Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families.
Identifieur interne : 000287 ( PubMed/Curation ); précédent : 000286; suivant : 000288Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families.
Auteurs : Zongyu Wang [République populaire de Chine] ; Wenying He [République populaire de Chine] ; Jijun Tang [République populaire de Chine] ; Fei Guo [République populaire de Chine]Source :
- Journal of chemical information and modeling [ 1549-960X ] ; 2020.
Abstract
Transcription factors (TFs) play a crucial role in controlling key cellular processes and responding to the environment. Yeast is a single-cell fungal organism that is a vital biological model organism for studying transcription and translation in basic biology. The transcriptional control process of yeast cells has been extensively calculated and studied using traditional methods and high-throughput technologies. However, the identities of transcription factors that regulate major functional categories of genes remain unknown. Due to the avalanche of biological data in the post-genomic era, it is an urgent need to develop automated computational methods to enable accurate identification of efficient transcription factor binding sites from the large number of candidates. In this paper, we analyzed high-resolution DNA-binding profiles and motifs for TFs, covering all possible contiguous 8-mers. First, we divided all 8-mer motifs into 16 various categories and selected all sorts of samples from each category by setting the threshold of E-score. Then, we employed five feature representation methods. Also, we adopted a total of four feature selection methods to filter out useless features. Finally, we used Extreme Gradient Boosting (XGBoost) as our base classifier and then utilized the one-vs-rest tactics to build 16 binary classifiers to solve this multiclassification problem. In the experiment, our method achieved the best performance with an overall accuracy of 79.72% and Mathew's correlation coefficient of 0.77. We found the similarity relationship among each category from different TF families and obtained sequence motif schematic diagrams via multiple sequence alignment. The complexity of DNA recognition may act as an important role in the evolution of gene regulation. Source codes are available at https://github.com/guofei-tju/tfbs.
DOI: 10.1021/acs.jcim.9b01012
PubMed: 31944107
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Chine</country><wicri:regionArea>School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350</wicri:regionArea></affiliation></author></analytic><series><title level="j">Journal of chemical information and modeling</title><idno type="eISSN">1549-960X</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Transcription factors (TFs) play a crucial role in controlling key cellular processes and responding to the environment. Yeast is a single-cell fungal organism that is a vital biological model organism for studying transcription and translation in basic biology. The transcriptional control process of yeast cells has been extensively calculated and studied using traditional methods and high-throughput technologies. However, the identities of transcription factors that regulate major functional categories of genes remain unknown. Due to the avalanche of biological data in the post-genomic era, it is an urgent need to develop automated computational methods to enable accurate identification of efficient transcription factor binding sites from the large number of candidates. In this paper, we analyzed high-resolution DNA-binding profiles and motifs for TFs, covering all possible contiguous 8-mers. First, we divided all 8-mer motifs into 16 various categories and selected all sorts of samples from each category by setting the threshold of E-score. Then, we employed five feature representation methods. Also, we adopted a total of four feature selection methods to filter out useless features. Finally, we used Extreme Gradient Boosting (XGBoost) as our base classifier and then utilized the one-vs-rest tactics to build 16 binary classifiers to solve this multiclassification problem. In the experiment, our method achieved the best performance with an overall accuracy of 79.72% and Mathew's correlation coefficient of 0.77. We found the similarity relationship among each category from different TF families and obtained sequence motif schematic diagrams via multiple sequence alignment. The complexity of DNA recognition may act as an important role in the evolution of gene regulation. Source codes are available at https://github.com/guofei-tju/tfbs.</div></front></TEI><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">31944107</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>23</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1549-960X</ISSN><JournalIssue CitedMedium="Internet"><Volume>60</Volume><Issue>3</Issue><PubDate><Year>2020</Year><Month>Mar</Month><Day>23</Day></PubDate></JournalIssue><Title>Journal of chemical information and modeling</Title><ISOAbbreviation>J Chem Inf Model</ISOAbbreviation></Journal><ArticleTitle>Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families.</ArticleTitle><Pagination><MedlinePgn>1876-1883</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1021/acs.jcim.9b01012</ELocationID><Abstract><AbstractText>Transcription factors (TFs) play a crucial role in controlling key cellular processes and responding to the environment. Yeast is a single-cell fungal organism that is a vital biological model organism for studying transcription and translation in basic biology. The transcriptional control process of yeast cells has been extensively calculated and studied using traditional methods and high-throughput technologies. However, the identities of transcription factors that regulate major functional categories of genes remain unknown. Due to the avalanche of biological data in the post-genomic era, it is an urgent need to develop automated computational methods to enable accurate identification of efficient transcription factor binding sites from the large number of candidates. In this paper, we analyzed high-resolution DNA-binding profiles and motifs for TFs, covering all possible contiguous 8-mers. First, we divided all 8-mer motifs into 16 various categories and selected all sorts of samples from each category by setting the threshold of E-score. Then, we employed five feature representation methods. Also, we adopted a total of four feature selection methods to filter out useless features. Finally, we used Extreme Gradient Boosting (XGBoost) as our base classifier and then utilized the one-vs-rest tactics to build 16 binary classifiers to solve this multiclassification problem. In the experiment, our method achieved the best performance with an overall accuracy of 79.72% and Mathew's correlation coefficient of 0.77. We found the similarity relationship among each category from different TF families and obtained sequence motif schematic diagrams via multiple sequence alignment. The complexity of DNA recognition may act as an important role in the evolution of gene regulation. 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