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Partitioning clustering algorithms for protein sequence data sets
© Fayech et al; licensee BioMed Central Ltd. 2009
- Received: 13 November 2008
- Accepted: 02 April 2009
- Published: 02 April 2009
Genome-sequencing projects are currently producing an enormous amount of new sequences and cause the rapid increasing of protein sequence databases. The unsupervised classification of these data into functional groups or families, clustering, has become one of the principal research objectives in structural and functional genomics. Computer programs to automatically and accurately classify sequences into families become a necessity. A significant number of methods have addressed the clustering of protein sequences and most of them can be categorized in three major groups: hierarchical, graph-based and partitioning methods. Among the various sequence clustering methods in literature, hierarchical and graph-based approaches have been widely used. Although partitioning clustering techniques are extremely used in other fields, few applications have been found in the field of protein sequence clustering. It is not fully demonstrated if partitioning methods can be applied to protein sequence data and if these methods can be efficient compared to the published clustering methods.
We developed four partitioning clustering approaches using Smith-Waterman local-alignment algorithm to determine pair-wise similarities of sequences. Four different sets of protein sequences were used as evaluation data sets for the proposed methods.
We show that these methods outperform several other published clustering methods in terms of correctly predicting a classifier and especially in terms of the correctness of the provided prediction. The software is available to academic users from the authors upon request.
- Good Partition
- Smith Waterman Algorithm
- Partition Cluster Algorithm
- Partition Cluster Method
- Protein Sequence Cluster
In bioinformatics, the number of protein sequences is more than half a million, and it is necessary to find meaningful partitions of them in order to detect their functions. Early approaches of comparing and grouping protein sequences are alignment methods. In fact, pair-wise alignment is used to compare and to cluster sequences. There are two types of pair-wise sequence alignments, local and global [1, 2]. Smith and Waterman local alignment algorithm  helps in finding conserved amino acid patterns in protein sequences. Needleman and Wunsch global alignment algorithm  attempts are made to align the entire sequence using as many characters as possible, up to both ends of each sequence. In order to cluster a large data set of proteins into meaningful clusters, the pair-wise alignment is computationally expensive because of the large number of comparisons carried out. In fact, each protein of the data set should be compared to all others of the data set.
For this reason the pair-wise alignment methods are not efficient to cluster a large set of data. These approaches do not consider the fact that the data set can be too large and may not fit into the main memory of some computers.
The main objective of the unsupervised learning technique is to find a natural grouping or meaningful partition using a distance function [5, 6]. Clustering is a technique which has been extensively applied in a variety of fields related to life science and biology. In sequence analysis, clustering is used to group homologous sequences into gene or protein families.
Many methods are currently available for the clustering of protein sequences into families and most of them can be categorized in three major groups: hierarchical, graph-based and partitioning methods. Among these various methods, most are based on hierarchical or graph-based techniques and they were successfully established. In fact, COG  uses a hierarchical merging of clusters and a manual validation to prevent chaining of multi-domain families. ProtoNet  uses a special metric as described by Sasson et al.  to merge clusters. Picasso  uses multiple alignments as profiles which are then merged hierarchically. ClusTr  and GeneRage  use standard single linkage clustering approaches. SYSTERS  combines hierarchical clustering with graph-based clustering. ProtoMap  and N-cut [15, 16] methods use graph-based clustering approaches. ProClust  uses an extension of the graph-based clustering approach proposed by . ProClust algorithm is based on transitivity criterion and it is capable of handling multi-domain proteins. TribeMCL  applies the Markov clustering approach (MCL) described by Van Dongen . This method operates on a graph that contains similarity information obtained by pair-wise alignment of sequences.
A small amount of partitioning techniques is used in the protein sequence clustering field. Guralnik and Karypis  have proposed one method based on a standard k-means approach where proteins are represented by vectors. However, no tool or database resulting from this interesting work has been made available to the scientific community. JACOP  uses the partitioning algorithm implemented under the name PAM (Partitioning Around Medoids) in the R statistical package . JACOP is based on a random sampling of sequences into groups. It is available on the MyHits platform  where user can submit his own data set. Methods presented bellow are not generally tools since they cannot be applied to cluster a user-provided data set. In fact, several of these methods have been applied to large known data sets and user can only consult the resulting classifications stored in databases. Among the protein sequence clustering methods defined bellow only ProClust, TribeMCL and JACOP are accessed by the community and user can classify his own sequence set.
