Sequence clustering strategies improve remote homology recognitions while reducing search times.

By Weizhong Li, Lukasz Jaroszewski, Adam Godzik. 2002

70 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

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Citations in the corpus

Uniclust databases of clustered and deeply annotated protein sequences and alignments (2016, methodology): “gz: archive containing three files with Pfam, SCOP, and PDB annotations, each formatted as tab-separated lists with nine columns: (1,2) identifiers for query and target, (3-5, 6-8) domain start and end-position and total sequence length for both UniProt and database sequence, (9) HHblits E-value.”
UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches (2014, background): “…of metagenomics data (Foerstner et al., 2008; Wommack et al., 2012), developing large-scale hierarchical clustering algorithms (Loewenstein et al., 2008), studying gene duplication (Rivera et al., 2010) and conducting genomic studies of peptide and oligonucleotide frequencies (Capone et al., 2010).”
HHsenser: exhaustive transitive profile search using HMM–HMM comparison (2006, background): “This should make HHsenser substantially more sensitive than straightforward implementations of intermediate profile search (14,22,23) (see caption of Figure 2).”
Clustered Sequence Representation for Fast Homology Search (2007, unknown): “Fourth, proﬁle-based algorithms such as PSI-BLAST (Altschul et al., 1997) can be misled by redundant matches during iteration, causing them to bias the proﬁle towards over-represented domains; this can result in a less sensitive search or even proﬁle corruption (Li et al., 2002; Park et al., 2000).”
The importance of alignment accuracy for molecular replacement. (2004, methodology): “BLAST searches were performed directly on protein sequences from the PDB. PSI-BLAST searches were executed according to a protocol called PDB-BLAST (Li et al., 2002), in which an iterative PSI-BLAST search is executed against the non-redundant sequence database (NR) and the resulting pro®le is used ”
Intrinsic disorder in transcription factors. (2006, methodology): “Briefly, we first used CDHIT (Cluster Database at High Identity with Tolerance) program from http://bioinformatics.org/project/?group_id=350 [Li et al., 2001, 2002] to reduce the homology to 80%, and then to 40% sequence identity according to the recommended procedures.”
Improving sequence-based fold recognition by using 3D model quality assessment (2005, background): “Hard targets are those sequences for which standard methods forhomologydetectionfailtoﬁndanysimilarfolds.”
Sequencing delivers diminishing returns for homology detection: implications for mapping the protein universe (2010, background): “Received on May 10, 2010; revised on August 31, 2010; accepted on September 10, 2010”
clusterMaker: a multi-algorithm clustering plugin for Cytoscape (2011, methodology): “There are many approaches to this classification, including machine learning [11-13] (see [11] for a good overview) as well as clustering large groups of proteins based on either sequence or structural similarity metrics [7,8,14-28].”
Clustering of database sequences for fast homology search using upper bounds on alignment score. (2004, methodology): “[12, 13] presented a method for fast and sensitive homology search by merging database sequences having high sequence identities.”
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].”