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<pubDate>Sat, 26 Jul 2008 03:12:34 BST</pubDate>


	<title>CiteULike: jyuh Dopazo</title>
	<description>CiteULike: jyuh Dopazo</description>


	<link>http://www.citeulike.org/user/jyuh/author/Dopazo</link>
	<dc:publisher>CiteULike.org</dc:publisher>
	<dc:language>en-gb</dc:language>
	<dc:rights>Copyright &#169; 2004-2008 citeulike.org</dc:rights>
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        <rdf:li rdf:resource="http://www.citeulike.org/user/jyuh/article/2857008"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/jyuh/article/2733895"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/jyuh/article/2535357"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/jyuh/article/2151781"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/jyuh/article/1938525"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/jyuh/article/1005973"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/jyuh/article/1449483"/>

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<item rdf:about="http://www.citeulike.org/user/jyuh/article/2857008">
    <title>GEPAS, a web-based tool for microarray data analysis and interpretation</title>
    <link>http://www.citeulike.org/user/jyuh/article/2857008</link>
    <description>&lt;i&gt;Nucl. Acids Res. (28 May 2008), gkn303.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org. 10.1093/nar/gkn303</description>
    <dc:title>GEPAS, a web-based tool for microarray data analysis and interpretation</dc:title>

    <dc:creator>Joaquin Tarraga</dc:creator>
    <dc:creator>Ignacio Medina</dc:creator>
    <dc:creator>Jose Carbonell</dc:creator>
    <dc:creator>Jaime Huerta-Cepas</dc:creator>
    <dc:creator>Pablo Minguez</dc:creator>
    <dc:creator>Eva Alloza</dc:creator>
    <dc:creator>Fatima Al-Shahrour</dc:creator>
    <dc:creator>Susana Vegas-Azcarate</dc:creator>
    <dc:creator>Stefan Goetz</dc:creator>
    <dc:creator>Pablo Escobar</dc:creator>
    <dc:creator>Francisco Garcia-Garcia</dc:creator>
    <dc:creator>Ana Conesa</dc:creator>
    <dc:creator>David Montaner</dc:creator>
    <dc:creator>Joaquin Dopazo</dc:creator>
    <dc:identifier>doi:10.1093/nar/gkn303</dc:identifier>
    <dc:source>Nucl. Acids Res. (28 May 2008), gkn303.</dc:source>
    <dc:date>2008-06-02T13:50:41-00:00</dc:date>
    <prism:publicationYear>2008</prism:publicationYear>
    <prism:publicationName>Nucl. Acids Res.</prism:publicationName>
    <prism:startingPage>gkn303</prism:startingPage>
    <prism:category>microarray</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/jyuh/article/2733895">
    <title>High-throughput functional annotation and data mining with the Blast2GO suite</title>
    <link>http://www.citeulike.org/user/jyuh/article/2733895</link>
    <description>&lt;i&gt;Nucl. Acids Res. (29 April 2008), gkn176.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data. 10.1093/nar/gkn176</description>
    <dc:title>High-throughput functional annotation and data mining with the Blast2GO suite</dc:title>

    <dc:creator>Stefan Gotz</dc:creator>
    <dc:creator>Juan Garcia-Gomez</dc:creator>
    <dc:creator>Javier Terol</dc:creator>
    <dc:creator>Tim Williams</dc:creator>
    <dc:creator>Shivashankar Nagaraj</dc:creator>
    <dc:creator>Maria Nueda</dc:creator>
    <dc:creator>Montserrat Robles</dc:creator>
    <dc:creator>Manuel Talon</dc:creator>
    <dc:creator>Joaquin Dopazo</dc:creator>
    <dc:creator>Ana Conesa</dc:creator>
    <dc:identifier>doi:10.1093/nar/gkn176</dc:identifier>
    <dc:source>Nucl. Acids Res. (29 April 2008), gkn176.</dc:source>
    <dc:date>2008-04-29T11:39:18-00:00</dc:date>
    <prism:publicationYear>2008</prism:publicationYear>
    <prism:publicationName>Nucl. Acids Res.</prism:publicationName>
    <prism:startingPage>gkn176</prism:startingPage>
    <prism:category>blast</prism:category>
    <prism:category>ontology</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/jyuh/article/2535357">
    <title>CLEAR-test: combining inference for differential expression and variability in microarray data analysis.</title>
    <link>http://www.citeulike.org/user/jyuh/article/2535357</link>
    <description>&lt;i&gt;J Biomed Inform, Vol. 41, No. 1. (February 2008), pp. 33-45.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;A common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability.</description>
    <dc:title>CLEAR-test: combining inference for differential expression and variability in microarray data analysis.</dc:title>

