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ABSTRACT: Motivation
In this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets.Results
We used REINDEER to index the abundances of sequences within 2585 human RNA-seq experiments in 45 h using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of ∼4 billion distinct k-mers across 2585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph of each dataset, then conceptually merges those de Bruijn graphs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances.Availability and implementation
https://github.com/kamimrcht/REINDEER.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Marchet C
PROVIDER: S-EPMC7355249 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Marchet Camille C Iqbal Zamin Z Gautheret Daniel D Salson Mikaël M Chikhi Rayan R
Bioinformatics (Oxford, England) 20200701 Suppl_1
<h4>Motivation</h4>In this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets.<h4>Results</h4>We used REINDEER to index the abundances of sequences within 2585 human RNA-seq experiments in 45 h using only 56 GB of RAM. This makes REINDEER the first method able to re ...[more]