Evidence map›Paper›PMID 29745861›Full record

ArticleBMC bioinformatics2018

Computing the family-free DCJ similarity.

Diego P Rubert, Edna A Hoshino, Marília D V Braga, Jens Stoye, Fábio V Martinez

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Diego P RubertFaculdade de Computação, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
Edna A HoshinoFaculdade de Computação, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil.
Marília D V BragaFaculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.
Jens StoyeFaculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.
Fábio V MartinezFaculdade de Computação, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil. fhvm@facom.ufms.br.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe genomic similarity is a large-scale measure for comparing two given genomes. In this work we study the (NP-hard) problem of computing the genomic similarity under the DCJ model in a setting that does not assume that the genes of the compared genomes are grouped into gene families. This problem is called family-free DCJ similarity.

resultsWe propose an exact ILP algorithm to solve the family-free DCJ similarity problem, then we show its APX-hardness and present four combinatorial heuristics with computational experiments comparing their results to the ILP.

conclusionsWe show that the family-free DCJ similarity can be computed in reasonable time, although for larger genomes it is necessary to resort to heuristics. This provides a basis for further studies on the applicability and model refinement of family-free whole genome similarity measures.

Indexed as

Models, GeneticPhylogenyAlgorithmsAnimalsComputer SimulationDatabases, GeneticGenomeGenomicsHeuristicsHumansMiceRatsDouble-cut-and-joinFamily-free genomic similarityGenome rearrangement

Identifiers

PMID29745861
PMCPMC5998916

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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.