Discovery and annotation of small proteins using genomics, proteomics, and computational approaches.
Identifieur interne : 002F24 ( Main/Exploration ); précédent : 002F23; suivant : 002F25Discovery and annotation of small proteins using genomics, proteomics, and computational approaches.
Auteurs : Xiaohan Yang [États-Unis] ; Timothy J. Tschaplinski ; Gregory B. Hurst ; Sara Jawdy ; Paul E. Abraham ; Patricia K. Lankford ; Rachel M. Adams ; Manesh B. Shah ; Robert L. Hettich ; Erika Lindquist ; Udaya C. Kalluri ; Lee E. Gunter ; Christa Pennacchio ; Gerald A. TuskanSource :
- Genome research [ 1549-5469 ] ; 2011.
Descripteurs français
- KwdFr :
- ARN non traduit (génétique), Annotation de séquence moléculaire (méthodes), Biologie informatique (MeSH), Cadres ouverts de lecture (MeSH), Données de séquences moléculaires (MeSH), Feuilles de plante (génétique), Génomique (MeSH), Plan de recherche (MeSH), Populus (génétique), Protéines végétales (génétique), Protéomique (MeSH), Étiquettes de séquences exprimées (MeSH).
- MESH :
English descriptors
- KwdEn :
- Computational Biology (MeSH), Expressed Sequence Tags (MeSH), Genomics (MeSH), Molecular Sequence Annotation (methods), Molecular Sequence Data (MeSH), Open Reading Frames (MeSH), Plant Leaves (genetics), Plant Proteins (genetics), Populus (genetics), Proteomics (MeSH), RNA, Untranslated (genetics), Research Design (MeSH).
- MESH :
- chemical , genetics : Plant Proteins, RNA, Untranslated.
- genetics : Plant Leaves, Populus.
- methods : Molecular Sequence Annotation.
- Computational Biology, Expressed Sequence Tags, Genomics, Molecular Sequence Data, Open Reading Frames, Proteomics, Research Design.
Abstract
Small proteins (10-200 amino acids [aa] in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained ~2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10-200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) coding-potential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.
DOI: 10.1101/gr.109280.110
PubMed: 21367939
PubMed Central: PMC3065711
Affiliations:
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Tuskan</name></author></analytic><series><title level="j">Genome research</title><idno type="eISSN">1549-5469</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Computational Biology (MeSH)</term><term>Expressed Sequence Tags (MeSH)</term><term>Genomics (MeSH)</term><term>Molecular Sequence Annotation (methods)</term><term>Molecular Sequence Data (MeSH)</term><term>Open Reading Frames (MeSH)</term><term>Plant Leaves (genetics)</term><term>Plant Proteins (genetics)</term><term>Populus (genetics)</term><term>Proteomics (MeSH)</term><term>RNA, Untranslated (genetics)</term><term>Research Design (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>ARN non traduit (génétique)</term><term>Annotation de séquence moléculaire (méthodes)</term><term>Biologie informatique (MeSH)</term><term>Cadres ouverts de lecture (MeSH)</term><term>Données de séquences moléculaires (MeSH)</term><term>Feuilles de plante (génétique)</term><term>Génomique (MeSH)</term><term>Plan de recherche (MeSH)</term><term>Populus (génétique)</term><term>Protéines végétales (génétique)</term><term>Protéomique (MeSH)</term><term>Étiquettes de séquences exprimées (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Plant Proteins</term><term>RNA, Untranslated</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Plant Leaves</term><term>Populus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ARN non traduit</term><term>Feuilles de plante</term><term>Populus</term><term>Protéines végétales</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Molecular Sequence Annotation</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Annotation de séquence moléculaire</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Computational Biology</term><term>Expressed Sequence Tags</term><term>Genomics</term><term>Molecular Sequence Data</term><term>Open Reading Frames</term><term>Proteomics</term><term>Research Design</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Biologie informatique</term><term>Cadres ouverts de lecture</term><term>Données de séquences moléculaires</term><term>Génomique</term><term>Plan de recherche</term><term>Protéomique</term><term>Étiquettes de séquences exprimées</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Small proteins (10-200 amino acids [aa] in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained ~2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10-200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) coding-potential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">21367939</PMID><DateCompleted><Year>2011</Year><Month>07</Month><Day>13</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1549-5469</ISSN><JournalIssue CitedMedium="Internet"><Volume>21</Volume><Issue>4</Issue><PubDate><Year>2011</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Genome research</Title><ISOAbbreviation>Genome Res</ISOAbbreviation></Journal><ArticleTitle>Discovery and annotation of small proteins using genomics, proteomics, and computational approaches.</ArticleTitle><Pagination><MedlinePgn>634-41</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1101/gr.109280.110</ELocationID><Abstract><AbstractText>Small proteins (10-200 amino acids [aa] in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained ~2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10-200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) coding-potential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Xiaohan</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. yangx@ornl.gov</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tschaplinski</LastName><ForeName>Timothy J</ForeName><Initials>TJ</Initials></Author><Author ValidYN="Y"><LastName>Hurst</LastName><ForeName>Gregory B</ForeName><Initials>GB</Initials></Author><Author ValidYN="Y"><LastName>Jawdy</LastName><ForeName>Sara</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Abraham</LastName><ForeName>Paul E</ForeName><Initials>PE</Initials></Author><Author ValidYN="Y"><LastName>Lankford</LastName><ForeName>Patricia K</ForeName><Initials>PK</Initials></Author><Author ValidYN="Y"><LastName>Adams</LastName><ForeName>Rachel M</ForeName><Initials>RM</Initials></Author><Author ValidYN="Y"><LastName>Shah</LastName><ForeName>Manesh B</ForeName><Initials>MB</Initials></Author><Author ValidYN="Y"><LastName>Hettich</LastName><ForeName>Robert L</ForeName><Initials>RL</Initials></Author><Author ValidYN="Y"><LastName>Lindquist</LastName><ForeName>Erika</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Kalluri</LastName><ForeName>Udaya C</ForeName><Initials>UC</Initials></Author><Author ValidYN="Y"><LastName>Gunter</LastName><ForeName>Lee E</ForeName><Initials>LE</Initials></Author><Author ValidYN="Y"><LastName>Pennacchio</LastName><ForeName>Christa</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Tuskan</LastName><ForeName>Gerald 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22;19(5):651-2</Citation><ArticleIdList><ArticleId IdType="pubmed">12651724</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Tennessee</li></region></list><tree><noCountry><name sortKey="Abraham, Paul E" sort="Abraham, Paul E" uniqKey="Abraham P" first="Paul E" last="Abraham">Paul E. Abraham</name><name sortKey="Adams, Rachel M" sort="Adams, Rachel M" uniqKey="Adams R" first="Rachel M" last="Adams">Rachel M. Adams</name><name sortKey="Gunter, Lee E" sort="Gunter, Lee E" uniqKey="Gunter L" first="Lee E" last="Gunter">Lee E. Gunter</name><name sortKey="Hettich, Robert L" sort="Hettich, Robert L" uniqKey="Hettich R" first="Robert L" last="Hettich">Robert L. Hettich</name><name sortKey="Hurst, Gregory B" sort="Hurst, Gregory B" uniqKey="Hurst G" first="Gregory B" last="Hurst">Gregory B. Hurst</name><name sortKey="Jawdy, Sara" sort="Jawdy, Sara" uniqKey="Jawdy S" first="Sara" last="Jawdy">Sara Jawdy</name><name sortKey="Kalluri, Udaya C" sort="Kalluri, Udaya C" uniqKey="Kalluri U" first="Udaya C" last="Kalluri">Udaya C. Kalluri</name><name sortKey="Lankford, Patricia K" sort="Lankford, Patricia K" uniqKey="Lankford P" first="Patricia K" last="Lankford">Patricia K. Lankford</name><name sortKey="Lindquist, Erika" sort="Lindquist, Erika" uniqKey="Lindquist E" first="Erika" last="Lindquist">Erika Lindquist</name><name sortKey="Pennacchio, Christa" sort="Pennacchio, Christa" uniqKey="Pennacchio C" first="Christa" last="Pennacchio">Christa Pennacchio</name><name sortKey="Shah, Manesh B" sort="Shah, Manesh B" uniqKey="Shah M" first="Manesh B" last="Shah">Manesh B. Shah</name><name sortKey="Tschaplinski, Timothy J" sort="Tschaplinski, Timothy J" uniqKey="Tschaplinski T" first="Timothy J" last="Tschaplinski">Timothy J. Tschaplinski</name><name sortKey="Tuskan, Gerald A" sort="Tuskan, Gerald A" uniqKey="Tuskan G" first="Gerald A" last="Tuskan">Gerald A. Tuskan</name></noCountry><country name="États-Unis"><region name="Tennessee"><name sortKey="Yang, Xiaohan" sort="Yang, Xiaohan" uniqKey="Yang X" first="Xiaohan" last="Yang">Xiaohan Yang</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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