Prediction of fine-tuned promoter activity from DNA sequence.
Identifieur interne : 001072 ( PubMed/Corpus ); précédent : 001071; suivant : 001073Prediction of fine-tuned promoter activity from DNA sequence.
Auteurs : Geoffrey Siwo ; Andrew Rider ; Asako Tan ; Richard Pinapati ; Scott Emrich ; Nitesh Chawla ; Michael FerdigSource :
- F1000Research [ 2046-1402 ] ; 2016.
Abstract
The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems.
DOI: 10.12688/f1000research.7485.1
PubMed: 27347373
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Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA; IBM TJ Watson Research Center, NY, USA; IBM Research-Africa, Johannesberg, South Africa.</nlm:affiliation></affiliation></author><author><name sortKey="Rider, Andrew" sort="Rider, Andrew" uniqKey="Rider A" first="Andrew" last="Rider">Andrew Rider</name><affiliation><nlm:affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Tan, Asako" sort="Tan, Asako" uniqKey="Tan A" first="Asako" last="Tan">Asako Tan</name><affiliation><nlm:affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Epicentre, Madison, WI, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Pinapati, Richard" sort="Pinapati, Richard" uniqKey="Pinapati R" first="Richard" last="Pinapati">Richard Pinapati</name><affiliation><nlm:affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Emrich, Scott" sort="Emrich, Scott" uniqKey="Emrich S" first="Scott" last="Emrich">Scott Emrich</name><affiliation><nlm:affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Chawla, Nitesh" sort="Chawla, Nitesh" uniqKey="Chawla N" first="Nitesh" last="Chawla">Nitesh Chawla</name><affiliation><nlm:affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Ferdig, Michael" sort="Ferdig, Michael" uniqKey="Ferdig M" first="Michael" last="Ferdig">Michael Ferdig</name><affiliation><nlm:affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</nlm:affiliation></affiliation></author></analytic><series><title level="j">F1000Research</title><idno type="ISSN">2046-1402</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems. </div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">27347373</PMID><DateCompleted><Year>2016</Year><Month>06</Month><Day>27</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>06</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">2046-1402</ISSN><JournalIssue CitedMedium="Print"><Volume>5</Volume><PubDate><Year>2016</Year></PubDate></JournalIssue><Title>F1000Research</Title><ISOAbbreviation>F1000Res</ISOAbbreviation></Journal><ArticleTitle>Prediction of fine-tuned promoter activity from DNA sequence.</ArticleTitle><Pagination><MedlinePgn>158</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.12688/f1000research.7485.1</ELocationID><Abstract><AbstractText>The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Siwo</LastName><ForeName>Geoffrey</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA; IBM TJ Watson Research Center, NY, USA; IBM Research-Africa, Johannesberg, South Africa.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rider</LastName><ForeName>Andrew</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tan</LastName><ForeName>Asako</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Epicentre, Madison, WI, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pinapati</LastName><ForeName>Richard</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Emrich</LastName><ForeName>Scott</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chawla</LastName><ForeName>Nitesh</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ferdig</LastName><ForeName>Michael</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA; Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>T32 GM075762</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>02</Month><Day>11</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>F1000Res</MedlineTA><NlmUniqueID>101594320</NlmUniqueID><ISSNLinking>2046-1402</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">DNA sequence</Keyword><Keyword MajorTopicYN="N">DREAM challenges</Keyword><Keyword MajorTopicYN="N">Expression prediction</Keyword><Keyword MajorTopicYN="N">Gene expression</Keyword><Keyword MajorTopicYN="N">Gene regulation</Keyword><Keyword MajorTopicYN="N">Machine learning</Keyword><Keyword MajorTopicYN="N">Promoter activity</Keyword><Keyword MajorTopicYN="N">Transcription modeling</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>02</Month><Day>08</Day></PubMedPubDate><PubMedPubDate 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HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Corpus/RBID.i -Sk "pubmed:27347373" \
| HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Corpus/biblio.hfd \
| NlmPubMed2Wicri -a MersV1
| This area was generated with Dilib version V0.6.33. |  |