The main idea here is to design and develop efficient clustering algorithms based on partitioning techniques, which are not very investigated in protein sequence clustering field, in order to cluster large sets of protein sequences. In fact, the number of protein sequences available now is very important (in the order of millions) and hierarchical methods are computationally very expensive so they cannot be extended to cluster large protein sets. However, partitioning methods are very simple and more appropriate to cluster large data sets . For these reasons, we propose here new clustering algorithms based on partitioning techniques which aim to find meaningful partitions, to improve the classification's quality and to reduce the computation time compared to the published clustering tools, ProClust, TribeMCL and JACOP, on different data sets.
Several partitioning clustering algorithms have been proposed in literature. K-means [23, 25–27] is a standard partitioning clustering method based on K centroids of a random initial partition which is iteratively improved. LEADER [28, 29] is an incremental partitioning clustering algorithm in which each of the K clusters is represented by a leader. CLARA (Clustering LARge Applications)  is a partitioning algorithm based on a combination of a sampling approach and the PAM algorithm. CLARANS (Clustering Large Applications based on RANdomized Search)  algorithm views the process of finding optimal medoids as searching through a certain graph, in which each node represents a set of medoids.
We adapted the partitioning algorithms cited bellows to protein sequence data sets. These proposed algorithms are named: Pro-Kmeans, Pro-LEADER, Pro-CLARA and Pro-CLARANS. Performance measures are used to evaluate the proposed methods and to compare them with ProClust, TribeMCL and JACOP results.
Summary of symbols and definitions
Data set of protein sequences to be clustered
Number of clusters
Number of proteins in D
a protein sequence i in D
Number of iterations
The main objective in the proposed algorithms Pro-Kmeans, Pro-LEADER, Pro-CLARA and Pro-CLARANS is to produce K clusters from a data set D of n protein sequences, so that the objective function f(V) is maximized.
Where Po is the gap opening penalty and Pe is the gap extension penalty.
We chose Smith and Waterman local alignment algorithm for computing alignment score. The choice of this algorithm was motivated by the sensitivity for low-scoring alignments  compared to heuristic algorithms such as FASTA  and BLAST , and by execution time  compared to Needleman and Wunsch global alignment algorithm.
We present here Pro-Kmeans, Pro-LEADER, Pro-CLARA and Pro-CLARANS partitioning clustering algorithms for protein sequence sets.
Where m is the size of the subset S i , for which belongs the object O j .
Pro-CLARA relies on the sampling approach to handle large data sets . Instead of finding medoids for the entire data set, Pro-CLARA algorithm draws a small sample S of 40 + 2K sequences from the data set D. To generate an optimal set of medoids for this sample, Pro-CLARA applies the proposed PAM algorithm for protein sequence data sets, Pro-PAM algorithm,
Where the maximal number of neighbours must be at least a threshold value 250 or obtained using the number of clusters K and the number of sequences, n, in the data set as: 1.25%*K*(n-K).
Pro-CLARANS algorithm aims to maximize the total score, TS ih
The proposed algorithms presented here have been implemented in Java package. All of these algorithms used the EMBOSS ftp://emboss.open-bio.org/pub/EMBOSS/ implementation of the Smith and Waterman local alignment algorithm for computing alignment score.
BLOSUM62 (Blocks Substitution Matrix)  was chosen to compute amino acids substitution scores . We chose the default penalties proposed by Smith and Waterman EMBOSS implementation as gap opening (P o ) and gap extension penalties (P e ) (P o = 10 and P e = 2).
To evaluate the Pro-Kmeans, Pro-LEADER, Pro-CLARA and Pro-CLARANS clustering algorithms, a large data set, Training data set, is used. We obtained from the training phase K clusters and each cluster is defined by a medoid (centroid or leader). The training phase results are used to cluster a different data set named, Test data set. Smith Waterman algorithm is used to compare each protein sequence on the test data set with all medoids R i(i ∈ [1..K])obtained from the training phase, and to assign each sequence to the nearest cluster. The predicted family group of each sequence is which of the nearest medoid.
The results obtained from the test phase are used to calculate the Sensitivity and the Specificity of each algorithm and to compare them with results of the published clustering tools, ProClust, TribeMCL and JACOP, tested on the same set: "Test data set".
where TP (True Positives) is the number of correctly identified true homologues pairs, FN (False Negatives) is the number of not identified true homologues pairs and FP (False Positives) is the number of non-homologue pairs predicted to be homologue. A pair of sequences is considered truly homologous, if both are in the same family group.
