An improved approach for reconstructing consensus repeats from short sequence reads.
Identifieur interne : 000A13 ( PubMed/Checkpoint ); précédent : 000A12; suivant : 000A14An improved approach for reconstructing consensus repeats from short sequence reads.
Auteurs : Chong Chu [États-Unis] ; Jingwen Pei [États-Unis] ; Yufeng Wu [États-Unis]Source :
- BMC genomics [ 1471-2164 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA.
- genetics : Arabidopsis, Birds, Drosophila melanogaster.
- methods : Sequence Analysis, DNA.
- Algorithms, Animals, Base Sequence, Consensus Sequence, Humans, Repetitive Sequences, Nucleic Acid, Sequence Alignment.
Abstract
Repeat elements are important components of most eukaryotic genomes. Most existing tools for repeat analysis rely either on high quality reference genomes or existing repeat libraries. Thus, it is still challenging to do repeat analysis for species with highly repetitive or complex genomes which often do not have good reference genomes or annotated repeat libraries. Recently we developed a computational method called REPdenovo that constructs consensus repeat sequences directly from short sequence reads, which outperforms an existing tool called RepARK. One major issue with REPdenovo is that it doesn't perform well for repeats with relatively high divergence rates or low copy numbers. In this paper, we present an improved approach for constructing consensus repeats directly from short reads. Comparing with the original REPdenovo, the improved approach uses more repeat-related k-mers and improves repeat assembly quality using a consensus-based k-mer processing method.
DOI: 10.1186/s12864-018-4920-6
PubMed: 30367582
Affiliations:
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02115, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, 02115, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author><author><name sortKey="Pei, Jingwen" sort="Pei, Jingwen" uniqKey="Pei J" first="Jingwen" last="Pei">Jingwen Pei</name><affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, Unit 2155, Storrs, 06269, CT, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, Unit 2155, Storrs, 06269, CT</wicri:regionArea><placeName><region type="state">Connecticut</region></placeName></affiliation></author><author><name sortKey="Wu, Yufeng" sort="Wu, Yufeng" uniqKey="Wu Y" first="Yufeng" last="Wu">Yufeng Wu</name><affiliation wicri:level="2"><nlm:affiliation>Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, Unit 2155, Storrs, 06269, CT, USA. yufeng.wu@uconn.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, Unit 2155, Storrs, 06269, CT</wicri:regionArea><placeName><region type="state">Connecticut</region></placeName></affiliation></author></analytic><series><title level="j">BMC genomics</title><idno type="eISSN">1471-2164</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Arabidopsis (genetics)</term><term>Base Sequence</term><term>Birds (genetics)</term><term>Consensus Sequence</term><term>DNA (chemistry)</term><term>Drosophila melanogaster (genetics)</term><term>Humans</term><term>Repetitive Sequences, Nucleic Acid</term><term>Sequence Alignment</term><term>Sequence Analysis, DNA (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ADN ()</term><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence d'ADN ()</term><term>Animaux</term><term>Arabidopsis (génétique)</term><term>Drosophila melanogaster (génétique)</term><term>Humains</term><term>Oiseaux (génétique)</term><term>Séquence consensus</term><term>Séquence nucléotidique</term><term>Séquences répétées d'acides nucléiques</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Arabidopsis</term><term>Birds</term><term>Drosophila melanogaster</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Arabidopsis</term><term>Drosophila melanogaster</term><term>Oiseaux</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Base Sequence</term><term>Consensus Sequence</term><term>Humans</term><term>Repetitive Sequences, Nucleic Acid</term><term>Sequence Alignment</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>ADN</term><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de séquence d'ADN</term><term>Animaux</term><term>Humains</term><term>Séquence consensus</term><term>Séquence nucléotidique</term><term>Séquences répétées d'acides nucléiques</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Repeat elements are important components of most eukaryotic genomes. Most existing tools for repeat analysis rely either on high quality reference genomes or existing repeat libraries. Thus, it is still challenging to do repeat analysis for species with highly repetitive or complex genomes which often do not have good reference genomes or annotated repeat libraries. Recently we developed a computational method called REPdenovo that constructs consensus repeat sequences directly from short sequence reads, which outperforms an existing tool called RepARK. One major issue with REPdenovo is that it doesn't perform well for repeats with relatively high divergence rates or low copy numbers. In this paper, we present an improved approach for constructing consensus repeats directly from short reads. Comparing with the original REPdenovo, the improved approach uses more repeat-related k-mers and improves repeat assembly quality using a consensus-based k-mer processing method.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30367582</PMID><DateCompleted><Year>2019</Year><Month>02</Month><Day>26</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2164</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>Suppl 6</Issue><PubDate><Year>2018</Year><Month>Aug</Month><Day>13</Day></PubDate></JournalIssue><Title>BMC genomics</Title><ISOAbbreviation>BMC Genomics</ISOAbbreviation></Journal><ArticleTitle>An improved approach for reconstructing consensus repeats from short sequence reads.</ArticleTitle><Pagination><MedlinePgn>566</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12864-018-4920-6</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Repeat elements are important components of most eukaryotic genomes. Most existing tools for repeat analysis rely either on high quality reference genomes or existing repeat libraries. Thus, it is still challenging to do repeat analysis for species with highly repetitive or complex genomes which often do not have good reference genomes or annotated repeat libraries. Recently we developed a computational method called REPdenovo that constructs consensus repeat sequences directly from short sequence reads, which outperforms an existing tool called RepARK. One major issue with REPdenovo is that it doesn't perform well for repeats with relatively high divergence rates or low copy numbers. In this paper, we present an improved approach for constructing consensus repeats directly from short reads. Comparing with the original REPdenovo, the improved approach uses more repeat-related k-mers and improves repeat assembly quality using a consensus-based k-mer processing method.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We compare the performance of the new method with REPdenovo and RepARK on Human, Arabidopsis thaliana and Drosophila melanogaster short sequencing data. And the new method fully constructs more repeats in Repbase than the original REPdenovo and RepARK, especially for repeats of higher divergence rates and lower copy number. We also apply our new method on Hummingbird data which doesn't have a known repeat library, and it constructs many repeat elements that can be validated using PacBio long reads.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We propose an improved method for reconstructing repeat elements directly from short sequence reads. The results show that our new method can assemble more complete repeats than REPdenovo (and also RepARK). Our new approach has been implemented as part of the REPdenovo software package, which is available for download at https://github.com/Reedwarbler/REPdenovo .</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chu</LastName><ForeName>Chong</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, 02115, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pei</LastName><ForeName>Jingwen</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, Unit 2155, Storrs, 06269, CT, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Yufeng</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, Unit 2155, Storrs, 06269, CT, USA. yufeng.wu@uconn.edu.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D023362">Evaluation Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>08</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Genomics</MedlineTA><NlmUniqueID>100965258</NlmUniqueID><ISSNLinking>1471-2164</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>9007-49-2</RegistryNumber><NameOfSubstance UI="D004247">DNA</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName 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