Cd-hit-est manual

cd-hit contains a number of programs designed to quickly group sequences. cd-hit groups proteins into clusters that meet a user- defined similarity threshold. cd-hit-est is similar to cd-hit, but designed to group nucleotide sequences (without introns). cd- hit-est-2d is similar to cd-hit-2d but designed to compare two nucleotide www.doorway.ru: CD-HIT stands for Cluster Database at High Identity with Tolerance. The program (cd-hit) takes a fasta format sequence database as input and produces a set of 'non-redundant' (nr) representative sequences as output. In addition cd-hit outputs a cluster file, documenting the sequence 'groupies' for each nr sequence representative. The idea is to reduce the overall size of the database without. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.

CD-HIT (CD-HIT-EST) clusters similar proteins (DNAs) into clusters that meet a user-defined similarity threshold. CD-HIT-2D (CD-HIT-EST-2D) compares 2 datasets and identifies the sequences in db2 that are similar to db1 above a threshold. CD-HIT identifies natural and artificial duplicates from pyrosequencing reads. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST. CD-HIT Suite: Biological Sequence Clustering and Comparison. By cd-hit's default algorithm, a sequence is clustered to the first cluster that meet the threshold (fast mode). If set to yes, the program will cluster it into the most similar cluster that meet the threshold (accurate but slow mode).

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