ArticleBMC bioinformatics2004
A functional hierarchical organization of the protein sequence space.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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.
Who cites it
15 citing papers in PubMed.
- KMD clustering: robust general-purpose clustering of biological data.Communications biology · 2023Article
- Trends in genome dynamics among major orders of insects revealed through variations in protein families.BMC genomics · 2015Article
- High-throughput genome scaffolding from in vivo DNA interaction frequency.Nature biotechnology · 2013Article
- Functional inference by ProtoNet family tree: the uncharacterized proteome of Daphnia pulex.BMC bioinformatics · 2013Article
- Gene cluster statistics with gene families.Molecular biology and evolution · 2009Article
- Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space.Bioinformatics (Oxford, England) · 2008Article
- Article
- Discovering multi-level structures in bio-molecular data through the Bernstein inequality.BMC bioinformatics · 2008Article
- Model order selection for bio-molecular data clustering.BMC bioinformatics · 2007Article
- ProtoBee: hierarchical classification and annotation of the honey bee proteome.Genome research · 2006Article
- EVEREST: automatic identification and classification of protein domains in all protein sequences.BMC bioinformatics · 2006Article
- Functional annotation prediction: all for one and one for all.Protein science : a publication of the Protein Society · 2006Article
- The structure at 2.5 A resolution of human basophilic leukemia-expressed protein BLES03.Acta crystallographica. Section F, Structural biology and crystallization communications · 2005Article
- ProTeus: identifying signatures in protein termini.Nucleic acids research · 2005Article
- ProTarget: automatic prediction of protein structure novelty.Nucleic acids research · 2005Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIt is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patterns in known proteins based on manually-validated alignments of known protein families. Such methods can achieve high sensitivity, but are limited by the necessary manual labor. This makes our current view of the protein world incomplete and biased. This paper concerns ProtoNet, a automatic unsupervised global clustering system that generates a hierarchical tree of over 1,000,000 proteins, based solely on sequence similarity.
resultsIn this paper we show that ProtoNet correctly captures functional and structural aspects of the protein world. Furthermore, a novel feature is an automatic procedure that reduces the tree to 12% its original size. This procedure utilizes only parameters intrinsic to the clustering process. Despite the substantial reduction in size, the system's predictive power concerning biological functions is hardly affected. We then carry out an automatic comparison with existing functional protein annotations. Consequently, 78% of the clusters in the compressed tree (5,300 clusters) get assigned a biological function with a high confidence. The clustering and compression processes are unsupervised, and robust.
conclusionsWe present an automatically generated unbiased method that provides a hierarchical classification of all currently known proteins.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.