A statistical framework for analyzing deep mutational scanning data

Rubin, Alan F.; Gelman, Hannah; Lucas, Nathan J.; Bajjalieh, Sandra M.; Papenfuss, Anthony T.; Speed, Terence P.; Fowler, Douglas M.

doi:10.1186/s13059-017-1272-5

Cited by 185 publications

(247 citation statements)

References 49 publications

(81 reference statements)

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“…However, models for computing error estimates that consider sampling noise and the distribution of scores for variants between replicate experiments are available. 71,72 As the field moves forward, unification of data analysis and error-estimation methods will enable comparisons of data quality across MAVEs just as it has for earlier technologies. 73 Fifth, interpretation of MAVE results can be complicated by interactions between variants in different functional elements or between variants and environmental effects.…”

Section: Limitations Of Maves and How To Overcome Themmentioning

confidence: 99%

Variant Interpretation: Functional Assays to the Rescue

Starita

Ahituv

Dunham

et al. 2017

The American Journal of Human Genetics

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Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of ''lookup tables'' of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.

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Section: Limitations Of Maves and How To Overcome Themmentioning

confidence: 99%

Variant Interpretation: Functional Assays to the Rescue

Starita

Ahituv

Dunham

et al. 2017

The American Journal of Human Genetics

Self Cite

302

294

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“…The fitness values for the mutations common across all replicates correlated strongly (Appendix Fig S4) (Pearson's correlation coefficient between 0.81 and 0.98). We averaged the fitness scores for common mutations across replicates using the Fisher scoring iteration‐based maximum‐likelihood estimates (Materials and Methods; Rubin et al , ). We observed a bimodal distribution of fitness effects for all mutations at each rifampicin concentration (Fig E).…”

Section: Resultsmentioning

confidence: 99%

“…Finally, we wrote a custom code to extract information about the nucleotide and amino acid changes corresponding to each qrowdot output. Fitness calculations for resistance to rifampicin : Sample code for calculating the resistance to rifampicin has been provided ( https://github.com/Alaksh/CREPE-Analysis-Code). Fitness calculations for resistance to rifampicin with each replicate were estimated using the two time point enrichment score calculation algorithm described previously (Rubin et al , ). The fitness for each variant was estimated as follows:

fitness, f = \log (\frac{C_{normali, normalsel} + 0.5}{C_{normalwt, normalsel} + 0.5}) - \log (\frac{C_{normali, normalinput} + 0.5}{C_{normalwt, normalinput} + 0.5})

where C i is the total count for a variant “i” in the library and C wt is the total count for the wild‐type reference in the library.…”

Section: Methodsmentioning

confidence: 99%

“…2 Fitness calculations for resistance to rifampicin: Sample code for calculating the resistance to rifampicin has been provided (https://github.com/Alaksh/CREPE-Analysis-Code). Fitness calculations for resistance to rifampicin with each replicate were estimated using the two time point enrichment score calculation algorithm described previously (Rubin et al, 2017). The fitness for each variant was estimated as follows:…”

Section: Crepe: Data Analysismentioning

confidence: 99%

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CRISPR /Cas9 recombineering‐mediated deep mutational scanning of essential genes in Escherichia coli

Choudhury

Fenster

Fankhauser

et al. 2020

Molecular Systems Biology

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Deep mutational scanning can provide significant insights into the function of essential genes in bacteria. Here, we developed a high‐throughput method for mutating essential genes of Escherichia coli in their native genetic context. We used Cas9‐mediated recombineering to introduce a library of mutations, created by error‐prone PCR, within a gene fragment on the genome using a single gRNA pre‐validated for high efficiency. Tracking mutation frequency through deep sequencing revealed biases in the position and the number of the introduced mutations. We overcame these biases by increasing the homology arm length and blocking mismatch repair to achieve a mutation efficiency of 85% for non‐essential genes and 55% for essential genes. These experiments also improved our understanding of poorly characterized recombineering process using dsDNA donors with single nucleotide changes. Finally, we applied our technology to target rpoB, the beta subunit of RNA polymerase, to study resistance against rifampicin. In a single experiment, we validate multiple biochemical and clinical observations made in the previous decades and provide insights into resistance compensation with the study of double mutants.

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“…Experimental mutational sensitivity information is now available for multiple proteins . Deep mutational scanning has also been used to study protein‐protein interactions and to affinity rank peptide ligands for receptor proteins .…”

Section: Discussionmentioning

confidence: 99%

Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information

et al. 2019

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Structure prediction methods often generate a large number of models for a target sequence. Even if the correct fold for the target sequence is sampled in this dataset, it is difficult to distinguish it from other decoy structures. An attempt to solve this problem using experimental mutational sensitivity data for the CcdB protein was described previously by exploiting the correlation of residue depth with mutational sensitivity (r ~ 0.6). We now show that such a correlation extends to four other proteins with localized active sites, and for which saturation mutagenesis datasets exist. We also examine whether incorporation of predicted secondary structure information and the DOPE model quality assessment score, in addition to mutational sensitivity, improves the accuracy of model discrimination using a decoy dataset of 163 targets from CASP. Although most CASP models would have been subjected to model quality assessment prior to submission, we find that the DOPE score makes a substantial contribution to the observed improvement. We therefore also applied the approach to CcdB and four other proteins for which reliable experimental mutational data exist and observe that inclusion of experimental mutational data results in a small qualitative improvement in model discrimination relative to that seen with just the DOPE score. This is largely because of our limited ability to quantitatively predict effects of point mutations on in vivo protein activity. Further improvements in the methodology are required to facilitate improved utilization of single mutant data.

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A statistical framework for analyzing deep mutational scanning data

Cited by 185 publications

References 49 publications

Variant Interpretation: Functional Assays to the Rescue

Variant Interpretation: Functional Assays to the Rescue

CRISPR /Cas9 recombineering‐mediated deep mutational scanning of essential genes in Escherichia coli

Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information

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