Locating Protein-Coding Regions in Human DNA Sequences by a Multiple Sensor-Neural Network Approach
Genes in higher eukaryotes may span tens or hundreds of kilobases with the protein-coding regions accounting for only a few percent of the total sequence. Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We...
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| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 88; no. 24; pp. 11261 - 11265 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Washington, DC
National Academy of Sciences of the United States of America
15.12.1991
National Acad Sciences |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0027-8424 1091-6490 |
| DOI | 10.1073/pnas.88.24.11261 |
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| Abstract | Genes in higher eukaryotes may span tens or hundreds of kilobases with the protein-coding regions accounting for only a few percent of the total sequence. Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a reliable computational approach for locating protein-coding portions of genes in anonymous DNA sequence. Using a concept suggested by robotic environmental sensing, our method combines a set of sensor algorithms and a neural network to localize the coding regions. Several algorithms that report local characteristics of the DNA sequence, and therefore act as sensors, are also described. In its current configuration the "coding recognition module" identifies 90% of coding exons of length 100 bases or greater with less than one false positive coding exon indicated per five coding exons indicated. This is a significantly lower false positive rate than any method of which we are aware. This module demonstrates a method with general applicability to sequence-pattern recognition problems and is available for current research efforts. |
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| AbstractList | Genes in higher eukaryotes may span tens or hundreds of kilobases with the protein-coding regions accounting for only a few percent of the total sequence. Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a reliable computational approach for locating protein-coding portions of genes in anonymous DNA sequence. Using a concept suggested by robotic environmental sensing, our method combines a set of sensor algorithms and a neural network to localize the coding regions. Several algorithms that report local characteristics of the DNA sequence, and therefore act as sensors, are also described. In its current configuration the "coding recognition module" identifies 90% of coding exons of length 100 bases or greater with less than one false positive coding exon indicated per five coding exons indicated. This is a significantly lower false positive rate than any method of which we are aware. This module demonstrates a method with general applicability to sequence-pattern recognition problems and is available for current research efforts.Genes in higher eukaryotes may span tens or hundreds of kilobases with the protein-coding regions accounting for only a few percent of the total sequence. Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a reliable computational approach for locating protein-coding portions of genes in anonymous DNA sequence. Using a concept suggested by robotic environmental sensing, our method combines a set of sensor algorithms and a neural network to localize the coding regions. Several algorithms that report local characteristics of the DNA sequence, and therefore act as sensors, are also described. In its current configuration the "coding recognition module" identifies 90% of coding exons of length 100 bases or greater with less than one false positive coding exon indicated per five coding exons indicated. This is a significantly lower false positive rate than any method of which we are aware. This module demonstrates a method with general applicability to sequence-pattern recognition problems and is available for current research efforts. Genes in higher eukaryotes may span tens or hundreds of kilobases with the protein-coding regions accounting for only a few percent of the total sequence. Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a reliable computational approach for locating protein-coding portions of genes in anonymous DNA sequence. Using a concept suggested by robotic environmental sensing, our method combines a set of sensor algorithms and a neural network to localize the coding regions. Several algorithms that report local characteristics of the DNA sequence, and therefore act as sensors, are also described. Genes in higher eukaryotes may span tens or hundreds of kilobases with the protein-coding regions accounting for only a few percent of the total sequence. Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. The authors describe a reliable computational approach for locating protein-coding portions of genes in anonymous DNA sequence. Using a concept suggested by robotic environmental sensing, the authors method combines a set of sensor algorithms and a neural network to localize the coding regions. Several algorithms that report local characteristics of the DNA sequence, and therefore act as sensors, are also described. In its current configuration the coding recognition module identifies 90% of coding exons of length 100 bases or greater with less than one false positive coding exon indicated per five coding exons indicated. This is a significantly lower false positive rate than any method of which the authors are aware. This module demonstrates a method with general applicability to sequence-pattern recognition problems and is available for current research efforts. Genes in higher eukaryotes may span tens or hundreds of kilobases with the protein-coding regions accounting for only a few percent of the total sequence. Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a reliable computational approach for locating protein-coding portions of genes in anonymous DNA sequence. Using a concept suggested by robotic environmental sensing, our method combines a set of sensor algorithms and a neural network to localize the coding regions. Several algorithms that report local characteristics of the DNA sequence, and therefore act as sensors, are also described. In its current configuration the "coding recognition module" identifies 90% of coding exons of length 100 bases or greater with less than one false positive coding exon indicated per five coding exons indicated. This is a significantly lower false positive rate than any method of which we are aware. This module demonstrates a method with general applicability to sequence-pattern recognition problems and is available for current research efforts. |
| Author | Edward C. Uberbacher Richard J. Mural |
| AuthorAffiliation | Biology Division, Oak Ridge National Laboratory, TN |
| AuthorAffiliation_xml | – name: Biology Division, Oak Ridge National Laboratory, TN |
| Author_xml | – sequence: 1 givenname: E C surname: Uberbacher fullname: Uberbacher, E C organization: Biology Division, Oak Ridge National Laboratory, TN – sequence: 2 givenname: R J surname: Mural fullname: Mural, R J organization: Biology Division, Oak Ridge National Laboratory, TN |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=5225985$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/1763041$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/5604872$$D View this record in Osti.gov |
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| Issue | 24 |
| Keywords | Human Exon Nucleotide sequence Localization DNA Recognition |
| Language | English |
| License | CC BY 4.0 |
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| PublicationTitle | Proceedings of the National Academy of Sciences - PNAS |
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| Snippet | Genes in higher eukaryotes may span tens or hundreds of kilobases with the protein-coding regions accounting for only a few percent of the total sequence.... |
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| SubjectTerms | 550200 - Biochemistry Algorithms ALKALINE PHOSPHATASE ANIMALS Base Sequence BASIC BIOLOGICAL SCIENCES Biological and medical sciences BIOLOGICAL MARKERS BLOOD COAGULATION FACTORS Chromosome Mapping Chromosomes, Human COAGULANTS coding Databases, Factual DNA DNA - genetics DNA SEQUENCING DRUGS ENZYMES Enzymes - genetics ESTERASES Exons False positive errors Fundamental and applied biological sciences. Psychology GENES Genes, ras genetics HEMATOLOGIC AGENTS Hominidae - genetics Humans HYDROLASES MAMMALS MAN Models, Genetic Molecular and cellular biology Molecular genetics Molecular Sequence Data MOLECULAR STRUCTURE Murals neural networks Neural Networks (Computer) Nucleic acids Nucleotide sequences Open reading frames ORGANIC COMPOUNDS PATTERN RECOGNITION PHOSPHATASES PHOSPHORUS-GROUP TRANSFERASES PHOSPHOTRANSFERASES PRIMATES PROTEINS Proteins - genetics PROTHROMBIN Reading frames Sensors STRUCTURAL CHEMICAL ANALYSIS TRANSFERASES VERTEBRATES |
| Title | Locating Protein-Coding Regions in Human DNA Sequences by a Multiple Sensor-Neural Network Approach |
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