MicroRNA categorization using sequence motifs and k-mers.
Identifieur interne : 000C24 ( PubMed/Checkpoint ); précédent : 000C23; suivant : 000C25MicroRNA categorization using sequence motifs and k-mers.
Auteurs : Malik Yousef [Israël] ; Waleed Khalifa [Israël] ; Lhan Erkin Acar [Turquie] ; Jens Allmer [Turquie]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Fabaceae, Précurseurs des ARN, microARN.
- métabolisme : Précurseurs des ARN, microARN.
- Animaux, Fabaceae, Humains, Phylogénie, Séquence nucléotidique, Séquençage nucléotidique à haut débit, microARN.
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : MicroRNAs.
- classification : Fabaceae.
- genetics : Fabaceae, MicroRNAs, RNA Precursors.
- chemical , metabolism : MicroRNAs, RNA Precursors.
- Animals, Base Sequence, High-Throughput Nucleotide Sequencing, Humans, Phylogeny.
Abstract
Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species.
DOI: 10.1186/s12859-017-1584-1
PubMed: 28292266
Affiliations:
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Yousef</name><affiliation wicri:level="1"><nlm:affiliation>Community Information Systems, Zefat Academic College, Zefat, 13206, Israel. malik.yousef@gmail.com.</nlm:affiliation><country xml:lang="fr">Israël</country><wicri:regionArea>Community Information Systems, Zefat Academic College, Zefat, 13206</wicri:regionArea><wicri:noRegion>13206</wicri:noRegion></affiliation></author><author><name sortKey="Khalifa, Waleed" sort="Khalifa, Waleed" uniqKey="Khalifa W" first="Waleed" last="Khalifa">Waleed Khalifa</name><affiliation wicri:level="1"><nlm:affiliation>Computer Science, The College of Sakhnin, Sakhnin, 30810, Israel.</nlm:affiliation><country xml:lang="fr">Israël</country><wicri:regionArea>Computer Science, The College of Sakhnin, Sakhnin, 30810</wicri:regionArea><wicri:noRegion>30810</wicri:noRegion></affiliation></author><author><name sortKey="Acar, Lhan Erkin" sort="Acar, Lhan Erkin" uniqKey="Acar " first=" Lhan Erkin" last="Acar"> Lhan Erkin Acar</name><affiliation wicri:level="1"><nlm:affiliation>Biotechnology, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.</nlm:affiliation><country xml:lang="fr">Turquie</country><wicri:regionArea>Biotechnology, Izmir Institute of Technology, 35430, Urla, Izmir</wicri:regionArea><wicri:noRegion>Izmir</wicri:noRegion></affiliation></author><author><name sortKey="Allmer, Jens" sort="Allmer, Jens" uniqKey="Allmer J" first="Jens" last="Allmer">Jens Allmer</name><affiliation wicri:level="1"><nlm:affiliation>Molecular Biology and Genetics, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey. jens@allmer.de.</nlm:affiliation><country xml:lang="fr">Turquie</country><wicri:regionArea>Molecular Biology and Genetics, Izmir Institute of Technology, 35430, Urla, Izmir</wicri:regionArea><wicri:noRegion>Izmir</wicri:noRegion></affiliation></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Base Sequence</term><term>Fabaceae (classification)</term><term>Fabaceae (genetics)</term><term>High-Throughput Nucleotide Sequencing</term><term>Humans</term><term>MicroRNAs (chemistry)</term><term>MicroRNAs (genetics)</term><term>MicroRNAs (metabolism)</term><term>Phylogeny</term><term>RNA Precursors (genetics)</term><term>RNA Precursors (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Animaux</term><term>Fabaceae ()</term><term>Fabaceae (génétique)</term><term>Humains</term><term>Phylogénie</term><term>Précurseurs des ARN (génétique)</term><term>Précurseurs des ARN (métabolisme)</term><term>Séquence nucléotidique</term><term>Séquençage nucléotidique à haut débit</term><term>microARN ()</term><term>microARN (génétique)</term><term>microARN (métabolisme)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>MicroRNAs</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Fabaceae</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Fabaceae</term><term>MicroRNAs</term><term>RNA Precursors</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Fabaceae</term><term>Précurseurs des ARN</term><term>microARN</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>MicroRNAs</term><term>RNA Precursors</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Précurseurs des ARN</term><term>microARN</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Base Sequence</term><term>High-Throughput Nucleotide Sequencing</term><term>Humans</term><term>Phylogeny</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Animaux</term><term>Fabaceae</term><term>Humains</term><term>Phylogénie</term><term>Séquence nucléotidique</term><term>Séquençage nucléotidique à haut débit</term><term>microARN</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">28292266</PMID><DateCompleted><Year>2017</Year><Month>08</Month><Day>30</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><Issue>1</Issue><PubDate><Year>2017</Year><Month>Mar</Month><Day>14</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>MicroRNA categorization using sequence motifs and k-mers.</ArticleTitle><Pagination><MedlinePgn>170</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-017-1584-1</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yousef</LastName><ForeName>Malik</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Community Information Systems, Zefat Academic College, Zefat, 13206, Israel. malik.yousef@gmail.com.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Khalifa</LastName><ForeName>Waleed</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>Computer Science, The College of Sakhnin, Sakhnin, 30810, Israel.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Acar</LastName><ForeName>İlhan Erkin</ForeName><Initials>İE</Initials><AffiliationInfo><Affiliation>Biotechnology, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Allmer</LastName><ForeName>Jens</ForeName><Initials>J</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-2164-7335</Identifier><AffiliationInfo><Affiliation>Molecular Biology and Genetics, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey. jens@allmer.de.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Bionia Incorporated, IZTEKGEB A8, 35430, Urla, Izmir, Turkey. jens@allmer.de.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>03</Month><Day>14</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D035683">MicroRNAs</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D012322">RNA Precursors</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001483" MajorTopicYN="N">Base Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007887" MajorTopicYN="N">Fabaceae</DescriptorName><QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D059014" MajorTopicYN="N">High-Throughput Nucleotide Sequencing</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D035683" MajorTopicYN="N">MicroRNAs</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010802" MajorTopicYN="N">Phylogeny</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012322" MajorTopicYN="N">RNA Precursors</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Differentiate miRNAs among species</Keyword><Keyword MajorTopicYN="N">Machine learning</Keyword><Keyword MajorTopicYN="N">Pre-microRNA</Keyword><Keyword MajorTopicYN="N">Sequence motifs</Keyword><Keyword MajorTopicYN="N">k-mer</Keyword><Keyword MajorTopicYN="N">miRNA categorization</Keyword><Keyword 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