Evidence map›Paper›PMID 15608170›Full record

ArticleNucleic acids research2005

SPD--a web-based secreted protein database.

Yunjia Chen, Yong Zhang, Yanbin Yin, Ge Gao, Songgang Li, Ying Jiang, Xiaocheng Gu, Jingchu Luo

Abstract read
In one paragraph

Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.

0numbers the graph read from it
0cells of the map it votes in
103citing 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

103 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. SEPDB: a database of secreted proteins.Database : the journal of biological databases and curation · 2024
    Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. The secretome mouse provides a genetic platform to delineate tissue-specific in vivo secretion.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  15. Review
  16. Review
  17. Article
  18. Secretome-Based Screening in Target Discovery.SLAS discovery : advancing life sciences R & D · 2020
    Review
  19. Article
  20. Review

43 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

8 authors.

Yunjia ChenCentre of Bioinformatics, College of Life Sciences, National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China.
Yong Zhang
Yanbin Yin
Ge Gao
Songgang Li
Ying Jiang
Xiaocheng Gu
Jingchu Luo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the improved secreted protein prediction approach and comprehensive data sources, including Swiss-Prot, TrEMBL, RefSeq, Ensembl and CBI-Gene, we have constructed secretomes of human, mouse and rat, with a total of 18 152 secreted proteins. All the entries are ranked according to the prediction confidence. They were further annotated via a proteome annotation pipeline that we developed. We also set up a secreted protein classification pipeline and classified our predicted secreted proteins into different functional categories. To make the dataset more convincing and comprehensive, nine reference datasets are also integrated, such as the secreted proteins from the Gene Ontology Annotation (GOA) system at the European Bioinformatics Institute, and the vertebrate secreted proteins from Swiss-Prot. All these entries were grouped via a TribeMCL based clustering pipeline. We have constructed a web-based secreted protein database, which has been publicly available at http://spd.cbi.pku.edu.cn. Users can browse the database via a GO assignment or chromosomal-location-based interface. Moreover, text query and sequence similarity search are also provided, and the sequence and annotation data can be downloaded freely from the SPD website.

Indexed as

Databases, ProteinAnimalsHumansInternetMiceProteinsRatsSequence Analysis, ProteinUser-Computer InterfaceProteins

Identifiers

PMID15608170
PMCPMC540047

What OpenQuestion holds

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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.