Sequence-based 5-mers highly correlated to epigenetic modifications in genes interactions.
Identifieur interne : 001F54 ( Ncbi/Merge ); précédent : 001F53; suivant : 001F55Sequence-based 5-mers highly correlated to epigenetic modifications in genes interactions.
Auteurs : Dariush Salimi [Iran] ; Ali Moeini [Iran] ; Ali Masoudi-Nejad [Iran]Source :
- Genes & genomics [ 2092-9293 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : Peroxisome Proliferator-Activated Receptors.
- genetics : Base Sequence, Epigenesis, Genetic.
- Humans, Signal Transduction.
Abstract
One of the main concerns in biology is extracting sophisticated features from DNA sequence for gene interaction determination, receiving a great deal of researchers' attention. The epigenetic modifications along with their patterns have been intensely recognized as dominant features affecting on gene expression. However, studying sequenced-based features highly correlated to this key element has remained limited. The main objective in this research was to propose a new feature highly correlated to epigenetic modifications capable of classification of genes. In this paper, classification of 34 genes in PPAR signaling pathway associated with muscle fat tissue in human was performed. Using different statistical outlier detection methods, we proposed that 5-mers highly correlated to epigenetic modifications can correctly categorize the genes involved in the same biological pathway or process. Thirty-four genes in PPAR signaling pathway were classified via applying a proposed feature, 5-mers strongly associated to 17 different epigenetic modifications. For this, diverse statistical outlier detection methods were applied to specify the group of thoroughly correlated genes. The results indicated that these 5-mers can appropriately identify correlated genes. In addition, our results corresponded to GeneMania interaction information, leading to support the suggested method. The appealing findings imply that not only epigenetic modifications but also their highly correlated 5-mers can be applied for reconstructing gene regulatory networks as supplementary data as well as other applications like physical interaction, genes prioritization, indicating some sort of data fusion in this analysis.
DOI: 10.1007/s13258-018-0730-0
PubMed: 30187226
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Dariush" sort="Salimi, Dariush" uniqKey="Salimi D" first="Dariush" last="Salimi">Dariush Salimi</name><affiliation wicri:level="1"><nlm:affiliation>Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Moeini, Ali" sort="Moeini, Ali" uniqKey="Moeini A" first="Ali" last="Moeini">Ali Moeini</name><affiliation wicri:level="1"><nlm:affiliation>Department of Algorithms and Computation, School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran. moeini@ut.ac.ir.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Algorithms and Computation, School of Engineering Science, College of Engineering, University of Tehran, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Masoudi Nejad, Ali" sort="Masoudi Nejad, Ali" uniqKey="Masoudi Nejad A" first="Ali" last="Masoudi-Nejad">Ali Masoudi-Nejad</name><affiliation wicri:level="1"><nlm:affiliation>Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. amasoudin@ut.ac.ir.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author></analytic><series><title level="j">Genes & genomics</title><idno type="eISSN">2092-9293</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Base Sequence (genetics)</term><term>Epigenesis, Genetic (genetics)</term><term>Humans</term><term>Peroxisome Proliferator-Activated Receptors (genetics)</term><term>Signal Transduction</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Humains</term><term>Récepteurs activés par les proliférateurs de peroxysomes (génétique)</term><term>Séquence nucléotidique (génétique)</term><term>Transduction du signal</term><term>Épigenèse génétique (génétique)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Peroxisome Proliferator-Activated Receptors</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Base Sequence</term><term>Epigenesis, Genetic</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Récepteurs activés par les proliférateurs de peroxysomes</term><term>Séquence nucléotidique</term><term>Épigenèse génétique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Signal Transduction</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Transduction du signal</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">One of the main concerns in biology is extracting sophisticated features from DNA sequence for gene interaction determination, receiving a great deal of researchers' attention. The epigenetic modifications along with their patterns have been intensely recognized as dominant features affecting on gene expression. However, studying sequenced-based features highly correlated to this key element has remained limited. The main objective in this research was to propose a new feature highly correlated to epigenetic modifications capable of classification of genes. In this paper, classification of 34 genes in PPAR signaling pathway associated with muscle fat tissue in human was performed. Using different statistical outlier detection methods, we proposed that 5-mers highly correlated to epigenetic modifications can correctly categorize the genes involved in the same biological pathway or process. Thirty-four genes in PPAR signaling pathway were classified via applying a proposed feature, 5-mers strongly associated to 17 different epigenetic modifications. For this, diverse statistical outlier detection methods were applied to specify the group of thoroughly correlated genes. The results indicated that these 5-mers can appropriately identify correlated genes. In addition, our results corresponded to GeneMania interaction information, leading to support the suggested method. The appealing findings imply that not only epigenetic modifications but also their highly correlated 5-mers can be applied for reconstructing gene regulatory networks as supplementary data as well as other applications like physical interaction, genes prioritization, indicating some sort of data fusion in this analysis.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30187226</PMID><DateCompleted><Year>2019</Year><Month>02</Month><Day>22</Day></DateCompleted><DateRevised><Year>2019</Year><Month>02</Month><Day>22</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">2092-9293</ISSN><JournalIssue CitedMedium="Internet"><Volume>40</Volume><Issue>12</Issue><PubDate><Year>2018</Year><Month>12</Month></PubDate></JournalIssue><Title>Genes & genomics</Title><ISOAbbreviation>Genes Genomics</ISOAbbreviation></Journal><ArticleTitle>Sequence-based 5-mers highly correlated to epigenetic modifications in genes interactions.</ArticleTitle><Pagination><MedlinePgn>1363-1371</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s13258-018-0730-0</ELocationID><Abstract><AbstractText>One of the main concerns in biology is extracting sophisticated features from DNA sequence for gene interaction determination, receiving a great deal of researchers' attention. The epigenetic modifications along with their patterns have been intensely recognized as dominant features affecting on gene expression. However, studying sequenced-based features highly correlated to this key element has remained limited. The main objective in this research was to propose a new feature highly correlated to epigenetic modifications capable of classification of genes. In this paper, classification of 34 genes in PPAR signaling pathway associated with muscle fat tissue in human was performed. Using different statistical outlier detection methods, we proposed that 5-mers highly correlated to epigenetic modifications can correctly categorize the genes involved in the same biological pathway or process. Thirty-four genes in PPAR signaling pathway were classified via applying a proposed feature, 5-mers strongly associated to 17 different epigenetic modifications. For this, diverse statistical outlier detection methods were applied to specify the group of thoroughly correlated genes. The results indicated that these 5-mers can appropriately identify correlated genes. In addition, our results corresponded to GeneMania interaction information, leading to support the suggested method. The appealing findings imply that not only epigenetic modifications but also their highly correlated 5-mers can be applied for reconstructing gene regulatory networks as supplementary data as well as other applications like physical interaction, genes prioritization, indicating some sort of data fusion in this analysis.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Salimi</LastName><ForeName>Dariush</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Moeini</LastName><ForeName>Ali</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Algorithms and Computation, School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran. moeini@ut.ac.ir.</Affiliation></AffiliationInfo></Author><Author 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