Advances and perspectives in computational prediction of microbial gene essentiality
Theminimal subset of genes required for cellular growth, survival and viability of an organismare classified as essential genes. Knowledge of essential genes gives insight into the core structure and functioning of a cell. Thismight lead tomore efficient antimicrobial drug discovery, to elucidation...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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OXFORD UNIV PRESS
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/80734 |
| _version_ | 1848764261203968000 |
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| author | Mobegi, Fredrick Zomer, A. de Jonge, M.I. van Hijum, S.A.F.T. |
| author_facet | Mobegi, Fredrick Zomer, A. de Jonge, M.I. van Hijum, S.A.F.T. |
| author_sort | Mobegi, Fredrick |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Theminimal subset of genes required for cellular growth, survival and viability of an organismare classified as essential genes. Knowledge of essential genes gives insight into the core structure and functioning of a cell. Thismight lead tomore efficient antimicrobial drug discovery, to elucidation of the correlations between genotype and phenotype, and a better understanding of theminimal requirements for a (synthetic) cell. Traditionally, constructing a catalog of essential genes for a given microbe involved costly and time-consuming laboratory experiments. While experimentalmethods have produced abundant gene essentiality data formodel organisms like Escherichia coli and Bacillus subtilis, the knowledge generated cannot automatically be extrapolated to predict essential genes in all bacteria. In addition, essential genes identified in the laboratory are by definition 'conditionally essential', as they are essential under the specified experimental conditions: these might not resemble conditions in themicroorganisms' natural habitat(s). Also, large-scale experimental assaying for essential genes is not always feasible because of the time investment required to setup these assays. The ability to rapidly and precisely identify essential genes in silico is therefore important and has great potential for applications inmedicine, biotechnology and basic biological research. Here, we review the advancesmade in the use of computationalmethods to predictmicrobial gene essentiality, perspectives for the future of these techniques and the possible practical applications of essential genes. |
| first_indexed | 2025-11-14T11:16:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-80734 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:16:32Z |
| publishDate | 2017 |
| publisher | OXFORD UNIV PRESS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-807342021-01-15T04:29:11Z Advances and perspectives in computational prediction of microbial gene essentiality Mobegi, Fredrick Zomer, A. de Jonge, M.I. van Hijum, S.A.F.T. Science & Technology Life Sciences & Biomedicine Biotechnology & Applied Microbiology Genetics & Heredity gene essentiality prediction computational methods homology transposons next-generation sequencing MULTIPLE SEQUENCE ALIGNMENT GLOBAL TRANSPOSON MUTAGENESIS CLUSTAL-W GENOME IDENTIFICATION PHENOTYPE NETWORK RECONSTRUCTION LOCALIZATION OPTIMIZATION Theminimal subset of genes required for cellular growth, survival and viability of an organismare classified as essential genes. Knowledge of essential genes gives insight into the core structure and functioning of a cell. Thismight lead tomore efficient antimicrobial drug discovery, to elucidation of the correlations between genotype and phenotype, and a better understanding of theminimal requirements for a (synthetic) cell. Traditionally, constructing a catalog of essential genes for a given microbe involved costly and time-consuming laboratory experiments. While experimentalmethods have produced abundant gene essentiality data formodel organisms like Escherichia coli and Bacillus subtilis, the knowledge generated cannot automatically be extrapolated to predict essential genes in all bacteria. In addition, essential genes identified in the laboratory are by definition 'conditionally essential', as they are essential under the specified experimental conditions: these might not resemble conditions in themicroorganisms' natural habitat(s). Also, large-scale experimental assaying for essential genes is not always feasible because of the time investment required to setup these assays. The ability to rapidly and precisely identify essential genes in silico is therefore important and has great potential for applications inmedicine, biotechnology and basic biological research. Here, we review the advancesmade in the use of computationalmethods to predictmicrobial gene essentiality, perspectives for the future of these techniques and the possible practical applications of essential genes. 2017 Journal Article http://hdl.handle.net/20.500.11937/80734 10.1093/bfgp/elv063 English OXFORD UNIV PRESS restricted |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Biotechnology & Applied Microbiology Genetics & Heredity gene essentiality prediction computational methods homology transposons next-generation sequencing MULTIPLE SEQUENCE ALIGNMENT GLOBAL TRANSPOSON MUTAGENESIS CLUSTAL-W GENOME IDENTIFICATION PHENOTYPE NETWORK RECONSTRUCTION LOCALIZATION OPTIMIZATION Mobegi, Fredrick Zomer, A. de Jonge, M.I. van Hijum, S.A.F.T. Advances and perspectives in computational prediction of microbial gene essentiality |
| title | Advances and perspectives in computational prediction of microbial gene essentiality |
| title_full | Advances and perspectives in computational prediction of microbial gene essentiality |
| title_fullStr | Advances and perspectives in computational prediction of microbial gene essentiality |
| title_full_unstemmed | Advances and perspectives in computational prediction of microbial gene essentiality |
| title_short | Advances and perspectives in computational prediction of microbial gene essentiality |
| title_sort | advances and perspectives in computational prediction of microbial gene essentiality |
| topic | Science & Technology Life Sciences & Biomedicine Biotechnology & Applied Microbiology Genetics & Heredity gene essentiality prediction computational methods homology transposons next-generation sequencing MULTIPLE SEQUENCE ALIGNMENT GLOBAL TRANSPOSON MUTAGENESIS CLUSTAL-W GENOME IDENTIFICATION PHENOTYPE NETWORK RECONSTRUCTION LOCALIZATION OPTIMIZATION |
| url | http://hdl.handle.net/20.500.11937/80734 |