CD-HIT: accelerated for clustering the next-generation sequencing data
By Limin Fu, Beifang Niu, Zhengwei Zhu, Sitao Wu, Weizhong Li. 2012
9,756 citations recorded by the source corpus.
Summary: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. Availability: http://cd-hit.org. Contact: liwz@sdsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Subfields: Genetics, Molecular Biology
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In Genetics
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Citations in the corpus
- Predicting multiple conformations via sequence clustering and AlphaFold2 (2023, neutral): “Next, the alignment was trimmed down to include only sequences with sequence homology of 90% or less using CD-HIT 65 .”
- 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 ).”
- Clustering huge protein sequence sets in linear time (2017, methodology): “By increasing m from 20 to 80, Linclust takes only 1.5 to 2 times longer but attains a sensitivity similar to UCLUST (pink in Fig.”
- AlphaFold2-aware protein-DNA binding site prediction using graph transformer (2022, methodology): “Finally, CD-HIT [45] was used to ensure no redundant protein with more than 30% sequence identity within the training set and between the training and test set.”
- Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi (2015, neutral): “1 (Fu et al. 2012) at 97% sequence similarity into operational taxonomic units (OTUs).”
- EffectorP 3.0: prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes (2021, background): “Proteins in the negative sets that share sequence homology to an effector in the positive training set were excluded and protein sets were homology-reduced, both with cd-hit 4.8.1 (-c 0.9) (Fu et al. , 2012).”
- Roary: rapid large-scale prokaryote pan genome analysis (2015, positive): “Sequences are then clustered with MCL (Enright et al. , 2002) and finally the pre-clustering results from CD-HIT are merged together with the results of MCL.”
- Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants (2021, methodology): “Neff score is defined as the number of clusters after the raw MSA inputs were clustered at the 62% sequence identity using CD-HIT(66) with the word length of 4, as used previously.(67) 4.”