Evidence map›Paper›PMID 18405346›Full record

ArticleBMC genomics2008

Analysis of Aspergillus nidulans metabolism at the genome-scale.

Helga David, Ilknur S Ozçelik, Gerald Hofmann, Jens Nielsen

Abstract read
In one paragraph

Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Article
  2. Reconstruction of a Genome-Scale Metabolic Model forInternational journal of molecular sciences · 2025
    Article
  3. Article
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  5. Article
  6. Identification of Secondary Metabolites fromMolecules (Basel, Switzerland) · 2020
    Article
  7. Optimizing cultivation ofComputational and structural biotechnology journal · 2020
    Article
  8. Article
  9. Frontiers in cellular and infection microbiology · 2019
    Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Whole-genome metabolic model ofBiotechnology for biofuels · 2016
    Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. MEMOSys 2.0: an update of the bioinformatics database for genome-scale models and genomic data.Database : the journal of biological databases and curation · 2014
    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

4 authors.

Helga DavidFluxome Sciences A/S, Diplomvej 378, Kgs. 2800 Lyngby, Denmark. hd@fluxome.com
Ilknur S Ozçelik
Gerald Hofmann
Jens Nielsen

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAspergillus nidulans is a member of a diverse group of filamentous fungi, sharing many of the properties of its close relatives with significance in the fields of medicine, agriculture and industry. Furthermore, A. nidulans has been a classical model organism for studies of development biology and gene regulation, and thus it has become one of the best-characterized filamentous fungi. It was the first Aspergillus species to have its genome sequenced, and automated gene prediction tools predicted 9,451 open reading frames (ORFs) in the genome, of which less than 10% were assigned a function.

resultsIn this work, we have manually assigned functions to 472 orphan genes in the metabolism of A. nidulans, by using a pathway-driven approach and by employing comparative genomics tools based on sequence similarity. The central metabolism of A. nidulans, as well as biosynthetic pathways of relevant secondary metabolites, was reconstructed based on detailed metabolic reconstructions available for A. niger and Saccharomyces cerevisiae, and information on the genetics, biochemistry and physiology of A. nidulans. Thereby, it was possible to identify metabolic functions without a gene associated, and to look for candidate ORFs in the genome of A. nidulans by comparing its sequence to sequences of well-characterized genes in other species encoding the function of interest. A classification system, based on defined criteria, was developed for evaluating and selecting the ORFs among the candidates, in an objective and systematic manner. The functional assignments served as a basis to develop a mathematical model, linking 666 genes (both previously and newly annotated) to metabolic roles. The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene expression data concerning a study on glucose repression, thereby providing a means of upgrading the information content of experimental data and getting further insight into this phenomenon in A. nidulans.

conclusionWe demonstrate how pathway modeling of A. nidulans can be used as an approach to improve the functional annotation of the genome of this organism. Furthermore we show how the metabolic model establishes functional links between genes, enabling the upgrade of the information content of transcriptome data.

Indexed as

Genome, FungalMetabolic Networks and PathwaysAspergillus nidulansGenomicsModels, Biological

Identifiers

PMID18405346
PMCPMC2386489

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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.