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A novel hierarchical clustering algorithm for gene sequences

BMC BIOINFORMATICS 2012年第1期13卷 1-15页

作者： Wei, Dan Jiang, Qingshan Wei, Yanjie Wang, Shengrui Chinese Acad Sci Shenzhen Key Lab High Performance Data Min Shenzhen Inst Adv Technol Shenzhen Peoples R China Xiamen Univ Dept Cognit Sci Xiamen Peoples R China Xiamen Univ Fujian Key Lab Brain Intelligent Syst Xiamen Peoples R China Univ Sherbrooke Dept Comp Sci Sherbrooke PQ J1K 2R1 Canada

Background: clustering DNA sequences into functional groups is an important problem in bioinformatics. We propose a new alignment-free algorithm, mBKM, based on a new distance measure, DMk, for clustering gene sequences. This method transforms DNA sequences into the feature vectors which contain the occurrence, location and order relation of k-tuples in DNA sequence. Afterwards, a hierarchical procedure is applied to clustering DNA sequences based on the feature vectors. Results: The proposed distance measure and clustering method are evaluated by clustering functionally related genes and by phylogenetic analysis. This method is also compared with BlastClust, CD-HIT-EST and some others. The experimental results show our method is effective in classifying DNA sequences with similar biological characteristics and in discovering the underlying relationship among the sequences. Conclusions: We introduced a novel clustering algorithm which is based on a new sequence similarity measure. It is effective in classifying DNA sequences with similar biological characteristics and in discovering the relationship among the sequences.

关键词： cluster algorithm cluster Result H1N1 Virus cluster Performance hierarchical cluster algorithm

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A composite key management scheme for mobile ad hoc networks

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On the Move Federated Workshops

作者： Fu, Yingfang He, Jingsha Li, Guorui Beijing Univ Technol Coll Comp Sci & Technol Beijing 100022 Peoples R China Beijing Univ Technol Sch Software Engn Beijing 100022 Peoples R China

ISBN: (纸本)3540482695

An Ad Hoc network is a multi-path autonomous system comprised of a group of mobile nodes with wireless transceivers. In this paper, we first review the present research in key management in Ad Hoc networks. Then, by considering the characteristics of Ad Hoc networks in which the free movement of nodes can lead to a variety of topological changes, especially network separation and convergence, we propose a new key management scheme based on a combination of techniques, such as hierarchical topology structure, virtual CA (certification authority), offline CA and certificate chain. We show that the proposed scheme would improve key management in security, expandability, validity, fault tolerance and usability.

关键词： ad hoc networks key management identity-based cryptosystem threshold cryptography hierarchical cluster algorithm

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Optimising parallel R correlation matrix calculations on gene expression data using MapReduce

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BMC BIOINFORMATICS 2014年第1期15卷 1-9页

作者： Wang, Shicai Pandis, Ioannis Johnson, David Emam, Ibrahim Guitton, Florian Oehmichen, Axel Guo, Yike Univ London Imperial Coll Sci Technol & Med Data Sci Inst London England Shanghai Univ Sch Comp Sci Shanghai Peoples R China

Background: High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratification purposes. However, the current speed of the clustering algorithms cannot meet the requirement of large-scale molecular data due to poor performance of the correlation matrix calculation. With high-throughput sequencing technologies promising to produce even larger datasets per subject, we expect the performance of the state-of-the-art statistical algorithms to be further impacted unless efforts towards optimisation are carried out. MapReduce is a widely used high performance parallel framework that can solve the problem. Results: In this paper, we evaluate the current parallel modes for correlation calculation methods and introduce an efficient data distribution and parallel calculation algorithm based on MapReduce to optimise the correlation calculation. We studied the performance of our algorithm using two gene expression benchmarks. In the micro-benchmark, our implementation using MapReduce, based on the R package RHIPE, demonstrates a 3.26-5.83 fold increase compared to the default Snowfall and 1.56-1.64 fold increase compared to the basic RHIPE in the Euclidean, Pearson and Spearman correlations. Though vanilla R and the optimised Snowfall outperforms our optimised RHIPE in the micro-benchmark, they do not scale well with the macro-benchmark. In the macro-benchmark the optimised RHIPE performs 2.03-16.56 times faster than vanilla R. Benefiting from the 3.30-5.13 times faster data preparation, the optimised RHIPE performs 1.22-1.71 times faster than the optimised Snowfall. Both the optimised RHIPE and the optimised Snowfall successfully performs the Kendall correlation with TCGA dataset within 7 hours. Both of them conduct more than 30 times faster than the estimated vanilla R. Conclusions: The performance evaluation found that the new Ma

关键词： Data Block Vanilla Data Preparation hierarchical cluster algorithm MapReduce Framework

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CLAG: an unsupervised non hierarchical clustering algorithm handling biological data

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BMC BIOINFORMATICS 2012年第1期13卷 1-14页

作者： Dib, Linda Carbone, Alessandra UPMC UMR7238 F-75006 Paris France CNRS UMR7238 Lab Genom Microorganismes F-75006 Paris France

Background: Searching for similarities in a set of biological data is intrinsically difficult due to possible data points that should not be clustered, or that should group within several clusters. Under these hypotheses, hierarchical agglomerative clustering is not appropriate. Moreover, if the dataset is not known enough, like often is the case, supervised classification is not appropriate either. Results: CLAG (for clusters AGgregation) is an unsupervised non hierarchical clustering algorithm designed to cluster a large variety of biological data and to provide a clustered matrix and numerical values indicating cluster strength. CLAG clusterizes correlation matrices for residues in protein families, gene-expression and miRNA data related to various cancer types, sets of species described by multidimensional vectors of characters, binary matrices. It does not ask to all data points to cluster and it converges yielding the same result at each run. Its simplicity and speed allows it to run on reasonably large datasets. Conclusions: CLAG can be used to investigate the cluster structure present in biological datasets and to identify its underlying graph. It showed to be more informative and accurate than several known clustering methods, as hierarchical agglomerative clustering, k-means, fuzzy c-means, model-based clustering, affinity propagation clustering, and not to suffer of the convergence problem proper to this latter.

关键词： hierarchical Agglomerative cluster Affinity Propagation hierarchical cluster algorithm cluster Step Breast Cancer Dataset

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ARG-based genome-wide analysis of cacao cultivars

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BMC BIOINFORMATICS 2012年第Sup19期13卷 S17-S17页

作者： Utro, Filippo Cornejo, Omar Eduardo Livingstone, Donald Motamayor, Juan Carlos Parida, Laxmi IBM TJ Watson Res Computat Biol Ctr Yorktown Hts NY 10598 USA Stanford Univ Sch Med Dept Genet Stanford CA 94305 USA USDA Miami FL 33186 USA Mars Inc Miami FL 33158 USA

Background: Ancestral recombinations graph (ARG) is a topological structure that captures the relationship between the extant genomic sequences in terms of genetic events including recombinations. IRiS is a system that estimates the ARG on sequences of individuals, at genomic scales, capturing the relationship between these individuals of the species. Recently, this system was used to estimate the ARG of the recombining X Chromosome of a collection of human populations using relatively dense, bi-allelic SNP data. Results: While the ARG is a natural model for capturing the inter-relationship between a single chromosome of the individuals of a species, it is not immediately apparent how the model can utilize whole-genome (across chromosomes) diploid data. Also, the sheer complexity of an ARG structure presents a challenge to graph visualization techniques. In this paper we examine the ARG reconstruction for (1) genome-wide or multiple chromosomes, (2) multi-allelic and (3) extremely sparse data. To aid in the visualization of the results of the reconstructed ARG, we additionally construct a much simplified topology, a classification tree, suggested by the ARG. As the test case, we study the problem of extracting the relationship between populations of Theobroma cacao. The chocolate tree is an outcrossing species in the wild, due to self-incompatibility mechanisms at play. Thus a principled approach to understanding the inter-relationships between the different populations must take the shuffling of the genomic segments into account. The polymorphisms in the test data are short tandem repeats (STR) and are multi-allelic (sometimes as high as 30 distinct possible values at a locus). Each is at a genomic location that is bilaterally transmitted, hence the ARG is a natural model for this data. Another characteristic of this plant data set is that while it is genome-wide, across 10 linkage groups or chromosomes, it is very sparse, i.e., only 96 loci from a genome of approx

关键词： Short Tandem Repeat Classification Tree Ensemble Method hierarchical cluster algorithm Much Recent Common Ancestor

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Large-scale clustering of CAGE tag expression data

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BMC BIOINFORMATICS 2007年第1期8卷 1-10页

作者： Shimokawa, Kazuro Okamura-Oho, Yuko Kurita, Takio Frith, Martin C. Kawai, Jun Carninci, Piero Hayashizaki, Yoshihide RIKEN Yokohama Inst Genom Sci Ctr Genome Explorat Res GrpTsurumi Ku Yokohama Kanagawa 2300045 Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 305 Japan Univ Queensland Inst Mol Biosci St Lucia Qld 4072 Australia Wako Inst RIKEN Discovery Res Inst Genom Sci Lab Wako Saitama 351 Japan

Background: Recent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed. Results: We use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70-100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups. Conclusion: Our approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data.

