REINDEER: efficient indexing of k-mer ...
Document type :
Article dans une revue scientifique
PMID :
Title :
REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets
Author(s) :
Marchet, Camille [Auteur correspondant]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Iqbal, Zamin [Auteur]
Gautheret, Daniel [Auteur]
Institut de Biologie Intégrative de la Cellule [I2BC]
Salson, Mikaël [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chikhi, Rayan [Auteur]
Algorithmes pour les séquences biologiques - Sequence Bioinformatics
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Iqbal, Zamin [Auteur]
Gautheret, Daniel [Auteur]
Institut de Biologie Intégrative de la Cellule [I2BC]
Salson, Mikaël [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chikhi, Rayan [Auteur]

Algorithmes pour les séquences biologiques - Sequence Bioinformatics
Journal title :
Bioinformatics
Pages :
i177-i185
Publisher :
Oxford University Press (OUP)
Publication date :
2020-07-01
ISSN :
1367-4803
HAL domain(s) :
Informatique [cs]/Bio-informatique [q-bio.QM]
English abstract : [en]
MotivationIn 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 ...
Show more >MotivationIn 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.ResultsWe 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.Show less >
Show more >MotivationIn 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.ResultsWe 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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
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