RSDB: representative protein sequence databases have high information content
By Jong-Chan Park, Liisa Holm, Andreas Heger, Cyrus Chothia. 2000
44 citations recorded by the source corpus.
Subfields: Molecular Biology, Spectroscopy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Sitao Wu
- Yang Zhang
- Weizhong Li
- Beifang Niu
- Limin Fu
- Adam Godzik
- Philip E. Bourne
- Lukasz Jaroszewski
In Spectroscopy
- Weizhong Li
- Philip E. Bourne
- Eric D. Scheeff
- Adam Godzik
- Lukasz Jaroszewski
- Limin Fu
- Beifang Niu
- Cathy H. Wu
Citations in the corpus
- UniRef: comprehensive and non-redundant UniProt reference clusters (2007, positive): “Representative sequence databases can be used to provide the same amount of biological information with a smaller set of sequences (Park et al., 2000).”
- Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction (2006, background): “For purposes of homology searches, purging databases of similar sequences does not reduce search performance [45], and in some circumstances may improve performance [46].”
- Tolerating some redundancy significantly speeds up clustering of large protein databases (2002, methodology): “The RSDB project (Park et al., 2000) clustered the NR database at sequence identities from 20 to 99% (ftp://ftp. ebi.ac.uk/pub/contrib/jong/RSDB).”
- Ultrafast clustering algorithms for metagenomic sequence analysis (2012, methodology): “In the past, many available clustering programs were used for clustering protein sequences such as ProtoMap [18], ProtoNet [19], RSDB [20], GeneRAGE [21], TribeMCL [22], ProClust [23], UniqueProt [24], OrthMCL [25], MC-UPGMA [26], Blastclust [27] and CD-HIT [28–31].”
- Clustered Sequence Representation for Fast Homology Search (2007, unknown): “Itoh et al. (2004) describe an alternative redundancy-management technique that, in contrast with the RSDB approach, retains all members of each cluster.”
- Sequence clustering strategies improve remote homology recognitions while reducing search times. (2002, methodology): “One solution is to increase the number of explicitly considered alignments, but this dramatically slows the algorithm and may be still insufficient for families consisting of tens of thousands of homologs (Park et al., 2000).”