Dataset Information

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

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

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

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]

PMID: 32657392

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Project description:Understanding functions of proteins is one of the most important challenges in many studies of biological processes. The function of a protein can be predicted by analyzing the functions of structurally similar proteins, thus finding structurally similar proteins accurately and efficiently from a large set of proteins is crucial. A protein structure can be represented as a vector by 3D-Zernike Descriptor (3DZD) which compactly represents the surface shape of the protein tertiary structure. This simplified representation accelerates the searching process. However, computing the similarity of two protein structures is still computationally expensive, thus it is hard to efficiently process many simultaneous requests of structurally similar protein search. This paper proposes indexing techniques which substantially reduce the search time to find structurally similar proteins. In particular, we first exploit two indexing techniques, i.e., iDistance and iKernel, on the 3DZDs. After that, we extend the techniques to further improve the search speed for protein structures. The extended indexing techniques build and utilize an reduced index constructed from the first few attributes of 3DZDs of protein structures. To retrieve top-k similar structures, top-10 × k similar structures are first found using the reduced index, and top-k structures are selected among them. We also modify the indexing techniques to support θ-based nearest neighbor search, which returns data points less than θ to the query point. The results show that both iDistance and iKernel significantly enhance the searching speed. In top-k nearest neighbor search, the searching time is reduced 69.6%, 77%, 77.4% and 87.9%, respectively using iDistance, iKernel, the extended iDistance, and the extended iKernel. In θ-based nearest neighbor serach, the searching time is reduced 80%, 81%, 95.6% and 95.6% using iDistance, iKernel, the extended iDistance, and the extended iKernel, respectively.

Dataset Information

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

Publications

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

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