Evidence map›Paper›PMID 29132318›Full record

ArticleBMC bioinformatics2017

BugMat and FindNeighbour: command line and server applications for investigating bacterial relatedness.

Oriol Mazariegos-Canellas, Trien Do, Tim Peto, David W Eyre, Anthony Underwood, Derrick Crook, David H Wyllie

Abstract read
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. A publicly accessible database forMicrobial genomics · 2020
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Observational
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

7 authors.

Oriol Mazariegos-CanellasNuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Trien DoNuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Tim PetoNuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
David W EyreNuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Anthony UnderwoodPublic Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Derrick CrookNuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
David H WyllieNuffield Department of Medicine, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK. David.wyllie@ndm.ox.ac.uk.

Funding

Medical Research Council G0800778National Institute for Health Research G0800778National Institute for Health Research HPRU-2012-10041Wellcome TrustWellcome Trust 087646/Z/08/Z
6 · The paper itself

Abstract

backgroundLarge scale bacterial sequencing has made the determination of genetic relationships within large sequence collections of bacterial genomes derived from the same microbial species an increasingly common task. Solutions to the problem have application to public health (for example, in the detection of possible disease transmission), and as part of divide-and-conquer strategies selecting groups of similar isolates for computationally intensive methods of phylogenetic inference using (for example) maximal likelihood methods. However, the generation and maintenance of distance matrices is computationally intensive, and rapid methods of doing so are needed to allow translation of microbial genomics into public health actions.

resultsWe developed, tested and deployed three solutions. BugMat is a fast C++ application which generates one-off in-memory distance matrices. FindNeighbour and FindNeighbour2 are server-side applications which build, maintain, and persist either complete (for FindNeighbour) or sparse (for FindNeighbour2) distance matrices given a set of sequences. FindNeighbour and BugMat use a variation model to accelerate computation, while FindNeighbour2 uses reference-based compression. Performance metrics show scalability into tens of thousands of sequences, with options for scaling further.

conclusionThree applications, each with distinct strengths and weaknesses, are available for distance-matrix based analysis of large bacterial collections. Deployed as part of the Public Health England solution for M. tuberculosis genomic processing, they will have wide applicability.

Indexed as

Genome, BacterialPhylogenySoftwareBacteriaGenomicsLikelihood FunctionsMycobacterium tuberculosisBacterial genomesDistance matrixPhylogenetic analysis

Identifiers

PMID29132318
PMCPMC5683244

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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.