross-Omics>Pathway Analysis/Gene Regulatory Networks/Tools

Abstract The ERGO bioinformatics suite is a tool designed for comprehensive genome analysis. ERGO integrates data from every level including genomic, biochemical data, literature, and high-throughput analysis into a comprehensive user friendly network of metabolic and non-metabolic pathways.

This network is then used to feedback and improve ERGO’s functional assignments. Using a cross genome approach, ERGO extracts information from the latest sequence data found in over 2,200 genomes from both eukaryotes and prokaryotes.

In contrast to conventional systems, ERGO takes into account similarity, genomic clustering profiles, chromosomal neighborhoods, expression data, and functional subsystems prior to assigning function to an Open Reading Frame (ORF).

Examining individual ORFs as well as entire genomes from multiple perspectives such as this allows investigators to see ‘connections’ and valuable information that is often missing when analyzing things from only one perspective.

The cyclical nature of the integration of this new information continually elevates our knowledge and understanding of the complex dynamics of living organisms.

Products features/capabilities are as follows:

ERGO is different from other systems --

While conventional systems annotate genes based principally on the results of similarity searches:

1) ERGO moves functional annotation to a higher level of accuracy by integrating multiple levels of information.

2) Through the use of both proprietary tools and publicly available resources, a researcher is able to analyze genes (or proteins) in a variety of contexts including but Not limited to genomic clustering, chromosomal neighborhoods, expression profiles, and functional subsystems.

3) Due to its diversity of data, pathways and analytical tools, ERGO is able to predict proteins that are functionally linked in a variety of situations such as metabolic pathways, signaling pathways, protein trafficking, and/or structural complexes.

4) Information is consolidated into an organism-specific profile.

5) ERGO is supported by a dynamic, ever changing database that strengthens with the introduction of every new genome.

6) Each new genome increases the knowledge in 3 dimensions: through the introduction of new proteins, new pathways, and new patterns. This new knowledge is then spread vertically across and throughout the database thereby strengthening the entire system.

A user can do the following with ERGO --

1) Determine the function of a given sequence, Expressed Sequence Tag (EST) or gene and integrate it into appropriate pathways.

2) Investigate DNA sequences, proteins and/or pathways that are common to groups of user defined organisms.

3) Characterize ORFs and ESTs using proprietary tools such as functional coupling, pinned regions, and preserved operons.

4) Characterize proteins using public tools such as Protein families’ database (Pfam), Clusters of Orthologous Groups of proteins (COGS), Basic Local Alignment Search Tool (BLAST), and Position-Specific Iterated (PSI)-BLAST.

5) Compare individual annotations with public databases such as EcoGENE, Subtilist, Genequiz, DAtA (Database of Arabidopsis thaliana Annotation), TIGR, trEMBL, and Swiss-PROT.

6) Update functional assignments.

7) Refine nomenclature of genes, proteins, and pathways.

8) Analyze metabolic and non-metabolic models of various organisms.

9) Identify unique proteins relative to over 2,200 other genomes.

10) Analyze whole genomes between user defined subsets of genomes from over 2,200 organisms.

11) Analyze microarray data in the context of a gene, pathway, subsystem, functional network, and the whole genome.

12) Analyze genome-wide knock out data in the context of a gene, pathway, and functional network.

ERGO can be used by pharmaceutical, agricultural and other industrial companies --

ERGO can be used to:

1) Develop prioritized lists of metabolic and non-metabolic drug targets through effective data mining.

2) Develop a comprehensive evaluation of potential drug targets prior to evaluation through comparative analysis of functional networks.

3) Decrease laboratory costs and increase productivity through use of integrated data analysis prior to bench analysis.

4) Decrease valuable time-consuming analytical efforts through an integrated, easy-to-use workbench of tools.

5) Increase Intellectual Property (IP) potential through the discovery of novel enzymes, proteins, and pathways.

6) Increase production yields through knowledge-driven metabolic engineering.

7) Increase understanding and interpretation of high throughput analysis through its data integration into functional networks.

ERGO can be used to identify proteins missing from various biochemical pathways --

ERGO's strength is its ability to identify missing proteins involved in biochemical pathways. Since ERGO integrates both genomic and pathway data from over 2,200 different organisms investigators see patterns of proteins sticking together that function in the same pathway.

ERGO's proprietary tools visualize these patterns Not only locally within a 20 kb region but also across the entire genome to enable investigators to see extra proteins. Many times these extra proteins are of unknown function and are even misannotated.

Further analysis reveals that they have characteristics you would expect to see in the missing enzymes and/or proteins of a pathway thereby allowing the investigator to hypothesize on the function of these proteins.

It is significant to note that the number of patterns increases significantly with the introduction of each new genome into the database.

ERGO can also be used to predict functions in eukaryotes --

One of the strongest aspects of ERGO is its diverse database encompassing genomes from all domains of life (eukaryotes, archaea and bacteria).

Due to its integration of biochemical pathways with genomic, microarray and wet lab data, ERGO visualizes chromosomal, biochemical, structural, or regulatory patterns Not seen when viewed from only one perspective.

These patterns help to figure out the missing pieces that cannot be identified with conventional similarity searches. Quite often the patterns are Not present in the organism of most interest to the investigator but a distant relative either in the same domain or across domains.

ERGO takes advantage of this information and extrapolates it to genomes that do Not exhibit the pattern, most notably the eukaryotes.

This approach has been very effective in filling in missing pieces of metabolism in some of the eukaryotes since many metabolic pathways are shared between prokaryotes and eukaryotes.

System Requirements

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Manufacturer

Manufacturer Web Site Integrated Genomics ERGO

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G6G Abstract Number 20097R

G6G Manufacturer Number 101459