    <dc:creator>J Valls</dc:creator>
    <dc:creator>M Grau</dc:creator>
    <dc:creator>X Solé</dc:creator>
    <dc:creator>P Hernández</dc:creator>
    <dc:creator>D Montaner</dc:creator>
    <dc:creator>J Dopazo</dc:creator>
    <dc:creator>MA Peinado</dc:creator>
    <dc:creator>G Capellá</dc:creator>
    <dc:creator>V Moreno</dc:creator>
    <dc:creator>MA Pujana</dc:creator>
    <dc:identifier>doi:10.1016/j.jbi.2007.05.005</dc:identifier>
    <dc:source>J Biomed Inform, Vol. 41, No. 1. (February 2008), pp. 33-45.</dc:source>
    <dc:date>2008-03-15T03:33:57-00:00</dc:date>
    <prism:publicationYear>2008</prism:publicationYear>
    <prism:publicationName>J Biomed Inform</prism:publicationName>
    <prism:issn>1532-0480</prism:issn>
    <prism:volume>41</prism:volume>
    <prism:number>1</prism:number>
    <prism:startingPage>33</prism:startingPage>
    <prism:endingPage>45</prism:endingPage>
    <prism:category>no-tag</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/jyuh/article/2151781">
    <title>Joint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases.</title>
    <link>http://www.citeulike.org/user/jyuh/article/2151781</link>
    <description>&lt;i&gt;Nucleic Acids Res (17 December 2007)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Single nucleotide polymorphisms (SNPs) are, together with copy number variation, the primary source of variation in the human genome. SNPs are associated with altered response to drug treatment, susceptibility to disease and other phenotypic variation. Furthermore, during genetic screens for disease-associated mutations in groups of patients and control individuals, the distinction between disease causing mutation and polymorphism is often unclear. Annotation of the functional and structural implications of single nucleotide changes thus provides valuable information to interpret and guide experiments. The SNPeffect and PupaSuite databases are now synchronized to deliver annotations for both non-coding and coding SNP, as well as annotations for the SwissProt set of human disease mutations. In addition, SNPeffect now contains predictions of Tango2: an improved aggregation detector, and Waltz: a novel predictor of amyloid-forming sequences, as well as improved predictors for regions that are recognized by the Hsp70 family of chaperones. The new PupaSuite version incorporates predictions for SNPs in silencers and miRNAs including their targets, as well as additional methods for predicting SNPs in TFBSs and splice sites. Also predictions for mouse and rat genomes have been added. In addition, a PupaSuite web service has been developed to enable data access, programmatically. The combined database holds annotations for 4 965 073 regulatory as well as 133 505 coding human SNPs and 14 935 disease mutations, and phenotypic descriptions of 43 797 human proteins and is accessible via http://snpeffect.vib.be and http://pupasuite.bioinfo.cipf.es/.</description>
    <dc:title>Joint annotation of coding and non-coding single nucleotide polymorphisms and mutations in the SNPeffect and PupaSuite databases.</dc:title>

    <dc:creator>Joke Reumers</dc:creator>
    <dc:creator>Lucia Conde</dc:creator>
    <dc:creator>Ignacio Medina</dc:creator>
    <dc:creator>Sebastian Maurer-Stroh</dc:creator>
    <dc:creator>Joost Van Durme</dc:creator>
    <dc:creator>Joaquin Dopazo</dc:creator>
    <dc:creator>Frederic Rousseau</dc:creator>
    <dc:creator>Joost Schymkowitz</dc:creator>
    <dc:source>Nucleic Acids Res (17 December 2007)</dc:source>
    <dc:date>2007-12-20T14:30:02-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>Nucleic Acids Res</prism:publicationName>
    <prism:issn>1362-4962</prism:issn>
    <prism:category>snp</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/jyuh/article/1938525">
    <title>Functional profiling of microarray experiments using text-mining derived bioentities</title>
    <link>http://www.citeulike.org/user/jyuh/article/1938525</link>
    <description>&lt;i&gt;Bioinformatics, Vol. 23, No. 22. (15 November 2007), pp. 3098-3099.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Motivation: The increasing use of microarray technologies brought about a parallel demand in methods for the functional interpretation of the results. Beyond the conventional functional annotations for genes, such as gene ontology, pathways, etc. other sources of information are still to be exploited. Text-mining methods allow extracting informative terms (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. We show how to use these associations within an appropriate statistical framework and how to apply them through easy-to-use, web-based environments to the functional interpretation of microarray experiments. Functional enrichment and gene set enrichment tests using bioentities are presented. Availability: Marmite and MarmiteScan can be found in the Babelomics suite: http://www.babelomics.org Contact: jdopazo@cipf.es Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm445</description>
    <dc:title>Functional profiling of microarray experiments using text-mining derived bioentities</dc:title>

