Evidence map›Paper›PMID 25879410›Full record

ArticleBMC genomics2015

CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers.

Rachid Ounit, Steve Wanamaker, Timothy J Close, Stefano Lonardi

Abstract read
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 305 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
305citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

305 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Trial
  5. Nanopore Sequencing for Chikungunya Virus: Principles and Application.Methods in molecular biology (Clifton, N.J.) · 2027
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245 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rachid OunitDepartment of Computer Science & Engineering, University of California, 900 University Avenue, CA, 92521, Riverside, USA. rouni001@cs.ucr.edu.
Steve WanamakerDepartment of Plant & Botanic Sciences, University of California, 900 University Avenue, CA, 92521, Riverside, USA. steve.wanamaker@ucr.edu.
Timothy J CloseDepartment of Plant & Botanic Sciences, University of California, 900 University Avenue, CA, 92521, Riverside, USA. timothy.close@ucr.edu.
Stefano LonardiDepartment of Computer Science & Engineering, University of California, 900 University Avenue, CA, 92521, Riverside, USA. stelo@cs.ucr.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe problem of supervised DNA sequence classification arises in several fields of computational molecular biology. Although this problem has been extensively studied, it is still computationally challenging due to size of the datasets that modern sequencing technologies can produce.

resultsWe introduce CLARK a novel approach to classify metagenomic reads at the species or genus level with high accuracy and high speed. Extensive experimental results on various metagenomic samples show that the classification accuracy of CLARK is better or comparable to the best state-of-the-art tools and it is significantly faster than any of its competitors. In its fastest single-threaded mode CLARK classifies, with high accuracy, about 32 million metagenomic short reads per minute. CLARK can also classify BAC clones or transcripts to chromosome arms and centromeric regions.

conclusionsCLARK is a versatile, fast and accurate sequence classification method, especially useful for metagenomics and genomics applications. It is freely available at http://clark.cs.ucr.edu/ .

Indexed as

AlgorithmsMetagenomicsGenomicsInternetUser-Computer Interface

Identifiers

PMID25879410
PMCPMC4428112

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.