Evidence map›Paper›PMID 19934259›Full record

ArticleNucleic acids research2010

GeneSigDB--a curated database of gene expression signatures.

Aedín C Culhane, Thomas Schwarzl, Razvan Sultana, Kermshlise C Picard, Shaita C Picard, Tim H Lu, Katherine R Franklin, Simon J French, Gerald Papenhausen, Mick Correll and 1 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed, 5 pooled it
–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

68 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Expression profiling of archival tumors for long-term health studies.Clinical cancer research : an official journal of the American Association for Cancer Research · 2012
    Pooled it
  4. iBBiG: iterative binary bi-clustering of gene sets.Bioinformatics (Oxford, England) · 2012
    Pooled it
  5. Pooled it
  6. Article
  7. Article
  8. Article
  9. Article
  10. Application of weighted gene co-expression network analysis to identify the hub genes in H1N1.International journal of physiology, pathophysiology and pharmacology · 2021
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Drug repurposing for the treatment of glioblastoma multiforme.Journal of experimental & clinical cancer research : CR · 2017
    Review
  19. Article
  20. Article

8 more citing papers are in PubMed but not listed here.

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

11 authors.

Aedín C CulhaneDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. johnq@jimmy.harvard.edu
Thomas Schwarzl
Razvan Sultana
Kermshlise C Picard
Shaita C Picard
Tim H Lu
Katherine R Franklin
Simon J French
Gerald Papenhausen
Mick Correll
John Quackenbush

Funding

Genomic Analysis of Network Perturbations in Human DiseaseP50HG004233 · NHGRI · DANA-FARBER CANCER INST · PI VIDAL, MARC · 2007 to 2017
$31.6M
Screening for Breast Cancer Using Molecular SignaturesR01CA098522 · NCI · H. LEE MOFFITT CANCER CTR &RES INST · PI YEATMAN, TIMOTHY J · 2003 to 2007
$2.8M
NCI NIH HHS R01 CA098522NCI NIH HHS R01-CA098522NHGRI NIH HHS 1P50HG004233NHGRI NIH HHS P50 HG004233
6 · The paper itself

Abstract

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats.

Indexed as

Databases, GeneticDatabases, Nucleic AcidAlgorithmsComputational BiologyDatabases, ProteinGene Expression ProfilingGene Expression Regulation, NeoplasticHumansInformation Storage and RetrievalInternetNeoplasmsOligonucleotide Array Sequence AnalysisSoftwareStem Cells

Identifiers

PMID19934259
PMCPMC2808880

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.