ArticleBMC genomics2012
Dynamics of domain coverage of the protein sequence universe.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Gene Ontology Capsule GAN: an improved architecture for protein function prediction.PeerJ. Computer science · 2022Article
- Exploring the dark foldable proteome by considering hydrophobic amino acids topology.Scientific reports · 2017Article
- Cache Domains That are Homologous to, but Different from PAS Domains Comprise the Largest Superfamily of Extracellular Sensors in Prokaryotes.PLoS computational biology · 2016Article
- The language of the protein universe.Current opinion in genetics & development · 2015Review
- Most partial domains in proteins are alignment and annotation artifacts.Genome biology · 2015Article
- CDvist: a webserver for identification and visualization of conserved domains in protein sequences.Bioinformatics (Oxford, England) · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe currently known protein sequence space consists of millions of sequences in public databases and is rapidly expanding. Assigning sequences to families leads to a better understanding of protein function and the nature of the protein universe. However, a large portion of the current protein space remains unassigned and is referred to as its "dark matter".
resultsHere we suggest that true size of "dark matter" is much larger than stated by current definitions. We propose an approach to reducing the size of "dark matter" by identifying and subtracting regions in protein sequences that are not likely to contain any domain.
conclusionsRecent improvements in computational domain modeling result in a decrease, albeit slowly, in the relative size of "dark matter"; however, its absolute size increases substantially with the growth of sequence data.
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Registered trials
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