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Article Dans Une Revue Bioinformatics Année : 2020

REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets

Résumé

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.
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Origine : Publication financée par une institution

Dates et versions

hal-03413006 , version 1 (17-11-2021)

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Paternité - Pas d'utilisation commerciale

Identifiants

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Camille Marchet, Zamin Iqbal, Daniel Gautheret, Mikaël Salson, Rayan Chikhi. REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets. Bioinformatics, 2020, 36 (Supplement_1), pp.i177-i185. ⟨10.1093/bioinformatics/btaa487⟩. ⟨hal-03413006⟩
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