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CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. CD-HIT was originally developed to cluster protein sequences to create reference databases with reduced redundancy Li, et al.
The server provides interactive interface and additional visualization tools. Recently development of cd-hit, especially the multiple-threaded version introduced in Fu et al , enables clustering of very large NGS datasets. For example, it only took cd-hit less than a day on a core computer to cluster a few hundred million protein sequences from a metagenomics study.
Clustering a sequence database usually requires all-by-all comparisons; therefore it is very time-consuming. It is very difficult for these methods to cluster large databases. CD-HIT is a greedy incremental clustering approach. The basic CD-HIT algorithm sorts the input sequences from long to short, and processes them sequentially from the longest to the shortest.
The first sequence is automatically classified as the first cluster representative sequence. Then each query sequence of the remaining sequences is compared to the representative sequences found before it, and is classified as redundant or representative based on whether it is similar to one of the existing representative sequences. In default manner fast mode , a query is grouped into the first representative without comparing to other representatives.
In accurate mode, a query is compared to all representatives and grouped to the most similar one. Based on this greedy method, we established several integrated heuristics that make CD-HIT very efficient. Most sequence alignment tools use short words or k-mer k is the length to speed up computation.
An index table, as opposite to a hash table used in most fast alignment methods, uses a unique index for every unique k-mer, therefore an index table is much faster than a hash table.
Two sequences of a certain identity must share at least a specific number of identical k-mers. It is possible to tell that the identity between two sequences is below a cutoff by counting common k-mers. The filter checks the common k-mers and rejects unnecessary alignments. It is the key for the high efficiency of a short word filter Li, et al. Through statistical analysis of real alignments, we identified the distribution of common k-mer at different sequence lengths and identities, and applied the results to short word filter.
The short word filter not only filters out unnecessary alignment, when alignment is needed, it also identifies a narrow band for banded dynamic programming alignment, which is much faster than regular dynamic programming. Reduced alphabet to be implemented: This is for protein clustering.
Gapped word to be implemented: At low identity cutoff, a gapped word is more efficient than an ungapped word for filtering. A limitation of short word filter is that it can not be used below certain clustering thresholds. Cd-hit-est cannot cluster very long sequences either e.
CD-HIT clusters proteins into clusters that meet a user-defined similarity threshold, usually a sequence identity. Each cluster has one representative sequence.
The input is a protein dataset in fasta format and the output are two files: The most updated options are available from the command line version of the programs. Running the programs without any argument will print out the detailed options. See the figure below, the -aL, -AL, -aS and -AS options can be used to specify the alignment coverage on both the representative sequence and other sequences. It identifies the sequences in db2 that are similar to db1 at a certain threshold.
The input are two protein datasets db1, db2 in fasta format and the output are two files: Please note that by default, cd-hit only lists matches where sequences in db2 are not longer than sequences in db1. You may use options -S2 or -s2 to overwrite this default. You can also swap db1 and db Since eukaryotic genes usually have long introns, which cause long gaps, it is difficult to make full-length alignments for these genes. We implemented a program called cd-hit to identify duplicated reads by reengineering cd-hit-est.
Duplicates are either exactly identical or meet these criteria includes: Here, 3 and 4 can be adjusted by users. We allow mismatches in order to tolerate sequencing errors. To find duplicates in Illumina reads, please use cd-hit-dup see later sections.
Multi-threaded cd-hit programs were implemented with OpenMP. The default value of n is 1 single thread. We have run cd-hit on 4-core, 8-core to core computers and have observed a great speedup.
It splits the input database; runs cd-hit or cd-hit-est in parallel on a computer cluster; and finally merges the outputs into a single file. You can run it as you run cd-hit or cd-hit-est. There are two ways to run jobs on a cluster: In any case, you should have a shared file system, the path to your working directory must be same on all the remote computers.
This script can also be used if you are clustering a very large database and your computer doesn't have enough RAM. In that case, all the divided jobs will still run on a single computer. It splits the input databases; runs cd-hit-2d or cd-hit-est-2d in parallel on a computer cluster; and finally merges the outputs into a single file.
You can run it as you run cd-hit-2d or cd-hit-est-2d. This approach is much faster than running from scratch. It also preserves stable cluster structure. With multiple-step, iterated runs of CD-HIT, you perform a clustering in a neighbor-joining method, which generates a hierarchical structure. The third step use psi-cd-hit, please see psi-cd-hit section for details. There is a problem with one-step clustering.
Two very similar sequences A and B may be clustered into different clusters. Hierarchically clustering will reduce this problem. CD-HIT AuxTools is a set of auxiliary programs that can be used to assist the analysis of the next generation sequencing data. It currently includes programs for removing read duplicates, finding pairs of overlapping reads or joining pair-end reads etc. When two files of paired end reads are used as inputs, each pair of reads will be concatenated into a single one.
A number of options are provided to tune how the duplicates are removed. Running the program without arguments should print out the list of available options, as the following:. For pair-end inputs, the program will take part whole or prefix of the first end and part whole or prefix of the second read, and join them together to form a single read to do the analysis.
A positive value of this option specifies the length of the prefix to be taken from each read. If a read is shorter than this length, letter 'N's will be appended to the read to make up for the length. When this option is not used or is used with a non-positive value, the program will use the length of the longest read as the value of this option.
For single input analysis, only a positive value of this option will be effective. It also allows the program to use only the prefix up to the specified length of each read to do the analysis. In case that a read is shorter than this length, no 'N' is appended to the read since it is not necessary. For duplicate detection, any two reads with number of mismatches no greater than the specified value are considered to be duplicates. For chimeric detection, this option control how similar a read should be to either of its parents.
This option specifies whether or not to carry out an additional step to filter out chimeric clusters. A read or cluster representative is considered as a potential chimeric only if it shares at least the number of bases specified by this option with either of its parents. This option is effective only if the option is set to true for filtering chimeric clusters.
Each read is associated with an abundance number, which is the number of duplicates for the read. The abundance cutoff is mainly used for chimeric filtering to skip chimeric checking on reads with abundance below this cutoff. This option specifies the abundance ratio between a parent read and a chimeric read. So for a read to be chimeric, either of its parents must have abundance at least as high as the ratio times the abundance of the chimeric read.
Internally dissimilarity is measured by percent of mismatches with ungapped alignments. By default the percentage cutoff is set to 0. This option specifies a multiplier to this percentage cutoff. A higher value will increase the dissimilarity thresholds in chimeric filtering. The last file xxx2. In this file, the description for each chimeric cluster contains cluster ids of its parent clusters from the clustering file xxx. By default, only reads that are identical are considered as duplicates.
This only compare the first 50 bases of all sequences, considering that in Illumina reads, sequence quality are better at the beginning of the reads.
The former will allow each duplicate read to have up to 2 mismatches when aligned to its representative; and the later will allow up to one percent mismatches. Then each pair of the base long reads will be jointed to form a single base long read. The basic idea is to find two parent reads whose cross-over is sufficient similar to the chimeric read, while each single parent is sufficiently dissimilar to it.
Such dissimilarity is measured by the percent of mismatches for no-gapped alignments. Since the overlapping reads are searched using a greedy strategy, so different sortings of reads may lead to different result. So it is advisable to run the program multiple times with read shuffling by different random number seeds, and then collect and merge the results. The cluster member 0 in cluster 0 is the representative of the cluster, and it overlaps with each of the other members in the cluster.