Evidence map›Paper›PMID 26586805›Full record

ArticleNucleic acids research2016

COLOMBOS v3.0: leveraging gene expression compendia for cross-species analyses.

Marco Moretto, Paolo Sonego, Nicolas Dierckxsens, Matteo Brilli, Luca Bianco, Daniela Ledezma-Tejeida, Socorro Gama-Castro, Marco Galardini, Chiara Romualdi, Kris Laukens and 3 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

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  7. Frontiers in bioinformatics · 2024
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  8. Stress response ofCurrent research in food science · 2024
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  12. Article
  13. Using genome-wide expression compendia to study microorganisms.Computational and structural biotechnology journal · 2022
    Review
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  15. Article
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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

13 authors.

Marco MorettoDepartment of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, San Michele all'Adige, Trento (TN) 38010, Italy Department of Biology, University of Padova, Padova (PD) 35121, Italy.
Paolo SonegoDepartment of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, San Michele all'Adige, Trento (TN) 38010, Italy.
Nicolas DierckxsensInteruniversity Institute of Bioinformatics Brussels (IB), ULB-VUB, Triomflaan CP 263, B-1050 Brussels, Belgium.
Matteo BrilliDepartment of Genomics and Biology of Fruit Crops, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all' Adige, Trento (TN) 38010, Italy.
Luca BiancoDepartment of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, San Michele all'Adige, Trento (TN) 38010, Italy.
Daniela Ledezma-TejeidaPrograma de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, Mexico.
Socorro Gama-CastroPrograma de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, Mexico.
Marco GalardiniEMBL-EBI, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK.
Chiara RomualdiDepartment of Biology, University of Padova, Padova (PD) 35121, Italy.
Kris LaukensDepartment of Mathematics and Computer Science, University of Antwerp, B-2020 Antwerp, Belgium Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, B-2650 Edegem, Belgium.
Julio Collado-VidesPrograma de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, Mexico.
Pieter MeysmanDepartment of Mathematics and Computer Science, University of Antwerp, B-2020 Antwerp, Belgium Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, B-2650 Edegem, Belgium.
Kristof EngelenDepartment of Computational Biology, Research and Innovation Center, Fondazione Edmund Mach, San Michele all'Adige, Trento (TN) 38010, Italy kristof.engelen@fmach.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COLOMBOS is a database that integrates publicly available transcriptomics data for several prokaryotic model organisms. Compared to the previous version it has more than doubled in size, both in terms of species and data available. The manually curated condition annotation has been overhauled as well, giving more complete information about samples' experimental conditions and their differences. Functionality-wise cross-species analyses now enable users to analyse expression data for all species simultaneously, and identify candidate genes with evolutionary conserved expression behaviour. All the expression-based query tools have undergone a substantial improvement, overcoming the limit of enforced co-expression data retrieval and instead enabling the return of more complex patterns of expression behaviour. COLOMBOS is freely available through a web application at http://colombos.net/. The complete database is also accessible via REST API or downloadable as tab-delimited text files.

Indexed as

Databases, GeneticGene Expression ProfilingArchaeaBacteriaOligonucleotide Array Sequence AnalysisSequence Analysis, RNASoftware

Identifiers

PMID26586805
PMCPMC4702885

What OpenQuestion holds

Textmetadata
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.