Representativeness of variation benchmark datasets
Schaafsma, Gerard C P LU
and Vihinen, Mauno
LU
(2018)
In BMC Bioinformatics
19(1).
p.461-461
- Abstract
BACKGROUND: Benchmark datasets are essential for both method development and performance assessment. These datasets have numerous requirements, representativeness being one. In the case of variant tolerance/pathogenicity prediction, representativeness means that the dataset covers the space of variations and their effects.
RESULTS: We performed the first analysis of the representativeness of variation benchmark datasets. We used statistical approaches to investigate how proteins in the benchmark datasets were representative for the entire human protein universe. We investigated the distributions of variants in chromosomes, protein structures, CATH domains and classes, Pfam protein families, Enzyme Commission (EC) classifications... (More)
BACKGROUND: Benchmark datasets are essential for both method development and performance assessment. These datasets have numerous requirements, representativeness being one. In the case of variant tolerance/pathogenicity prediction, representativeness means that the dataset covers the space of variations and their effects.
RESULTS: We performed the first analysis of the representativeness of variation benchmark datasets. We used statistical approaches to investigate how proteins in the benchmark datasets were representative for the entire human protein universe. We investigated the distributions of variants in chromosomes, protein structures, CATH domains and classes, Pfam protein families, Enzyme Commission (EC) classifications and Gene Ontology annotations in 24 datasets that have been used for training and testing variant tolerance prediction methods. All the datasets were available in VariBench or VariSNP databases. We tested also whether the pathogenic variant datasets contained neutral variants defined as those that have high minor allele frequency in the ExAC database. The distributions of variants over the chromosomes and proteins varied greatly between the datasets.
CONCLUSIONS: None of the datasets was found to be well representative. Many of the tested datasets had quite good coverage of the different protein characteristics. Dataset size correlates to representativeness but only weakly to the performance of methods trained on them. The results imply that dataset representativeness is an important factor and should be taken into account in predictor development and testing.
(Less)
- author
- Schaafsma, Gerard C P
LU
and Vihinen, Mauno
LU
- organization
- publishing date
- 2018-11-29
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Representativeness, Benchmark datasets, Variation, Variation interpretation, Mutation
- in
- BMC Bioinformatics
- volume
- 19
- issue
- 1
- pages
- 461 - 461
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:85057534424
- pmid:30497376
- ISSN
- 1471-2105
- DOI
- 10.1186/s12859-018-2478-6
- language
- English
- LU publication?
- yes
- id
- c8e952ec-a911-4dc0-a990-0567f646e3b9
- date added to LUP
- 2018-12-03 09:53:03
- date last changed
- 2024-01-30 04:12:53
@article{c8e952ec-a911-4dc0-a990-0567f646e3b9,
abstract = {{<p>BACKGROUND: Benchmark datasets are essential for both method development and performance assessment. These datasets have numerous requirements, representativeness being one. In the case of variant tolerance/pathogenicity prediction, representativeness means that the dataset covers the space of variations and their effects.</p><p>RESULTS: We performed the first analysis of the representativeness of variation benchmark datasets. We used statistical approaches to investigate how proteins in the benchmark datasets were representative for the entire human protein universe. We investigated the distributions of variants in chromosomes, protein structures, CATH domains and classes, Pfam protein families, Enzyme Commission (EC) classifications and Gene Ontology annotations in 24 datasets that have been used for training and testing variant tolerance prediction methods. All the datasets were available in VariBench or VariSNP databases. We tested also whether the pathogenic variant datasets contained neutral variants defined as those that have high minor allele frequency in the ExAC database. The distributions of variants over the chromosomes and proteins varied greatly between the datasets.</p><p>CONCLUSIONS: None of the datasets was found to be well representative. Many of the tested datasets had quite good coverage of the different protein characteristics. Dataset size correlates to representativeness but only weakly to the performance of methods trained on them. The results imply that dataset representativeness is an important factor and should be taken into account in predictor development and testing.</p>}},
author = {{Schaafsma, Gerard C P and Vihinen, Mauno}},
issn = {{1471-2105}},
keywords = {{Representativeness; Benchmark datasets; Variation; Variation interpretation; Mutation}},
language = {{eng}},
month = {{11}},
number = {{1}},
pages = {{461--461}},
publisher = {{BioMed Central (BMC)}},
series = {{BMC Bioinformatics}},
title = {{Representativeness of variation benchmark datasets}},
url = {{http://dx.doi.org/10.1186/s12859-018-2478-6}},
doi = {{10.1186/s12859-018-2478-6}},
volume = {{19}},
year = {{2018}},
}