Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

By Weizhong Li, Adam Godzik. 2006

10,567 citations recorded by the source corpus.

Subfields: Molecular Biology, Algorithms & Complexity, Computational Biology & Bioinformatics

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Citations in the corpus

Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, positive): “We focused on single-chain RNAs and reduced redundancy by clustering sequences with Cd-hit 25 at an 80% sequence similarity threshold, resulting in 782 unique sequence clusters from 5,583 RNA chains.”
Uniclust databases of clustered and deeply annotated protein sequences and alignments (2016, methodology): “In the first cascaded clustering step, in which we generate the Uniclust90 sequence set, we use the simple greedy clustering strategy of CD-HIT (10) that was already part of MMseqs.”
Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “The resulting 13,858 proteins were clustered through PSI-CD-HIT [79, 80] (version 4.0; at 30% PIDE or Eval< 10 − 6 ).”
DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, methodology): “And the remaining proteins were filtered to less than or equal to 25% pairwise similarity with e-value cutoff ‘1e-4’ by CD-Hit suite (Li and Godzik, 2006).”
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).”
Clustering huge protein sequence sets in linear time (2017, methodology): “Similar to the k-mer prefilter used in CD-HIT, UCLUST, kclust and MMseqs [8, 11, 26, 27], the preclustering reduces the number of comparisons by a large factor F using the slow, exact measure, but the time complexity of the ex240 act distance calculation O(N(2)/F ) is still quadratic in N .”
UniRef: comprehensive and non-redundant UniProt reference clusters (2007, positive): “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.”
Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences (2014, methodology): “Submitted 10 June 2014 Accepted 6 August 2014 Published 21 August 2014 Corresponding author J. Gregory Caporaso, gregcaporaso@gmail.com Academic editor Andrew Weightman Additional Information and Declarations can be found on page 22 DOI 10.7717/peerj.545 Copyright 2014 Rideout et al. Distributed und”
High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities (2013, methodology): “Sequence reads from each sample were clustered into similaritybased OTUs at a range of genetic divergence from 1% to 10% (insertions and deletions included) using the CD-HIT method (Li & Godzik 2006) implemented in softwareCLOTU.”
Stacks 2: Analytical Methods for Paired-end Sequencing Improve RADseq-based Population Genomics (2019, methodology): “Both packages differ from Stacks in their approach. radassembler first clusters forward reads into loci using Stacks v1, then builds paired‐ end contigs with the CAP3 overlap‐layout method (Huang & Madan, 1999). radassembler focuses only on locus construction and does not identify SNPs or call genot”
A Primer on Metagenomics (2010, methodology): “. MG-RAST accepts a 454 dataset as input, normalizes it (removes artefactual duplicate sequences, a known problem with 454 sequencing), and then performs gene calling and annotation by a variety of sequence similarity searches (mainly BLAST) against various sequence databases, including 16S rDNA. It”
Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans. (2011, background): “Similar sequences were identified with the cd-hit program (26) with the following variables: a minimum coverage of 99% and minimum pairwise identity of 97% [see references 18 and 27 for more information about phylotype binning (operational taxonomic unit picking), UniFracbased clustering, taxonomic ”