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dc.contributor.authorMobegi, Fredrick
dc.contributor.authorZomer, A.
dc.contributor.authorde Jonge, M.I.
dc.contributor.authorvan Hijum, S.A.F.T.
dc.date.accessioned2020-08-24T06:53:51Z
dc.date.available2020-08-24T06:53:51Z
dc.date.issued2017
dc.identifier.citationMobegi, F.M. and Zomer, A. and de Jonge, M.I. and van Hijum, S.A.F.T. 2017. Advances and perspectives in computational prediction of microbial gene essentiality. Briefings in Functional Genomics. 16 (2): pp. 70-79.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/80734
dc.identifier.doi10.1093/bfgp/elv063
dc.description.abstract

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.

dc.languageEnglish
dc.publisherOXFORD UNIV PRESS
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectBiotechnology & Applied Microbiology
dc.subjectGenetics & Heredity
dc.subjectgene essentiality prediction
dc.subjectcomputational methods
dc.subjecthomology
dc.subjecttransposons
dc.subjectnext-generation sequencing
dc.subjectMULTIPLE SEQUENCE ALIGNMENT
dc.subjectGLOBAL TRANSPOSON MUTAGENESIS
dc.subjectCLUSTAL-W
dc.subjectGENOME
dc.subjectIDENTIFICATION
dc.subjectPHENOTYPE
dc.subjectNETWORK
dc.subjectRECONSTRUCTION
dc.subjectLOCALIZATION
dc.subjectOPTIMIZATION
dc.titleAdvances and perspectives in computational prediction of microbial gene essentiality
dc.typeJournal Article
dcterms.source.volume16
dcterms.source.number2
dcterms.source.startPage70
dcterms.source.endPage79
dcterms.source.issn2041-2649
dcterms.source.titleBriefings in Functional Genomics
dc.date.updated2020-08-24T06:53:51Z
curtin.departmentSchool of Molecular and Life Sciences (MLS)
curtin.accessStatusIn process
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidMobegi, Fredrick [0000-0003-0554-9919]
curtin.contributor.researcheridMobegi, Fredrick [D-1058-2015]
dcterms.source.eissn2041-2657
curtin.contributor.scopusauthoridMobegi, Fredrick [56479121000]


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