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

Biological sequence families contain many sequences that are very similar to each other 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.

Automated summary

In benchmarking sequence analysis methods, splitting data into separate training and test sets is crucial. This study proposes two new methods, based on independent set graph algorithms, that successfully split sequence data into dissimilar training and test sets. This enables the construction of more diverse benchmark datasets.