Data provided by National Institute of Standards and Technology
McCandlish and Stoltzfus gathered data from deep mutational scanning experiments on 12 proteins, comprising 56641 distinct amino acid replacement mutations. By converting fitnesses to within-study quantiles, they combined results from all studies to draw general conclusions about distributions of fitness effects for the 380 different types of possible amino acid changes in proteins. They found that most replacements are neither conservative nor radical, but barely different from the background distribution. The shapes of these distributions can be approximated by a maximum-entropy model with only 1 parameter. This data package makes it possible to reproduce the main calculations used by Stoltzfus and McCandlish. The data also may be useful to researchers carrying out meta-analyses of mutation-scanning experiments or DFE experiments.
About this Dataset
Updated: 2024-02-22
Metadata Last Updated:
2018-09-18 00:00:00
Date Created:
N/A
Data Provided by:
Dataset Owner:
N/A
| Title | Supplementary data for "Distributions of fitness effects for amino acid changes from high-throughout mutagenesis experiments" (McCandlish and Stoltzfus, 2018) |
|---|---|
| Description | McCandlish and Stoltzfus gathered data from deep mutational scanning experiments on 12 proteins, comprising 56641 distinct amino acid replacement mutations. By converting fitnesses to within-study quantiles, they combined results from all studies to draw general conclusions about distributions of fitness effects for the 380 different types of possible amino acid changes in proteins. They found that most replacements are neither conservative nor radical, but barely different from the background distribution. The shapes of these distributions can be approximated by a maximum-entropy model with only 1 parameter. This data package makes it possible to reproduce the main calculations used by Stoltzfus and McCandlish. The data also may be useful to researchers carrying out meta-analyses of mutation-scanning experiments or DFE experiments. |
| Modified | 2018-09-18 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | mutation , deep mutational scanning , fitness , protein |
{
"identifier": "762AD4DA63D05927E05324570681D36C1970",
"accessLevel": "public",
"contactPoint": {
"hasEmail": "mailto:[email protected]",
"fn": "Arlin B. Stoltzfus"
},
"programCode": [
"006:045"
],
"landingPage": "https:\/\/data.nist.gov\/od\/id\/762AD4DA63D05927E05324570681D36C1970",
"title": "Supplementary data for \"Distributions of fitness effects for amino acid changes from high-throughout mutagenesis experiments\" (McCandlish and Stoltzfus, 2018)",
"description": "McCandlish and Stoltzfus gathered data from deep mutational scanning experiments on 12 proteins, comprising 56641 distinct amino acid replacement mutations. By converting fitnesses to within-study quantiles, they combined results from all studies to draw general conclusions about distributions of fitness effects for the 380 different types of possible amino acid changes in proteins. They found that most replacements are neither conservative nor radical, but barely different from the background distribution. The shapes of these distributions can be approximated by a maximum-entropy model with only 1 parameter. This data package makes it possible to reproduce the main calculations used by Stoltzfus and McCandlish. The data also may be useful to researchers carrying out meta-analyses of mutation-scanning experiments or DFE experiments.",
"language": [
"en"
],
"distribution": [
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/README.md.sha256",
"mediaType": "text\/plain"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/figure_s1.pdf",
"mediaType": "application\/pdf"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/figure_s1.pdf.sha256",
"mediaType": "text\/plain"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/ms2018_calculations.nb",
"mediaType": "application\/mathematica"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/ms2018_calculations.nb.sha256",
"mediaType": "text\/plain"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/all_obs.tsv",
"mediaType": "text\/tab-separated-values"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/all_obs.tsv.sha256",
"mediaType": "text\/plain"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/raw_obs.tsv",
"mediaType": "text\/tab-separated-values"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/raw_obs.tsv.sha256",
"mediaType": "text\/plain"
},
{
"accessURL": "https:\/\/doi.org\/10.18434\/M31970",
"title": "DOI Access for Supplementary data for \"Distributions of fitness effects for amino acid changes from high-throughout mutagenesis experiments\" (McCandlish and Stoltzfus, 2018)"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/762AD4DA63D05927E05324570681D36C1970\/README.md",
"mediaType": "text\/plain"
}
],
"bureauCode": [
"006:55"
],
"modified": "2018-09-18 00:00:00",
"publisher": {
"@type": "org:Organization",
"name": "National Institute of Standards and Technology"
},
"theme": [
"Mathematics and Statistics:Numerical methods and software",
"Chemistry:Molecular characterization",
"Bioscience:Engineering\/synthetic biology"
],
"keyword": [
"mutation",
"deep mutational scanning",
"fitness",
"protein"
]
}