关键词： Gene Ontology hierarchical cluster algorithm Multiple Transcription Start Site hierarchical cluster Link algorithm cluster Centroid Vector

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Exploring supervised and unsupervised methods to detect topics in biomedical text

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BMC BIOINFORMATICS 2006年第1期7卷 140-140页

作者： Lee, Minsuk Wang, Weiqing Yu, Hong Department of Biomedical Informatics Columbia University 622West 168th Street VC-5 NY 10032 USA Department of Molecular Pharmacology Albert Einstein College of Medicine Bronx NY 10461 USA

Background: Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering;and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. Results: We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naive Bayes to assign automatically a document into one or more predefined topics;and Topic clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features;namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naive Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Conclusion: Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering;on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings.

关键词： MeSH Term Semantic Type Unify Medical Language System hierarchical cluster algorithm Inverse Document Frequency

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Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering

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BMC BIOINFORMATICS 2004年第1期5卷 114-114页

作者： de Brevern, AG Hazout, S Malpertuy, A Univ Paris 07 INSERM E0346 EBGM F-75251 Paris 05 France Atragene Bioinformat F-91000 Evry France

Background: Microarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray datasets often contain missing values (MVs) representing a major drawback for the use of the clustering methods. Usually the MVs are not treated, or replaced by zero or estimated by the k-Nearest Neighbor (kNN) approach. The topic of the paper is to study the stability of gene clusters, defined by various hierarchical clustering algorithms, of microarrays experiments including or not MVs. Results: In this study, we show that the MVs have important effects on the stability of the gene clusters. Moreover, the magnitude of the gene misallocations is depending on the aggregation algorithm. The most appropriate aggregation methods ( e. g. complete-linkage and Ward) are highly sensitive to MVs, and surprisingly, for a very tiny proportion of MVs ( e. g. 1%). In most of the case, the MVs must be replaced by expected values. The MVs replacement by the kNN approach clearly improves the identification of co-expressed gene clusters. Nevertheless, we observe that kNN approach is less suitable for the extreme values of gene expression. Conclusion: The presence of MVs ( even at a low rate) is a major factor of gene cluster instability. In addition, the impact depends on the hierarchical clustering algorithm used. Some methods should be used carefully. Nevertheless, the kNN approach constitutes one efficient method for restoring the missing expression gene values, with a low error level. Our study highlights the need of statistical treatments in microarray data to avoid misinterpretation.

关键词： hierarchical cluster algorithm Close cluster Reference clusterings Topology Preservation Bayesian Principal Component Analysis

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Speeding up the Consensus clustering methodology for microarray data analysis

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algorithmS FOR MOLECULAR BIOLOGY 2011年第1期6卷 1-1页

作者： Giancarlo, Raffaele Utro, Filippo Univ Palermo Dipartimento Matemat & Informat I-90123 Palermo Italy IBM TJ Watson Res Ctr Yorktown Yeights NY USA

Background: The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e. g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of Consensus (Consensus clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, Consensus is a natural candidate for a speed-up. Results: Since the time-precision performance of Consensus depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for Consensus. That is, the closely related algorithm FC (Fast Consensus) that would have the same precision as Consensus with a substantially better time performance. The performance of FC has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, FC turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of Consensus. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guara

关键词： cluster algorithm Supplementary File cluster Solution Nonnegative Matrix Factorization hierarchical cluster algorithm

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Classifying lymphomas

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Genome Biology 2000年第2期1卷 1-4页

作者： Weitzman, Jonathan B

DNA microarray analysis has been used for the first time to define clinically relevant subtypes of lymphoma.

关键词： hierarchical cluster algorithm DLBCL Patient Lymphocyte Malignancy Molecular Portrait Relevant Subtype

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