Sequence Clustering Algorithm Research Articles

Sequence Clustering Algorithms Based on Global and Local Similarity

现有的很多序列聚类算法是基于“局部特征可以表征整个序列”的假设来进行的,即不区分实际应用中序列的整体相似性和局部相似性.这对存在保守子模式的序列,如DNA和蛋白质序列是适用的,但对一些注重整体序列相似性的应用领域,如:在交易数据库中用户购买行为的比较,时间序列数据中全局模式的匹配等,由于难以产生频繁子模式,用基于全局相似性的度量方法进行聚类显得更为合理.此外,在基于局部相似性的序列聚类算法中,选取的局部子模式表征序列的能力也有待进一步提高.由此,针对不同应用领域,分别提出基于整体相似性的序列聚类算法GSClu和基于局部相似性的序列聚类算法LSClu.GSClu和LSClu分别利用带剪枝策略的二分k均值算法和基于有gap约束的强区分度子模式方法对各自领域的序列数据进行聚类.实验采用交易序列数据和蛋白质序列数据,实验结果表明,GSClu和LSClu对各自领域的序列数据具有较快的处理速度和良好的聚类质量.;Many current sequence clustering algorithms are based on the hypothesis that sequence can be characterized by its local features, without differentiating between global similarity and local similarity of sequences in different applications, which is applicable to biological sequences such as DNA and protein with conserved sub-patterns. However, in some domains such as the comparison of customers’ purchase behaviors in retail transaction database and the pattern match in time series data, due to difficulties in forming frequent sub-pattern, it is more reasonable to cluster these sequence data based on global similarity. Besides, among sequence clustering algorithms based on local similarity, the ability that sub-patterns characterize sequence should be improved. So, this paper proposes two clustering algorithms, GSClu (global similarity clustering) and LSClu (local similarity clustering), for different application fields, based on global and local similarity respectively. GSClu uses bisecting k-means technique and CSClu adopts sub-patterns with gap constraint to cluster the sequence data of corresponding application field. Sequence data in the experiments include retail transaction data and protein data. The experimental results show that GSClu and LSClu are of fast processing rate and high clustering quality.

Analysis and comparison of very large metagenomes with fast clustering and functional annotation

BackgroundThe remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes) are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand.ResultsThe new metagenomic data analysis method Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (RAMMCAP) was developed using an ultra-fast sequence clustering algorithm, fast protein family annotation tools, and a novel statistical metagenome comparison method that employs a unique graphic interface. RAMMCAP processes extremely large datasets with only moderate computational effort. It identifies raw read clusters and protein clusters that may include novel gene families, and compares metagenomes using clusters or functional annotations calculated by RAMMCAP. In this study, RAMMCAP was applied to the two largest available metagenomic collections, the "Global Ocean Sampling" and the "Metagenomic Profiling of Nine Biomes".ConclusionRAMMCAP is a very fast method that can cluster and annotate one million metagenomic reads in only hundreds of CPU hours. It is available from .

BAG: a graph theoretic sequence clustering algorithm

In this paper, we first discuss issues in clustering biological sequences with graph properties, which inspired the design of our sequence clustering algorithm BAG. BAG recursively utilises several graph properties: biconnectedness, articulation points, pquasi-completeness, and domain knowledge specific to biological sequence clustering. To reduce the fragmentation issue, we have developed a new metric called cluster utility to guide cluster splitting. Clusters are then merged back with less stringent constraints. Experiments with the entire COG database and other sequence databases show that BAG can cluster a large number of sequences accurately while keeping the number of fragmented clusters significantly low.

A Bioinformatic Approach to CLL IGHV Sequence Analysis Identifies Extensive Ig Sequence Archetypes among BothMutated and Unmutated Sequences.

Several recent studies have pointed towards a number of sets of CLL patients with highly similar IgV genes, both in terms of V, D, J segment and light chain use and also heavy chain third complementarity determining region (CDR3) architecture and amino acid composition. These findings support the notion that antigen may drive development or perpetuation of the leukemic cells. To effectively mine the available Ig sequence databases, we developed a novel sequence similarity and clustering algorithm that incorporates the mechanistic knowledge of IgV gene recombination. CDR3 sequences were initially aligned using the conventional blosum62 based scoring matrix, then rescored based on the junctional annotations. In this way, the method gives greater weight to sequence similarity created as a consequence of the VDJ recombination process, rather than simple germline homology. This modified scoring matrix allows the creation of more robust sequence “clusters” and is not restricted to sequences composed of the same germline genes. Preliminary analysis with this method on a collection of more than 1000 CLL IgV gene sequences has identified over 40 clusters, including all that have been previously described. Interestingly, a species abundance estimator indicates additional clusters of IgV genes in CLL remain to be discovered. Additionally, we performed a focused evaluation of over 100 VH4-34 containing sequences expressed by CLL cells of unrelated patients. The VH4-34 gene is among the most commonly used IVH genes expressed in CLL, has a curious bi-phasic distribution of mutational frequencies among different CLL cases, and can encode antibodies with specificity for the linear polylactosamine carbohydrate antigen “i” found on neonatal red blood cells. Prior studies by the Chiorazzi group identified a subgroup of patients with CLL cells that expressed remarkably similar mutated VH4-34 genes encoding isotype-switched Ig. Using the method described above, seven additional clusters of CLL cases that expressed highly related VH4-34-encoded Ig were identified, including three clusters containing sequences with a mutated VH4-34. The mutations seen within these clusters have a spectra that is distinct from other VH4-34 sequences from CLL, normal plasma cells, or marginal zone B cells. Furthermore, within each cluster there were conserved replacement mutations not commonly found in the other VH4-34 sequences. This clearly indicates specific selective pressures subsequent to IgV gene rearrangement and implies that the expressed Ig were selected for binding to several distinct antigens or epitopes. However, redundant antigen specificities encoded by different sequence clusters can not be excluded and the number of driving antigens may still be quite restricted. Comparative analysis of normal sequence collections is ongoing.

The phylogenetic diversity of eukaryotic transcription.

Eukaryotic transcription is a highly regulated process involving interactions between large numbers of proteins. To analyse the phylogenetic distribution of the components of this process, six crown eukaryote group genomes were queried with a reference set of transcription-associated (TA) proteins. On average, one in 10 proteins encoded by these genomes were found to be homologous to sequences in the reference set. Analysis of families identified using an accurate sequence clustering algorithm and containing both TA proteins and eukaryotic sequences showed that in two-thirds of the families the homologues originate from a single kingdom. Furthermore, in only 15% of the fungal-specific clusters are the homologues present in both budding and fission yeast, as compared with the metazoan-specific clusters where 53% of the homologues originate from two or more species. Families whose members comprise general transcription factor or RNA polymerase subunits exhibit a low degree of taxon specificity, suggesting that the transcription initiation complex is highly conserved. This contrasts with transcriptional regulator families, that are primarily taxon-specific, indicating proteins controlling gene activation exhibit considerable sequence diversity across the eukaryotic domain.

An efficient algorithm for large-scale detection of protein families

Detection of protein families in large databases is one of the principal research objectives in structural and functional genomics. Protein family classification can significantly contribute to the delineation of functional diversity of homologous proteins, the prediction of function based on domain architecture or the presence of sequence motifs as well as comparative genomics, providing valuable evolutionary insights. We present a novel approach called TRIBE-MCL for rapid and accurate clustering of protein sequences into families. The method relies on the Markov cluster (MCL) algorithm for the assignment of proteins into families based on precomputed sequence similarity information. This novel approach does not suffer from the problems that normally hinder other protein sequence clustering algorithms, such as the presence of multi-domain proteins, promiscuous domains and fragmented proteins. The method has been rigorously tested and validated on a number of very large databases, including SwissProt, InterPro, SCOP and the draft human genome. Our results indicate that the method is ideally suited to the rapid and accurate detection of protein families on a large scale. The method has been used to detect and categorise protein families within the draft human genome and the resulting families have been used to annotate a large proportion of human proteins.

D2_cluster: a validated method for clustering EST and full-length cDNAsequences.

Several efforts are under way to condense single-read expressed sequence tags (ESTs) and full-length transcript data on a large scale by means of clustering or assembly. One goal of these projects is the construction of gene indices where transcripts are partitioned into index classes (or clusters) such that they are put into the same index class if and only if they represent the same gene. Accurate gene indexing facilitates gene expression studies and inexpensive and early partial gene sequence discovery through the assembly of ESTs that are derived from genes that have yet to be positionally cloned or obtained directly through genomic sequencing. We describe d2_cluster, an agglomerative algorithm for rapidly and accurately partitioning transcript databases into index classes by clustering sequences according to minimal linkage or "transitive closure" rules. We then evaluate the relative efficiency of d2_cluster with respect to other clustering tools. UniGene is chosen for comparison because of its high quality and wide acceptance. It is shown that although d2_cluster and UniGene produce results that are between 83% and 90% identical, the joining rate of d2_cluster is between 8% and 20% greater than UniGene. Finally, we present the first published rigorous evaluation of under and over clustering (in other words, of type I and type II errors) of a sequence clustering algorithm, although the existence of highly identical gene paralogs means that care must be taken in the interpretation of the type II error. Upper bounds for these d2_cluster error rates are estimated at 0.4% and 0.8%, respectively. In other words, the sensitivity and selectivity of d2_cluster are estimated to be >99.6% and 99.2%.

A heuristic for scheduling task graphs with communication delays onto multiprocessors

The multiprocessor scheduling problem can be stated as finding a schedule for a task graph to be executed on a multiprocessor architecture so that the execution time can be minimized. Since this problem is known to be NP-hard, in all but a few very restricted cases, the main research efforts in this area are focused on heuristic methods for obtaining near-optimal solutions in a reasonable amount of time. A new compile-time single-pass multiprocessor scheduling technique, called chaining, has been developed and is presented in this paper. Chaining takes into account the communication overhead and can be applied to scheduling task graphs onto fully-connected multiprocessor architectures containing an arbitrary (bounded as well as an unbounded) number of processors. This technique can be viewed as a generalized list scheduling concept, that does not impose any preconditions about the ordering in which tasks are selected for scheduling as well as about the position within the current partial schedule where selected task can be placed. Varying the selection policy, implemented in this technique, we are able to generate a class of scheduling algorithms. As a representative example of this class we present Task Selection First (TSF) scheduler. We compare performances of the TSF scheduler with the dynamic level scheduler proposed by Sih and Lee, the dominant sequence clustering algorithm proposed by Yang and Gerasoulis, and the DSC/MLS algorithm, a modified version of Sarkar's two-step scheduling technique.

DSC: scheduling parallel tasks on an unbounded number of processors

We present a low-complexity heuristic, named the dominant sequence clustering algorithm (DSC), for scheduling parallel tasks on an unbounded number of completely connected processors. The performance of DSC is on average, comparable to, or even better than, other higher-complexity algorithms. We assume no task duplication and nonzero communication overhead between processors. Finding the optimum solution for arbitrary directed acyclic task graphs (DAG's) is NP-complete. DSC finds optimal schedules for special classes of DAG's, such as fork, join, coarse-grain trees, and some fine-grain trees. It guarantees a performance within a factor of 2 of the optimum for general coarse-grain DAG's. We compare DSC with three higher-complexity general scheduling algorithms: the ETF by J.J. Hwang, Y.C. Chow, F.D. Anger, and C.Y. Lee (1989); V. Sarkar's (1989) clustering algorithm; and the MD by M.Y. Wu and D. Gajski (1990). We also give a sample of important practical applications where DSC has been found useful. >