Clustering of highly homologous sequences to reduce the size of large protein databases

By Weizhong Li, Lukasz Jaroszewski, Adam Godzik. 2001

1,002 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

In Spectroscopy

Citations in the corpus

Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “Obtained sequences were processed with the CD-HIT software 43 version 4.7 (http://weizhong-lab.ucsd.edu/cd-hit/) using the options: -c 0.62 -”
UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches (2014, methodology): “The clusters are computed using a parallelized version of the CD-HIT algorithm (Li et al., 2001; Li and Godzik, 2006).”
UniRef: comprehensive and non-redundant UniProt reference clusters (2007, neutral): “First, the CD-HIT algorithm (Li and Godzik, 2006; Li et al., 2001) is used to cluster all sequences with a 100% sequence identity threshold.”
TPRpred: a tool for prediction of TPR-, PPR- and SEL1-like repeats from protein sequences (2007, methodology): “These profiles were generated by iterative searches against the non-redundant (NR) database at NCBI, filtered to a maximum pairwise sequence identity of 70% (NR-70) by CD-HIT [22,23].”
Sequence clustering strategies improve remote homology recognitions while reducing search times. (2002, methodology): “The web addresses are http://bioinformatics. burnham-inst.org/liwz/research/benchmark for the fold-recognition benchmark, http://bioinformatics.burnham-inst.org/cd-hi for the fast database clustering programs CD-HI and CD-HIT and http://bioinformatics.burnham-inst.org/pdb_blast for PDBBLAST search.”
A generic method for assignment of reliability scores applied to solvent accessibility predictions (2009, positive): “The query sequences were blasted for four iterations against a local copy of the National Center for Biotechnology Information (NCBI) non-redundant (nr) sequence database, which for speed-up purposes had been homology-reduced to less than 70% sequence identity [32].”
Systematic Analysis of Helical Protein Interfaces Reveals Targets for Synthetic Inhibitors (2010, methodology): “We clustered these complexes according to sequence similarity of all protein chains in each complex using the CD-HIT(27) sequence alignment program at a 95% similarity threshold.”
Tolerating some redundancy significantly speeds up clustering of large protein databases (2002, methodology): “Inspired by NRDB90, we developed a program called CD-HI (Li et al., 2001) that clusters the NR database at a high speed, consuming less than 1 day of CPU time to cluster NR at a threshold of 75% identity.”
UniqueProt: creating representative protein sequence sets (2003, methodology): “UniqueProt constitutes a level in between a relatively slow and careful clustering algorithm as used for example in GeneRAGE (19) and between the extremely fast and crude bias-reduction scheme CD-HI (20).”