Protein sequence data sets
To evaluate the performance of the proposed clustering algorithms Pro-Kmeans, Pro-LEADER, Pro-CLARA and Pro-CLARANS, and to compare their results with the available graph-based clustering tools ProClust and TribeMCL and the only available partitioning clustering tool JACOP, protein sequence families with known subfamilies/groups are considered. Protein sequences of HLA protein family have been collected from ftp://ftp.ebi.ac.uk/pub/databases/imgt/mhc/hla. From this set, we have randomly selected 893 sequences named DS1 and grouped into 12 classes. Protein sequences of Hydrolases protein family have been collected from http://www.brenda-enzymes.org/. Hydrolases protein family sequences are categorized into 8 classes according to their function and 3737 sequences, named DS2, have been considered from this family. From Globins protein family , sequences have been collected randomly from 8 different classes and 292 sequences, named DS3, have been selected from the data set IPR000971 in http://srs.ebi.ac.uk. Thus, totally 28 different classes containing sequences are considered as they have been classified by scientists/experts.
The data set considered has a total of 4922 sequences, named DS4, out of which 3500 sequences (practically 70% of the dataset DS4) are randomly for training, and 1422 for testing (practically 30% of the dataset DS4). The same method to obtain the training and the test sets are used on DS1, DS2 and DS3: randomly 70% of the set is selected for the training set and 30% for the test set [see Additional file 1].
Experiments are conducted on Intel Pentium4 processor based machine, having a clock frequency of 2.4 GHZ and 512 MB of RAM. Experimental results are obtained using default values as follows. In Pro-Kmeans, Pro-CLARA and Pro-CLARANS algorithms, the number of iterations q is fixed to 5  and the number of clusters K is fixed to 28. After a number of simulations, we find that the best clustering results are obtained when the parameter K = 28 . In Pro-LEADER algorithm, the Threshold value is fixed to 350 after a number of simulations .
Performance of the three other tools (ProClust, TribeMCL and JACOP) and our four proposed methods on DS1, DS2, DS3 and DS4 data sets with respect to two clustering quality measurements: Sensitivity (Sens.) and Specificity (Spec.)
In our experiments, the use of partitioning clustering methods Pro-Kmeans, Pro-LEADER, Pro-CLARA, Pro-CLARANS and JACOP have improved sensitivity and specificity of hierarchical methods, ProClust and TribeMCL.
We have demonstrated the performance of the proposed Pro-Kmeans, Pro-LEADER, Pro-CLARA, Pro-CLARANS algorithms for the clustering of protein sequences using similarity.
Experiments show also that on the considered data sets DS1, DS2, DS3 and DS4, the higher probability of correctly predicting a classifier (Sensitivity) is obtained using JACOP method. Pro-CLARA method gives the higher probability that the provided prediction is correct (Specificity) although that, Pro-Kmeans, Pro-LEADER, and, Pro-CLARANS obtain also good results. The use of Pro-LEADER method on very large and heterogeneous set, DS4, is not very valuable. In fact the number of not identified true homologues pairs (False Negatives) is very important for that, the obtained Sensitivity is limited to 23.34.
Pro-Kmeans, Pro-LEADER, Pro-CLARA and Pro-CLARANS result confirm that those proposed partitioning methods are valuable, reliable tools for the automatic functional clustering of protein sequences. The use of these methods instead of alignment methods or the classic known clustering methods by biologists can improve the clustering sensitivity and specificity and reduce significantly the computational time. The proposed methods can be used by new biologists especially to cluster a large data set of proteins into meaningful clusters in order to detect their functions.
Similar protein sequences probably have similar biochemical function and three dimensional structures. If two sequences from different organisms are similar, they may have a common ancestor sequence and hence they are said to be homologous. Protein sequence clustering, using Pro-Kmeans, Pro-LEADER, Pro-CLARA and Pro-CLARANS methods helps in classifying a new sequence, retrieve a set of similar sequences for a given query sequence and predict the protein structure of an unknown sequence. We noticed that the classification of large protein sequence data sets using clustering techniques instead of only alignment methods reduce extremely the execution time and improve the efficiency of this important task in molecular biology.
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