De novo computational prediction of non-coding RNA genes in prokaryotic genomes
Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with kn...
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| Published in | Bioinformatics Vol. 25; no. 22; pp. 2897 - 2905 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
15.11.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1460-2059 1367-4811 |
| DOI | 10.1093/bioinformatics/btp537 |
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| Abstract | Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues. Results: We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation. Availability: The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/. Contact: xyn@bmb.uga.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
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| AbstractList | Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.
Results: We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation.
Availability: The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/.
Contact: xyn@bmb.uga.edu
Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues. Results: We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation. Availability: The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/. Contact: xyn@bmb.uga.edu Supplementary information: Supplementary data are available at Bioinformatics online. The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues. We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation. The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/. Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues. Results: We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation. Availability: The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/. Contact: xyn@bmb.uga.edu Supplementary information: Supplementary data are available at Bioinformatics online. The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.MOTIVATIONThe computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation.RESULTSWe present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation.The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/.AVAILABILITYThe source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/. |
| Author | Xu, Ying Stead, Mark Zhou, Fengfeng Kushner, Sidney R. Marshburn, Sarah Tran, Thao T. |
| AuthorAffiliation | 1 School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 2 Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), 3 Department of Genetics, University of Georgia, Athens, GA, USA and 4 College of Computer Science and Technology, Jilin University, Changchun, China |
| AuthorAffiliation_xml | – name: 1 School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 2 Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), 3 Department of Genetics, University of Georgia, Athens, GA, USA and 4 College of Computer Science and Technology, Jilin University, Changchun, China |
| Author_xml | – sequence: 1 givenname: Thao T. surname: Tran fullname: Tran, Thao T. organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), Department of Genetics, University of Georgia, Athens, GA, USA and College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 2 givenname: Fengfeng surname: Zhou fullname: Zhou, Fengfeng organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), Department of Genetics, University of Georgia, Athens, GA, USA and College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 3 givenname: Sarah surname: Marshburn fullname: Marshburn, Sarah organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), Department of Genetics, University of Georgia, Athens, GA, USA and College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 4 givenname: Mark surname: Stead fullname: Stead, Mark organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), Department of Genetics, University of Georgia, Athens, GA, USA and College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 5 givenname: Sidney R. surname: Kushner fullname: Kushner, Sidney R. organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), Department of Genetics, University of Georgia, Athens, GA, USA and College of Computer Science and Technology, Jilin University, Changchun, China – sequence: 6 givenname: Ying surname: Xu fullname: Xu, Ying organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics and BioEnergy Science Center (BESC), Department of Genetics, University of Georgia, Athens, GA, USA and College of Computer Science and Technology, Jilin University, Changchun, China |
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| Keywords | Gene RNA Genome Coding De novo Prediction |
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| Snippet | Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational... The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology.... |
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| SubjectTerms | Algorithms Biological and medical sciences Computational Biology - methods Databases, Genetic Escherichia coli - genetics Fundamental and applied biological sciences. Psychology General aspects Genome, Bacterial Genomics - methods Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Operon Original Papers RNA, Bacterial - chemistry RNA, Untranslated - chemistry |
| Title | De novo computational prediction of non-coding RNA genes in prokaryotic genomes |
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