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dc.contributor.authorHietpas, Ryan T.
dc.contributor.authorRoscoe, Benjamin P.
dc.contributor.authorJiang, Li
dc.contributor.authorBolon, Daniel N. A.
dc.date2022-08-11T08:08:55.000
dc.date.accessioned2022-08-23T16:12:02Z
dc.date.available2022-08-23T16:12:02Z
dc.date.issued2012-07-21
dc.date.submitted2013-09-24
dc.identifier.citation<p>Nat Protoc. 2012 Jun 21;7(7):1382-96. doi: 10.1038/nprot.2012.069. <a href="http://dx.doi.org/10.1038/nprot.2012.069" target="_blank">Link to article on publisher's site</a></p>
dc.identifier.issn1750-2799
dc.identifier.doi10.1038/nprot.2012.069
dc.identifier.pmid22722372
dc.identifier.urihttp://hdl.handle.net/20.500.14038/33309
dc.description.abstractDeep sequencing can accurately measure the relative abundance of hundreds of mutations in a single bulk competition experiment, which can give a direct readout of the fitness of each mutant. Here we describe a protocol that we previously developed and optimized to measure the fitness effects of all possible individual codon substitutions for 10-aa regions of essential genes in yeast. Starting with a conditional strain (i.e., a temperature-sensitive strain), we describe how to efficiently generate plasmid libraries of point mutants that can then be transformed to generate libraries of yeast. The yeast libraries are competed under conditions that select for mutant function. Deep-sequencing analyses are used to determine the relative fitness of all mutants. This approach is faster and cheaper per mutant compared with analyzing individually isolated mutants. The protocol can be performed in ∼4 weeks and many 10-aa regions can be analyzed in parallel.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22722372&dopt=Abstract">Link to article in PubMed</a>
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3509169/pdf/nihms419571.pdf
dc.subjectCodon; Gene Library; Genes, Essential; Genes, Fungal; Genetic Fitness; Genetic Techniques; High-Throughput Nucleotide Sequencing; Oligonucleotides; Point Mutation; Yeasts
dc.subjectComputational Biology
dc.subjectGenetics
dc.subjectMolecular Genetics
dc.titleFitness analyses of all possible point mutations for regions of genes in yeast
dc.typeJournal Article
dc.source.journaltitleNat Protoc
dc.source.volume7
dc.source.issue7
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/gsbs_sp/1838
dc.identifier.contextkey4618807
html.description.abstract<p>Deep sequencing can accurately measure the relative abundance of hundreds of mutations in a single bulk competition experiment, which can give a direct readout of the fitness of each mutant. Here we describe a protocol that we previously developed and optimized to measure the fitness effects of all possible individual codon substitutions for 10-aa regions of essential genes in yeast. Starting with a conditional strain (i.e., a temperature-sensitive strain), we describe how to efficiently generate plasmid libraries of point mutants that can then be transformed to generate libraries of yeast. The yeast libraries are competed under conditions that select for mutant function. Deep-sequencing analyses are used to determine the relative fitness of all mutants. This approach is faster and cheaper per mutant compared with analyzing individually isolated mutants. The protocol can be performed in ∼4 weeks and many 10-aa regions can be analyzed in parallel.</p>
dc.identifier.submissionpathgsbs_sp/1838
dc.contributor.departmentDepartment of Biochemistry and Molecular Pharmacology
dc.source.pages1382-96
dc.contributor.studentRyan T. Hietpas


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