Resampling nucleotide sequences with closest-neighbor trimming and its comparison to other methods.

ABSTRACT: A large number of nucleotide sequences of various pathogens are available in public databases. The growth of the datasets has resulted in an enormous increase in computational costs. Moreover, due to differences in surveillance activities, the number of sequences found in databases varies from one country to another and from year to year. Therefore, it is important to study resampling methods to reduce the sampling bias. A novel algorithm-called the closest-neighbor trimming method-that resamples a given number of sequences from a large nucleotide sequence dataset was proposed. The performance of the proposed algorithm was compared with other algorithms by using the nucleotide sequences of human H3N2 influenza viruses. We compared the closest-neighbor trimming method with the naive hierarc

SUBMITTER: Yonezawa K 

PROVIDER: S-EPMC3583903 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature