<rdf:RDF xmlns:rdf="http://www.openarchives.org/OAI/2.0/rdf/" xmlns:ow="http://www.ontoweb.org/ontology/1#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:ds="http://dspace.org/ds/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/rdf/ http://www.openarchives.org/OAI/2.0/rdf.xsd">
   <ow:Publication rdf:about="oai:digibug.ugr.es:10481/74389">
      <dc:title>Functional Enrichment Analysis of Regulatory Elements</dc:title>
      <dc:creator>García Moreno, Adrián</dc:creator>
      <dc:creator>López Domínguez, Raúl</dc:creator>
      <dc:creator>Villatoro García, Juan Antonio</dc:creator>
      <dc:creator>Ramírez Mena, Alberto</dc:creator>
      <dc:creator>Aparicio Puerta, Ernesto Luis</dc:creator>
      <dc:creator>Hackenberg, Michael</dc:creator>
      <dc:creator>Carmona Sáez, Pedro</dc:creator>
      <dc:subject>Gene set analysis</dc:subject>
      <dc:subject>Regulation</dc:subject>
      <dc:subject>Web tool</dc:subject>
      <dc:subject>Enrichment analysis</dc:subject>
      <dc:subject>Functional analysis</dc:subject>
      <dc:description>This work has been partially supported by FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento/(grant CV20-36723), grant PID2020-119032RB-I00, MCIN/AEI/10.13039/501100011033 and FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades (Grant P20_00335).</dc:description>
      <dc:description>Statistical methods for enrichment analysis are important tools to extract biological information&#xd;
from omics experiments. Although these methods have been widely used for the analysis&#xd;
of gene and protein lists, the development of high-throughput technologies for regulatory elements&#xd;
demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis&#xd;
methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical&#xd;
significance is determined via a power weighting function for target genes and tested by theWallenius&#xd;
noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have&#xd;
been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of&#xd;
this tool to extract biological information from a list of regulatory elements. These new methods are&#xd;
available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that&#xd;
allows the integration of heterogeneous information.</dc:description>
      <dc:date>2022-04-20T08:50:15Z</dc:date>
      <dc:date>2022-04-20T08:50:15Z</dc:date>
      <dc:date>2022-03-03</dc:date>
      <dc:type>info:eu-repo/semantics/article</dc:type>
      <dc:identifier>Garcia-Moreno, A... [et al.]. Functional Enrichment Analysis of Regulatory Elements. Biomedicines 2022, 10, 590. [https://doi.org/10.3390/biomedicines10030590]</dc:identifier>
      <dc:identifier>http://hdl.handle.net/10481/74389</dc:identifier>
      <dc:identifier>10.3390/biomedicines10030590</dc:identifier>
      <dc:language>eng</dc:language>
      <dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
      <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
      <dc:rights>Atribución 3.0 España</dc:rights>
      <dc:publisher>MDPI</dc:publisher>
   </ow:Publication>
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