Evidence map›Paper›PMID 15663796›Full record

ArticleBMC bioinformatics2005

Large scale hierarchical clustering of protein sequences.

Antje Krause, Jens Stoye, Martin Vingron

Abstract read
In one paragraph

Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Fine-tuning of conditional Transformers improvesComputational and structural biotechnology journal · 2025
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  19. Family classification without domain chaining.Bioinformatics (Oxford, England) · 2009
    Article
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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

3 authors.

Antje KrauseMax Planck Institute for Molecular Genetics, Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany. akrause@igw.tfh-wildau.de
Jens Stoye
Martin Vingron

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSearching a biological sequence database with a query sequence looking for homologues has become a routine operation in computational biology. In spite of the high degree of sophistication of currently available search routines it is still virtually impossible to identify quickly and clearly a group of sequences that a given query sequence belongs to.

resultsWe report on our developments in grouping all known protein sequences hierarchically into superfamily and family clusters. Our graph-based algorithms take into account the topology of the sequence space induced by the data itself to construct a biologically meaningful partitioning. We have applied our clustering procedures to a non-redundant set of about 1,000,000 sequences resulting in a hierarchical clustering which is being made available for querying and browsing at http://systers.molgen.mpg.de/.

conclusionsComparisons with other widely used clustering methods on various data sets show the abilities and strengths of our clustering methods in producing a biologically meaningful grouping of protein sequences.

Indexed as

AlgorithmsCluster AnalysisComputational BiologyDatabases, FactualDatabases, GeneticDatabases, Nucleic AcidDatabases, ProteinFungal ProteinsGenetic LinkageGenomeInformation Storage and RetrievalModels, BiologicalMultigene FamilyPhylogenyProteinsProtein Structure, TertiaryFungal ProteinsProteins

Identifiers

PMID15663796
PMCPMC547898

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.