GraphPart: homology partitioning for biological sequence analysis.
NAR Genom Bioinform
; 5(4): lqad088, 2023 Dec.
Article
in En
| MEDLINE
| ID: mdl-37850036
ABSTRACT
When splitting biological sequence data for the development and testing of predictive models, it is necessary to avoid too-closely related pairs of sequences ending up in different partitions. If this is ignored, performance of prediction methods will tend to be overestimated. Several algorithms have been proposed for homology reduction, where sequences are removed until no too-closely related pairs remain. We present GraphPart, an algorithm for homology partitioning that divides the data such that closely related sequences always end up in the same partition, while keeping as many sequences as possible in the dataset. Evaluation of GraphPart on Protein, DNA and RNA datasets shows that it is capable of retaining a larger number of sequences per dataset, while providing homology separation on a par with reduction approaches.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
NAR Genom Bioinform
Year:
2023
Document type:
Article
Affiliation country:
Dinamarca
Country of publication:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM