Evidence map›Paper›PMID 31791245›Full record

ArticleBMC bioinformatics2019

deltaRpkm: an R package for a rapid detection of differential gene presence between related bacterial genomes.

Hatice Akarsu, Lisandra Aguilar-Bultet, Laurent Falquet

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact, top 83% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

3 authors at 2 institutions in 1 country.

Hatice AkarsuDepartment of Biology, University of Fribourg, Fribourg, Switzerland.
Lisandra Aguilar-BultetSwiss Institute of Bioinformatics, BUGFri group, Fribourg, Switzerland.
Laurent FalquetDepartment of Biology, University of Fribourg, Fribourg, Switzerland. Laurent.Falquet@unifr.ch.ORCID http://orcid.org/0000-0001-8102-7579
SIB Swiss Institute of Bioinformatics · CHUniversity of Bern · CH

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (CH) CRSII3_147692
6 · The paper itself

Abstract

backgroundComparative genomics has seen the development of many software performing the clustering, polymorphism and gene content analysis of genomes at different phylogenetic levels (isolates, species). These tools rely on de novo assembly and/or multiple alignments that can be computationally intensive for large datasets. With a large number of similar genomes in particular, e.g., in surveillance and outbreak detection, assembling each genome can become a redundant and expensive step in the identification of genes potentially involved in a given clinical feature.

resultsWe have developed deltaRpkm, an R package that performs a rapid differential gene presence evaluation between two large groups of closely related genomes. Starting from a standard gene count table, deltaRpkm computes the RPKM per gene per sample, then the inter-group δRPKM values, the corresponding median δRPKM (m) for each gene and the global standard deviation value of m (s

conclusionsTo our knowledge, deltaRpkm is the first tool to propose a straightforward inter-group differential gene presence analysis with large datasets of related genomes, including non-coding genes, and to output directly a list of genes potentially involved in a phenotype.

Indexed as

Genome, BacterialSoftwareBacteriaCluster AnalysisListeria monocytogenesPhenotypePhylogenyComparative genomicsDifferential gene presence/absenceRPKM

Identifiers

PMID31791245
PMCPMC6889214
OpenAlexW2990994449

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.