Fimpera: drastic improvement of Approximate Membership Query data-structures with counts.

ABSTRACT:

Motivation

High throughput sequencing technologies generate massive amounts of biological sequence datasets as costs fall. One of the current algorithmic challenges for exploiting these data on a global scale consists in providing efficient query engines on these petabyte-scale datasets. Most methods indexing those datasets rely on indexing words of fixed length k, called k-mers. Many applications, such as metagenomics, require the abundance of indexed k-mers as well as their simple presence or absence, but no method scales up to petabyte-scaled datasets. This deficiency is primarily because storing abundance requires explicit storage of the k-mers in order to associate them with their counts. Using counting Approximate Membership Queries (cAMQ) data structures, such as counting Bl

SUBMITTER: Robidou L 

PROVIDER: S-EPMC10212535 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature