Clustering

Introduction

With the advancement of next-generation sequencing technologies, the amount of available sequencing data is growing exponentially. Removing redundancy from such data could be crucial for reducing storage space, computational time, and noise interference in some analysis methods. The Clustering functionality allows to cluster sequence data to reduce this redundancy.

The clustering functionality is based on CD-HIT, a widely used program for clustering biological sequences. Basically, CD-HIT is a greedy incremental algorithm that starts with the longest input sequence as the first cluster representative and then processes the remaining sequences from long to short to classify each sequence as a redundant or representative sequence based on its similarities to the existing representatives. The similarities are estimated by common word counting using word indexing and counting tables to filter out unnecessary sequence alignments, which are used to compute exact similarities.

Run Clustering

This functionality can be found under Transcriptomics → RNA-Seq Assembly → Clustering. The wizard allows configuring analysis parameters (Figure 1, Figure 2, Figure 3, and Figure 4).

Input

  • Input Sequences: Select a FASTA file containing input nucleotide sequences to be clustered (e.g. assembled transcripts).

Figure 1. Input Page.

Algorithm Options

  • Sequence Identity Type: Sequence identity is calculated as:

    • Global: number of identical bases in alignment divided by the length of the sorter sequence.

    • Local: number of identical bases in alignment divided by the length of the alignment.

NOTE: The local option requires that the longer and shorter sequence coverage parameters be other than 0.

  • Sequence Identity Threshold: Sequence identity threshold to consider clusters. Must be greater than or equal to 0.8

  • Band Width: Band width of the alignment.

  • Word Length: Word size for the alignments. Choose of word size:

    • 10, 11 for thresholds 0.95 ~ 1.0.

    • 8,9 for thresholds 0.90 ~ 0.95.

    • 7 for thresholds 0.88 ~ 0.9.

    • 6 for thresholds 0.85 ~ 0.88.

    • 5 for thresholds 0.80 ~ 0.85.

    • 4 for thresholds 0.8.

  • Length Cutoff: Length of sequence to skip. Sequences below this length will be skipped.

  • Length Difference Cutoff: Length difference cutoff. It is required as a proportion (0-1). If set to 0.9, the shorter sequences need to be at least 90% length of the representative of the cluster.

  • Accurate Mode: By default, a sequence is clustered to the first cluster that meets the threshold (fast cluster). If this option is checked, the program will cluster it into the most similar cluster that meets the threshold (accurate but slow mode). This won't change the representatives of the final clusters.

  • Comparing Both Strands: By default, the program does both, +/+ and +/- alignments. If this option is unchecked, the program only performs +/+ strand alignments.

Figure 2. Configuration Page 1

Alignment Coverage Options

  • Adjust Longer Sequence Coverage: Establish an alignment coverage for the longer sequence. This option is mandatory if the “Local” Sequence Identity Type will be used.

  • Longer Sequence Coverage: Alignment coverage for the longer sequence. It is required as a proportion (0-1). If set to 0.9, the alignment must cover 90% of the longer sequence.

  • Adjust Shorter Sequence Coverage: Establish an alignment coverage for the shorter sequence. This option is mandatory if the “Local” Sequence Identity Type will be used.

  • Shorter Sequence Coverage: Alignment coverage for the shorter sequence. It is required as a proportion (0-1). If set to 0.9, the alignment must cover 90% of the shorter sequence.

  • Longer Sequence Unmatched %: Maximum unmatched percentage for the longer sequence. If set to 0.1, the unmatched region (excluding leading and tailing gaps) must not be more than 10% of the longer sequence.

  • Shorter Sequence Unmatched %: Maximum unmatched percentage for the shorter sequence. If set to 0.1, the unmatched region (excluding leading and tailing gaps) must not be more than 10% of the shorter sequence.

  • Alignment Position Constraints: If it is checked, the program will force sequences to align at beginnings, and it only does +/+ alignment.

Figure 3. Configuration Page 2

Output Data

  • Representative Sequences: Select the destination file to save the representative sequence of each cluster in FASTA format.

  • Save Cluster File: CD-HIT produces a file containing information about each cluster. Set this option to obtain this file.

  • Output Cluster File: Select a file where the "cluster" file will be placed.

Figure 4. Output Page

Results

Once finished, results are returned in a project containing the representative sequence of each cluster. The SeqName field shows the identifier of the representative sequence. The Description field contains the sequence identifiers for the sequences that have been grouped into each cluster (Figure 4).

In addition, the “Cluster File” is a text file generated by CD-HIT. It contains information about each cluster, such as the sequences grouped in each cluster, what is the representative sequence and how similar are the sequences between them.

>Cluster 0 #Name of the cluster
0	227nt, >TRINITY_DN1539_c0_g1_i1... at 227:1:2041:2267/-/99.56% # Information about the similarity 
1	14980nt, >TRINITY_DN1539_c0_g2_i1... * # Representative Sequence labeled with *
2	14977nt, >TRINITY_DN1539_c0_g2_i2... at 1:14977:1:14980/+/100.00%

Figure 4: Results Project

Finally, a report page (Figure 5) will show a summary of the Clustering results. In the “CD-HIT Results” table different information is shown:

  • The first column, the cluster size, represents the number of original sequences used to generate a single cluster. A cluster is a group of sequences that are similar to each other, so, if a cluster has a size of 3, it means that 3 original sequences were similar enough to each other that they were grouped together into one cluster. This cluster is then represented by one sequence, known as the representative sequence.

  • The second column, the number of clusters (# clusters), represents the number of clusters that were created with a specific cluster size. For example, if this column shows the number 297 next to the cluster size 3, it means that there are 297 clusters that each contain 3 original sequences.

  • The Representative Sequences column have and ‘id’ button in each row that can be clicked in order to see the reference sequence for each cluster.

Figure 5: Summary Report

Furthermore, the Cluster Distribution chart (Figure 6) displays the number of clusters of different sizes that have been obtained.

Figure 6: Cluster Distribution Chart

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