Article

GATExplorer: genomic and transcriptomic explorer; mapping expression probes to gene loci, transcripts, exons and ncRNAs.

Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CiC-IBMCC, CSIC/USAL), Salamanca, Spain.
BMC Bioinformatics (Impact Factor: 2.67). 01/2010; 11:221. DOI: 10.1186/1471-2105-11-221
Source: PubMed

ABSTRACT Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. However, expression signals are typically calculated using the assignment of "probesets" to genes, without addressing the problem of "gene" definition or proper consideration of the location of the measuring probes in the context of the currently known genomes and transcriptomes. Moreover, as our knowledge of metazoan genomes improves, the number of both protein-coding and noncoding genes, as well as their associated isoforms, continues to increase. Consequently, there is a need for new databases that combine genomic and transcriptomic information and provide updated mapping of expression probes to current genomic annotations.
GATExplorer (Genomic and Transcriptomic Explorer) is a database and web platform that integrates a gene loci browser with nucleotide level mappings of oligo probes from expression microarrays. It allows interactive exploration of gene loci, transcripts and exons of human, mouse and rat genomes, and shows the specific location of all mappable Affymetrix microarray probes and their respective expression levels in a broad set of biological samples. The web site allows visualization of probes in their genomic context together with any associated protein-coding or noncoding transcripts. In the case of all-exon arrays, this provides a means by which the expression of the individual exons within a gene can be compared, thereby facilitating the identification and analysis of alternatively spliced exons. The application integrates data from four major source databases: Ensembl, RNAdb, Affymetrix and GeneAtlas; and it provides the users with a series of files and packages (R CDFs) to analyze particular query expression datasets. The maps cover both the widely used Affymetrix GeneChip microarrays based on 3' expression (e.g. human HG U133 series) and the all-exon expression microarrays (Gene 1.0 and Exon 1.0).
GATExplorer is an integrated database that combines genomic/transcriptomic visualization with nucleotide-level probe mapping. By considering expression at the nucleotide level rather than the gene level, it shows that the arrays detect expression signals from entities that most researchers do not contemplate or discriminate. This approach provides the means to undertake a higher resolution analysis of microarray data and potentially extract considerably more detailed and biologically accurate information from existing and future microarray experiments.

1 Bookmark
 · 
133 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Long non-coding ribonucleic acids (lncRNAs) have been proposed as biomarkers in prostate cancer. This paper proposes a selection method which uses data from tiled microarrays to identify relatively long regions of moderate expression independent of the microarray platform and probe design. The method is used to search for candidate long non-coding ribonucleic acids (lncRNAs) at locus 8q24 and is run on three independent experiments which all use samples from prostate cancer patients. The robustness of the method is tested by utilizing repeated copies of tiled probes. The method shows high consistency between experiments that used the same samples, but different probe layout. There also is statistically significant consistency when comparing experiments with different samples. The method selected the long non-coding ribonucleic acid PCNCR1 in all three experiments.
    PLoS ONE 01/2014; 9(6):e99899. · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Colorectal cancer (CRC) is a heterogeneous disease in terms of clinical behavior and response to therapy. Increasing evidence suggests that long noncoding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, and some of them have been implicated in CRC biogenesis and prognosis. Using an lncRNA-mining approach, we constructed lncRNAs expression profiles in approximately 888 CRC samples. By applying unsupervised consensus clustering to LncRNA expression profiles, we identified five distinct molecular subtypes of CRC with different biological pathways and phenotypically distinct in their clinical outcome in both univariate and multivariate analysis. The prognostic significance of the lncRNA-based classifier was confirmed in independent patient cohorts. Further analysis revealed that most of the signature lncRNAs positively correlated with somatic copy number alterations (SCNAs). This lncRNAs-based classification schema thus provides a molecular classification applicable to individual tumors that has implications to influence treatment decisions.
    Molecular Oncology. 01/2014;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Many long non-coding RNAs(lncRNAs) have been found to be a good marker for several tumors. Using lncRNA-mining approach, we aimed to identify lncRNA expression signature that can predict breast cancer patient survival.Methods We performed LncRNA expression profiling in 887 breast cancer patients from Gene Expression Omnibus (GEO) datasets. The association between lncRNA signature and clinical survival was analyzed using the training set(n = 327,from GSE 20685). The validation for the association was performed in another three independent testing sets(252 from GSE21653, 204 from GSE12276, and 104 from GSE42568).ResultsA set of four lncRNA genes (U79277, AK024118, BC040204, AK000974) have been identified by the random survival forest algorithm. Using a risk score based on the expression signature of these lncRNAs, we separated the patients into low-risk and high-risk groups with significantly different survival times in the training set. This signature was validated in the other three cohorts. Further study revealed that the four-lncRNA expression signature was independent of age and subtype. Gene Set Enrichment Analysis (GSEA) suggested that gene sets were involved in several cancer metastasis related pathways.Conclusions These findings indicate that lncRNAs may be implicated in breast cancer pathogenesis. The four-lncRNA signature may have clinical implications in the selection of high-risk patients for adjuvant therapy.
    Journal of experimental & clinical cancer research : CR. 10/2014; 33(1):84.

Full-text (3 Sources)

Download
44 Downloads
Available from
May 23, 2014