Dataset Information

GExplore: a web server for integrated queries of protein domains, gene expression and mutant phenotypes.

ABSTRACT:

Background

The majority of the genes even in well-studied multi-cellular model organisms have not been functionally characterized yet. Mining the numerous genome wide data sets related to protein function to retrieve potential candidate genes for a particular biological process remains a challenge.

Description

GExplore has been developed to provide a user-friendly database interface for data mining at the gene expression/protein function level to help in hypothesis development and experiment design. It supports combinatorial searches for proteins with certain domains, tissue- or developmental stage-specific expression patterns, and mutant phenotypes. GExplore operates on a stand-alone database and has fast response times, which is essential for exploratory searches. The interface is not only user-friendly, but also modular so that it accommodates additional data sets in the future.

Conclusion

GExplore is an online database for quick mining of data related to gene and protein function, providing a multi-gene display of data sets related to the domain composition of proteins as well as expression and phenotype data. GExplore is publicly available at: http://genome.sfu.ca/gexplore/

SUBMITTER: Hutter H

PROVIDER: S-EPMC2779824 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GExplore: a web server for integrated queries of protein domains, gene expression and mutant phenotypes.

Hutter Harald H Ng Man-Ping MP Chen Nansheng N

BMC genomics 20091116

<h4>Background</h4>The majority of the genes even in well-studied multi-cellular model organisms have not been functionally characterized yet. Mining the numerous genome wide data sets related to protein function to retrieve potential candidate genes for a particular biological process remains a challenge.<h4>Description</h4>GExplore has been developed to provide a user-friendly database interface for data mining at the gene expression/protein function level to help in hypothesis development and ...[more]

PMID: 19917126

Similar Datasets

Project description:BackgroundLegumes (Leguminosae or Fabaceae) play a major role in agriculture. Transcriptomics studies in the model legume species, Medicago truncatula, are instrumental in helping to formulate hypotheses about the role of legume genes. With the rapid growth of publically available Affymetrix GeneChip Medicago Genome Array GeneChip data from a great range of tissues, cell types, growth conditions, and stress treatments, the legume research community desires an effective bioinformatics system to aid efforts to interpret the Medicago genome through functional genomics. We developed the Medicago truncatula Gene Expression Atlas (MtGEA) web server for this purpose.DescriptionThe Medicago truncatula Gene Expression Atlas (MtGEA) web server is a centralized platform for analyzing the Medicago transcriptome. Currently, the web server hosts gene expression data from 156 Affymetrix GeneChip(R) Medicago genome arrays in 64 different experiments, covering a broad range of developmental and environmental conditions. The server enables flexible, multifaceted analyses of transcript data and provides a range of additional information about genes, including different types of annotation and links to the genome sequence, which help users formulate hypotheses about gene function. Transcript data can be accessed using Affymetrix probe identification number, DNA sequence, gene name, functional description in natural language, GO and KEGG annotation terms, and InterPro domain number. Transcripts can also be discovered through co-expression or differential expression analysis. Flexible tools to select a subset of experiments and to visualize and compare expression profiles of multiple genes have been implemented. Data can be downloaded, in part or full, in a tabular form compatible with common analytical and visualization software. The web server will be updated on a regular basis to incorporate new gene expression data and genome annotation, and is accessible at: http://bioinfo.noble.org/gene-atlas/.ConclusionsThe MtGEA web server has a well managed rich data set, and offers data retrieval and analysis tools provided in the web platform. It's proven to be a powerful resource for plant biologists to effectively and efficiently identify Medicago transcripts of interest from a multitude of aspects, formulate hypothesis about gene function, and overall interpret the Medicago genome from a systematic point of view.

Project description:BACKGROUND: Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server. RESULTS: In seeking more powerful descriptors, the profile-profile alignment score generated from the COMPASS algorithm was first considered as a new descriptor (i.e., PPA). When considering a profile-profile alignment between two proteins in the context of fold recognition, one protein is regarded as a template (i.e., its 3D structure is known). Instead of a sequence profile derived from a Psi-blast search, a structure-seeded profile for the template protein was generated by searching its structural neighbors with the assistance of the TM-align structural alignment algorithm. Moreover, the COMPASS algorithm was used again to derive a profile-structural-profile-alignment-based descriptor (i.e., PSPA). We trained and tested the new DescFold in a total of 1,835 highly diverse proteins extracted from the SCOP 1.73 version. When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset. CONCLUSIONS: The new DescFold method was intensively benchmarked to have very competitive performance compared with some well-established fold recognition methods, suggesting that it can serve as a useful tool to assist in template-based protein structure prediction. The DescFold server is freely accessible at http://202.112.170.199/DescFold/index.html.

Project description:BACKGROUND: We are witnessing rapid progress in the development of methodologies for building the combinatorial gene regulatory networks involving both TFs (Transcription Factors) and miRNAs (microRNAs). There are a few tools available to do these jobs but most of them are not easy to use and not accessible online. A web server is especially needed in order to allow users to upload experimental expression datasets and build combinatorial regulatory networks corresponding to their particular contexts. METHODS: In this work, we compiled putative TF-gene, miRNA-gene and TF-miRNA regulatory relationships from forward-engineering pipelines and curated them as built-in data libraries. We streamlined the R codes of our two separate forward-and-reverse engineering algorithms for combinatorial gene regulatory network construction and formalized them as two major functional modules. As a result, we released the cGRNB (combinatorial Gene Regulatory Networks Builder): a web server for constructing combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. The cGRNB enables two major network-building modules, one for MPGE (miRNA-perturbed gene expression) datasets and the other for parallel miRNA/mRNA expression datasets. A miRNA-centered two-layer combinatorial regulatory cascade is the output of the first module and a comprehensive genome-wide network involving all three types of combinatorial regulations (TF-gene, TF-miRNA, and miRNA-gene) are the output of the second module. CONCLUSIONS: In this article we propose cGRNB, a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. Since parallel miRNA/mRNA expression datasets are rapidly accumulated by the advance of next-generation sequencing techniques, cGRNB will be very useful tool for researchers to build combinatorial gene regulatory networks based on expression datasets. The cGRNB web-server is free and available online at http://www.scbit.org/cgrnb.

Dataset Information

GExplore: a web server for integrated queries of protein domains, gene expression and mutant phenotypes.

Background

Description

Conclusion

Publications

GExplore: a web server for integrated queries of protein domains, gene expression and mutant phenotypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets