Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Fitness analyses of all possible point mutations for regions of genes in yeast


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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Bulk competition of libraries of point mutants in yeast.
Figure 2: Steps to generate plasmid libraries of point mutants.
Figure 3: Steps to prepare DNA for deep sequencing.
Figure 4: Analysis pipeline for measuring fitness effects of mutations from deep-sequencing data.

Similar content being viewed by others


  1. Hietpas, R.T., Jensen, J.D. & Bolon, D.N. Experimental illumination of a fitness landscape. Proc. Natl. Acad. Sci. USA 108, 7896–7901 (2011).

    Article  CAS  Google Scholar 

  2. Pool, J.E., Hellmann, I., Jensen, J.D. & Nielsen, R. Population genetic inference from genomic sequence variation. Genome Res. 20, 291–300 (2010).

    Article  CAS  Google Scholar 

  3. Jensen, J.D., Wong, A. & Aquadro, C.F. Approaches for identifying targets of positive selection. Trends Genet. 23, 568–577 (2007).

    Article  CAS  Google Scholar 

  4. Hegreness, M., Shoresh, N., Hartl, D. & Kishony, R. An equivalence principle for the incorporation of favorable mutations in asexual populations. Science 311, 1615–1617 (2006).

    Article  CAS  Google Scholar 

  5. Lind, P.A., Berg, O.G. & Andersson, D.I. Mutational robustness of ribosomal protein genes. Science 330, 825–827 (2010).

    Article  CAS  Google Scholar 

  6. Smith, J.M. & Haigh, J. The hitch-hiking effect of a favourable gene. Genet. Res. 23, 23–35 (1974).

    Article  CAS  Google Scholar 

  7. Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–91 (2002).

    Article  CAS  Google Scholar 

  8. Weinreich, D.M., Delaney, N.F., Depristo, M.A. & Hartl, D.L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006).

    Article  CAS  Google Scholar 

  9. Lenski, R.E. Quantifying fitness and gene stability in microorganisms. Biotechnology 15, 173–192 (1991).

    CAS  PubMed  Google Scholar 

  10. Cunningham, B.C. & Wells, J.A. High-resolution epitope mapping of hGH-receptor interactions by alanine-scanning mutagenesis. Science 244, 1081–1085 (1989).

    Article  CAS  Google Scholar 

  11. Fowler, D.M. et al. High-resolution mapping of protein sequence-function relationships. Nat. Methods 7, 741–746 (2010).

    Article  CAS  Google Scholar 

  12. Pitt, J.N. & Ferre-D'Amare, A.R. Rapid construction of empirical RNA fitness landscapes. Science 330, 376–379 (2010).

    Article  CAS  Google Scholar 

  13. Ernst, A. et al. Coevolution of PDZ domain-ligand interactions analyzed by high-throughput phage display and deep sequencing. Mol. Biosyst. 6, 1782–1790 (2010).

    Article  CAS  Google Scholar 

  14. Wach, A., Brachat, A., Pohlmann, R. & Philippsen, P. New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae. Yeast 10, 1793–1808 (1994).

    Article  CAS  Google Scholar 

  15. Tsalik, E.L. & Gartenberg, M.R. Curing Saccharomyces cerevisiae of the 2 micron plasmid by targeted DNA damage. Yeast 14, 847–852 (1998).

    Article  CAS  Google Scholar 

  16. Guthrie, C. & Fink, G.R. Guide to Yeast Genetics and Molecular and Cell Biology Vol. 350, Part B (Methods in Enzymology) (Academic Press, 2002).

  17. Gietz, R.D., Schiestl, R.H., Willems, A.R. & Woods, R.A. Studies on the transformation of intact yeast cells by the LiAc/SS-DNA/PEG procedure. Yeast 11, 355–360 (1995).

    Article  CAS  Google Scholar 

  18. Gietz, R.D. & Schiestl, R.H. Large-scale high-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 38–41 (2007).

    Article  CAS  Google Scholar 

  19. Scanlon, T.C., Gray, E.C. & Griswold, K.E. Quantifying and resolving multiple vector transformants in S. cerevisiae plasmid libraries. BMC Biotechnol. 9, 95 (2009).

    Article  Google Scholar 

  20. Johnston, M. & Davis, R.W. Sequences that regulate the divergent GAL1-GAL10 promoter in Saccharomyces cerevisiae. Mol. Cell Biol. 4, 1440–1448 (1984).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Cock, P.J., Fields, C.J., Goto, N., Heuer, M.L. & Rice, P.M. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 38, 1767–1771 (2009).

    Article  Google Scholar 

  22. Fowler, D.M., Araya, C.L., Gerard, W. & Fields, S. Enrich: software for analysis of protein function by enrichment and depletion of variants. Bioinformatics 27, 3430–3431 (2011).

    Article  CAS  Google Scholar 

  23. Pitt, J.N., Rajapakse, I. & Ferre-D'Amare, A.R. SEWAL: an open-source platform for next-generation sequence analysis and visualization. Nucleic Acids Res. 38, 7908–7915 (2010).

    Article  CAS  Google Scholar 

Download references


This work was supported in part by grants from the US National Institutes of Health (R01-GM083038) and the American Cancer Society (RSG-08–17301-GMC) to D.N.A.B.

Author information

Authors and Affiliations



R.H., B.R., L.J. and D.N.A.B. all contributed to the development and optimization of the protocol and writing the article. R.H. prepared the initial draft for the section on generating mutant libraries. B.R. prepared the initial draft for the section on growth competition. B.R. and L.J. prepared the initial draft for the section on preparing samples for deep sequencing. D.N.A.B. prepared the initial draft for the section on processing sequencing data. D.N.A.B. supervised the work and prepared the final version of the manuscript.

Corresponding author

Correspondence to Daniel N A Bolon.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Features and sequence of bacterial-yeast shuttle plasmid pRNDM. This plasmid was derived from pRS414 with the tryptophan marker replaced by KanMX4 and the beta-lactamase gene removed. (PDF 48 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hietpas, R., Roscoe, B., Jiang, L. et al. Fitness analyses of all possible point mutations for regions of genes in yeast. Nat Protoc 7, 1382–1396 (2012).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing