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Constructing benchmark test sets for biological sequence analysis using independent set algorithms

View ORCID ProfileSamantha N. Petti, View ORCID ProfileSean R. Eddy
doi: https://doi.org/10.1101/2021.09.29.462285
Samantha N. Petti
*NSF-Simons Center for the Mathematical and Statistical Analysis of Biology at Harvard University
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  • For correspondence: spetti@fas.harvard.edu
Sean R. Eddy
†Howard Hughes Medical Institute; Department of Molecular & Cellular Biology; and John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
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Abstract

Statistical inference and machine learning methods are benchmarked on test data independent of the data used to train the method. Biological sequence families are highly non-independent because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 30, 2021.
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Constructing benchmark test sets for biological sequence analysis using independent set algorithms
Samantha N. Petti, Sean R. Eddy
bioRxiv 2021.09.29.462285; doi: https://doi.org/10.1101/2021.09.29.462285
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Constructing benchmark test sets for biological sequence analysis using independent set algorithms
Samantha N. Petti, Sean R. Eddy
bioRxiv 2021.09.29.462285; doi: https://doi.org/10.1101/2021.09.29.462285

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