Evidence map›Paper›PMID 20860850›Full record

ArticleBMC bioinformatics2010

Detecting variants with Metabolic Design, a new software tool to design probes for explorative functional DNA microarray development.

Sébastien Terrat, Eric Peyretaillade, Olivier Gonçalves, Eric Dugat-Bony, Fabrice Gravelat, Anne Moné, Corinne Biderre-Petit, Delphine Boucher, Julien Troquet, Pierre Peyret

Abstract read
In one paragraph

Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Gene capture coupled to high-throughput sequencing as a strategy for targeted metagenome exploration.DNA research : an international journal for rapid publication of reports on genes and genomes · 2013
    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

10 authors.

Sébastien TerratClermont Université, Université d'Auvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.
Eric Peyretaillade
Olivier Gonçalves
Eric Dugat-Bony
Fabrice Gravelat
Anne Moné
Corinne Biderre-Petit
Delphine Boucher
Julien Troquet
Pierre Peyret

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroorganisms display vast diversity, and each one has its own set of genes, cell components and metabolic reactions. To assess their huge unexploited metabolic potential in different ecosystems, we need high throughput tools, such as functional microarrays, that allow the simultaneous analysis of thousands of genes. However, most classical functional microarrays use specific probes that monitor only known sequences, and so fail to cover the full microbial gene diversity present in complex environments. We have thus developed an algorithm, implemented in the user-friendly program Metabolic Design, to design efficient explorative probes.

resultsFirst we have validated our approach by studying eight enzymes involved in the degradation of polycyclic aromatic hydrocarbons from the model strain Sphingomonas paucimobilis sp. EPA505 using a designed microarray of 8,048 probes. As expected, microarray assays identified the targeted set of genes induced during biodegradation kinetics experiments with various pollutants. We have then confirmed the identity of these new genes by sequencing, and corroborated the quantitative discrimination of our microarray by quantitative real-time PCR. Finally, we have assessed metabolic capacities of microbial communities in soil contaminated with aromatic hydrocarbons. Results show that our probe design (sensitivity and explorative quality) can be used to study a complex environment efficiently.

conclusionsWe successfully use our microarray to detect gene expression encoding enzymes involved in polycyclic aromatic hydrocarbon degradation for the model strain. In addition, DNA microarray experiments performed on soil polluted by organic pollutants without prior sequence assumptions demonstrate high specificity and sensitivity for gene detection. Metabolic Design is thus a powerful, efficient tool that can be used to design explorative probes and monitor metabolic pathways in complex environments, and it may also be used to study any group of genes. The Metabolic Design software is freely available from the authors and can be downloaded and modified under general public license.

Indexed as

SoftwareAlgorithmsBiodegradation, EnvironmentalDNA PrimersDNA ProbesGenetic VariationMicroarray AnalysisOligonucleotide Array Sequence AnalysisPolycyclic Aromatic HydrocarbonsSoil MicrobiologySoil PollutantsSphingomonasDNA PrimersDNA ProbesPolycyclic Aromatic HydrocarbonsSoil Pollutants

Identifiers

PMID20860850
PMCPMC2955052

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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.