    <dc:creator>Pablo Minguez</dc:creator>
    <dc:creator>Fatima Al-Shahrour</dc:creator>
    <dc:creator>David Montaner</dc:creator>
    <dc:creator>Joaquin Dopazo</dc:creator>
    <dc:identifier>doi:10.1093/bioinformatics/btm445</dc:identifier>
    <dc:source>Bioinformatics, Vol. 23, No. 22. (15 November 2007), pp. 3098-3099.</dc:source>
    <dc:date>2007-11-19T16:26:16-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>Bioinformatics</prism:publicationName>
    <prism:volume>23</prism:volume>
    <prism:number>22</prism:number>
    <prism:startingPage>3098</prism:startingPage>
    <prism:endingPage>3099</prism:endingPage>
    <prism:category>no-tag</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/jyuh/article/1005973">
    <title>Prophet, a web-based tool for class prediction using microarray data.</title>
    <link>http://www.citeulike.org/user/jyuh/article/1005973</link>
    <description>&lt;i&gt;Bioinformatics (30 November 2006)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Sample classification and class prediction is the aim of many gene expression studies. We present a web-based application, Prophet, that builds prediction rules and allows using them for further sample classification. Prophet automatically chooses the best classifier, along with the optimal selection of genes, using a strategy that renders unbiased cross-validated errors. Prophet is linked to different microarray data analysis modules, and includes a unique feature: the possibility of performing the functional interpretation of the molecular signature found. AVAILABILITY: Prophet can be found at the URL http://prophet.bioinfo.cipf.es/ or within the GEPAS package at http://www.gepas.org.</description>
    <dc:title>Prophet, a web-based tool for class prediction using microarray data.</dc:title>

    <dc:creator>Ignacio Medina</dc:creator>
    <dc:creator>David Montaner</dc:creator>
    <dc:creator>Joaquín Tárraga</dc:creator>
    <dc:creator>Joaquín Dopazo</dc:creator>
    <dc:source>Bioinformatics (30 November 2006)</dc:source>
    <dc:date>2006-12-21T15:28:45-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Bioinformatics</prism:publicationName>
    <prism:issn>1460-2059</prism:issn>
    <prism:category>no-tag</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/jyuh/article/1449483">
    <title>FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.</title>
    <link>http://www.citeulike.org/user/jyuh/article/1449483</link>
    <description>&lt;i&gt;Nucleic Acids Res (3 May 2007)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the data, relating the available information with the hypotheses that originated the experiment. Thus, functional profiling methods have become essential in diverse scenarios such as microarray experiments, proteomics, etc. We present the FatiGO+, a web-based tool for the functional profiling of genome-scale experiments, specially oriented to the interpretation of microarray experiments. In addition to different functional annotations (gene ontology, KEGG pathways, Interpro motifs, Swissprot keywords and text-mining based bioentities related to diseases and chemical compounds) FatiGO+ includes, as a novelty, regulatory and structural information. The regulatory information used includes predictions of targets for distinct regulatory elements (obtained from the Transfac and CisRed databases). Additionally FatiGO+ uses predictions of target motifs of miRNA to infer which of these can be activated or deactivated in the sample of genes studied. Finally, properties of gene products related to their relative location and connections in the interactome have also been used. Also, enrichment of any of these functional terms can be directly analysed on chromosomal coordinates. FatiGO+ can be found at: http://www.fatigoplus.org and within the Babelomics environment http://www.babelomics.org.</description>
    <dc:title>FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.</dc:title>

    <dc:creator>Fátima Al-Shahrour</dc:creator>
    <dc:creator>Pablo Minguez</dc:creator>
    <dc:creator>Joaquín Tárraga</dc:creator>
    <dc:creator>Ignacio Medina</dc:creator>
    <dc:creator>Eva Alloza</dc:creator>
    <dc:creator>David Montaner</dc:creator>
    <dc:creator>Joaquín Dopazo</dc:creator>
    <dc:source>Nucleic Acids Res (3 May 2007)</dc:source>
    <dc:date>2007-07-11T14:51:20-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>Nucleic Acids Res</prism:publicationName>
    <prism:issn>1362-4962</prism:issn>
    <prism:category>no-tag</prism:category>
</item>



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