Evidence map›Paper›PMID 25140318›Full record

ArticleBioMed research international2014

An efficient parallel algorithm for multiple sequence similarities calculation using a low complexity method.

Evandro A Marucci, Geraldo F D Zafalon, Julio C Momente, Leandro A Neves, Carlo R Valêncio, Alex R Pinto, Adriano M Cansian, Rogeria C G de Souza, Yang Shiyou, José M Machado

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.9field-weighted citation impact, top 28% of its field
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

0 citing papers in PubMed, 7 citations in OpenAlex.

No citing paper in PubMed yet.

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

10 authors at 3 institutions in 2 countries.

Evandro A MarucciDepartment of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
Geraldo F D ZafalonDepartment of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.ORCID 0000-0003-2384-011X
Julio C MomenteDepartment of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.ORCID 0000-0003-3355-0343
Leandro A NevesDepartment of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
Carlo R ValêncioDepartment of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
Alex R PintoDepartment of Control Engineering and Automation, Federal University of Santa Catarina, Rua Pomerode 710, 89065-300 Blumenau, SC, Brazil.
Adriano M CansianDepartment of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
Rogeria C G de SouzaDepartment of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
Yang ShiyouCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
José M MachadoDepartment of Computer Science and Statistics, Sao Paulo State University, Rua Cristóvão Colombo 2265, 15054-000 São José do Rio Preto, SP, Brazil.
Universidade Estadual Paulista (Unesp) · BRUniversidade Federal de Santa Catarina · BRZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the advance of genomic researches, the number of sequences involved in comparative methods has grown immensely. Among them, there are methods for similarities calculation, which are used by many bioinformatics applications. Due the huge amount of data, the union of low complexity methods with the use of parallel computing is becoming desirable. The k-mers counting is a very efficient method with good biological results. In this work, the development of a parallel algorithm for multiple sequence similarities calculation using the k-mers counting method is proposed. Tests show that the algorithm presents a very good scalability and a nearly linear speedup. For 14 nodes was obtained 12x speedup. This algorithm can be used in the parallelization of some multiple sequence alignment tools, such as MAFFT and MUSCLE.

Indexed as

AlgorithmsSequence HomologySoftwareComputational BiologyGenomeSequence Alignment

Identifiers

PMID25140318
PMCPMC4130029
OpenAlexW1988160704

What OpenQuestion holds

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Registered trials

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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.