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PDF) CD-HIT: Accelerated for clustering the next-generation sequencing data
GitHub - LeeBergstrand/CDHITtoFASTA: Extracts CD-Hit clusters which contain reference proteins and stores them in FASTA format.
Clustering
Fast Program for Clustering and Comparing Large Sets of Protein or Nucleotide Sequences | SpringerLink
How to cluster peptide/protein sequences using cd-hit software? — Bioinformatics Review
PDF] Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences | Semantic Scholar
CD-HIT User's Guide
EdClust: A heuristic sequence clustering method with higher sensitivity
PDF] Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences by Weizhong Li, Adam Godzik · 10.1093/bioinformatics/btl158 · OA.mg
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.
Clustering huge protein sequence sets in linear time | Nature Communications
An example illustrating how the CD-HIT main paradigm works. Record 1 is... | Download Scientific Diagram
CD-HIT User's Guide
A comparison of the performance of different clustering approaches. For... | Download Scientific Diagram
USEARCH performance
Clustering redundant transcripts in OmicsBox with CD-HIT %%page%%
Analysis and comparison of very large metagenomes with fast clustering and functional annotation Weizhong Li, BMC Bioinformatics 2009 Present by Chuan-Yih. - ppt download
Frontiers | Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences
Index of /~psgendb/birchhomedir/FTP/doc/cd-hit
Clustering huge protein sequence sets in linear time | Nature Communications
Frontiers | Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences
kClust: fast and sensitive clustering of large protein sequence databases | BMC Bioinformatics | Full Text
How to cluster peptide/protein sequences using cd-hit software? — Bioinformatics Review
CD-HIT User's Guide
Genes | Free Full-Text | Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs