Evidence map›Paper›PMID 16845011›Full record

ArticleNucleic acids research2006

TOM: a web-based integrated approach for identification of candidate disease genes.

Simona Rossi, Daniele Masotti, Christine Nardini, Elena Bonora, Giovanni Romeo, Enrico Macii, Luca Benini, Stefano Volinia

Abstract readValidation Study
In one paragraph

Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Systems genetics in "-omics" era: current and future development.Theory in biosciences = Theorie in den Biowissenschaften · 2013
    Review
  4. Candidate gene prioritization.Molecular genetics and genomics : MGG · 2012
    Review
  5. Article
  6. Article
  7. Bioinformatic tools for identifying disease gene and SNP candidates.Methods in molecular biology (Clifton, N.J.) · 2010
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Candidate gene identification approach: progress and challenges.International journal of biological sciences · 2007
    Review
  20. 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

8 authors.

Simona RossiFunctional Genomics Laboratory and Telethon Facility, DAMA Data Mining for Analysis of DNA Microarrays, Dipartimento di Morfologia ed Embriologia, Via Fossato di Mortara 64b, 44100 Ferrara, Italy. simona.rossi@gmail.com
Daniele Masotti
Christine Nardini
Elena Bonora
Giovanni Romeo
Enrico Macii
Luca Benini
Stefano Volinia

Funding

Telethon GTF03012
6 · The paper itself

Abstract

The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals. The algorithm uses the information stored in public resources, including mapping, expression and functional databases. Given the queries, TOM will select and list one or more candidate genes. This approach allows the geneticist to bypass the costly and time consuming tracing of genetic markers through entire families and might improve the chance of identifying disease genes, particularly for rare diseases. We present here the tool and the results obtained on known benchmark and on hereditary predisposition to familial thyroid cancer. Our algorithm is available at http://www-micrel.deis.unibo.it/~tom/.

Indexed as

Genetic Predisposition to DiseaseSoftwareAlgorithmsComputational BiologyGene Expression ProfilingGenetic Diseases, InbornGenetic LinkageInternetOligonucleotide Array Sequence AnalysisSystems IntegrationThyroid NeoplasmsUser-Computer Interface

Identifiers

PMID16845011
PMCPMC1538851

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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.