http://www.biodatamining.org/ The most accessed research articles published by BioData Mining 2010-02-22T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis. http://www.biodatamining.org/content/3/1/1 Georgios Pavlopoulos Theodoros Soldatos Adriano Barbosa-Silva Reinhard Schneider BioData Mining 2010, 3:1 2010-02-22T00:00:00Z doi:10.1186/1756-0381-3-1 BioData Mining 1756-0381 3 1 2010-02-22T00:00:00Z PDF The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing. http://www.biodatamining.org/content/1/1/12 Georgios Pavlopoulos Anna-Lynn Wegener Reinhard Schneider BioData Mining 2008, 1:12 2008-11-28T00:00:00Z doi:10.1186/1756-0381-1-12 BioData Mining 1756-0381 1 12 2008-11-28T00:00:00Z XML Background: 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. 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. Results: 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. http://www.biodatamining.org/content/2/1/3 Sondes Fayech Nadia Essoussi Mohamed Limam BioData Mining 2009, 2:3 2009-04-02T00:00:00Z doi:10.1186/1756-0381-2-3 BioData Mining 1756-0381 2 3 2009-04-02T00:00:00Z XML Background: In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. Methods: We introduce BiMine, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, BiMine relies on a new evaluation function called Average Spearman's rho (ASR). Second, BiMine uses a new tree structure, called Bicluster Enumeration Tree (BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, BiMine introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters. Results: The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that BiMine competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users. http://www.biodatamining.org/content/2/1/9 Wassim Ayadi Mourad Elloumi Jin-Kao Hao BioData Mining 2009, 2:9 2009-12-16T00:00:00Z doi:10.1186/1756-0381-2-9 BioData Mining 1756-0381 2 9 2009-12-16T00:00:00Z XML Background: Gene-centric analysis tools for genome-wide association study data are being developed both to annotate single locus statistics and to prioritize or group single nucleotide polymorphisms (SNPs) prior to analysis. These approaches require knowledge about the relationships between SNPs on a genotyping platform and genes in the human genome. SNPs in the genome can represent broader genomic regions via linkage disequilibrium (LD), and population-specific patterns of LD can be exploited to generate a data-driven map of SNPs to genes. Methods: In this study, we implemented LD-Spline, a database routine that defines the genomic boundaries a particular SNP represents using linkage disequilibrium statistics from the International HapMap Project. We compared the LD-Spline haplotype block partitioning approach to that of the four gamete rule and the Gabriel et al. approach using simulated data; in addition, we processed two commonly used genome-wide association study platforms. Results: We illustrate that LD-Spline performs comparably to the four-gamete rule and the Gabriel et al. approach; however as a SNP-centric approach LD-Spline has the added benefit of systematically identifying a genomic boundary for each SNP, where the global block partitioning approaches may falter due to sampling variation in LD statistics. Conclusion: LD-Spline is an integrated database routine that quickly and effectively defines the genomic region marked by a SNP using linkage disequilibrium, with a SNP-centric block definition algorithm. http://www.biodatamining.org/content/2/1/7 William Bush Guanhua Chen Eric Torstenson Marylyn Ritchie BioData Mining 2009, 2:7 2009-12-03T00:00:00Z doi:10.1186/1756-0381-2-7 BioData Mining 1756-0381 2 7 2009-12-03T00:00:00Z XML Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem. http://www.biodatamining.org/content/2/1/6 Vladimir Likic BioData Mining 2009, 2:6 2009-10-12T00:00:00Z doi:10.1186/1756-0381-2-6 BioData Mining 1756-0381 2 6 2009-10-12T00:00:00Z XML Background: Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers. Results: We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pivot objects. The heuristic efficiently finds pairs of similar objects and these help to mimic the greedy choices of full AHC. Quality of approximate AHC as compared to full AHC is studied with three measures. The first measure evaluates the global quality of the achieved clustering, while the second compares biological relevance using enrichment of biological functions in every subtree of the clusterings. The third measure studies how well the contents of subtrees are conserved between the clusterings. Conclusion: The HappieClust algorithm is well suited for large-scale gene expression visualization and analysis both on personal computers as well as public online web applications. The software is available from the URL http://www.quretec.com/HappieClust http://www.biodatamining.org/content/1/1/9 Meelis Kull Jaak Vilo BioData Mining 2008, 1:9 2008-09-22T00:00:00Z doi:10.1186/1756-0381-1-9 BioData Mining 1756-0381 1 9 2008-09-22T00:00:00Z XML Background: In computer science, database systems are based on the relational model founded by Edgar Codd in 1970. On the other hand, in the area of biology the word 'database' often refers to loosely formatted, very large text files. Although such bio-databases may describe conflicts or ambiguities (e.g. a protein pair do and do not interact, or unknown parameters) in a positive sense, the flexibility of the data format sacrifices a systematic query mechanism equivalent to the widely used SQL. Results: To overcome this disadvantage, we propose embeddable string-search commands on a Wiki-based system and designed a half-formatted database. As proof of principle, a database of flavonoid with 6902 molecular structures from over 1687 plant species was implemented on MediaWiki, the background system of Wikipedia. Registered users can describe any information in an arbitrary format. Structured part is subject to text-string searches to realize relational operations. The system was written in PHP language as the extension of MediaWiki. All modifications are open-source and publicly available. Conclusion: This scheme benefits from both the free-formatted Wiki style and the concise and structured relational-database style. MediaWiki supports multi-user environments for document management, and the cost for database maintenance is alleviated. http://www.biodatamining.org/content/1/1/7 Masanori Arita Kazuhiro Suwa BioData Mining 2008, 1:7 2008-09-17T00:00:00Z doi:10.1186/1756-0381-1-7 BioData Mining 1756-0381 1 7 2008-09-17T00:00:00Z XML Background: Protein families could be related to each other at broad levels that group them as superfamilies. These relationships are harder to detect at the sequence level due to high evolutionary divergence. Sequence searches are strongly directed and influenced by the best representatives of families that are viewed as starting points. PSSMs are useful approximations and mathematical representations of protein alignments, with wide array of applications in bioinformatics approaches like remote homology detection, protein family analysis, detection of new members and evolutionary modelling. Computational intensive searches have been performed using the neural network based sensitive sequence search method called FASSM to identify the Best Representative PSSMs for families reported in Pfam database version 22. Results: We designed a novel data mining approach for the assessment of individual sequences from a protein family to identify a single Best Representative PSSM profile (BRP) per protein family. Using the approach, a database of protein family-specific best representative PSSM profiles called 3PFDB has been developed. PSSM profiles in 3PFDB are curated using performance of individual sequence as a reference in a rigorous scoring and coverage analysis approach using FASSM. We have assessed the suitability of 10, 85,588 sequences derived from seed or full alignments reported in Pfam database (Version 22). Coverage analysis using FASSM method is used as the filtering step to identify the best representative sequence, starting from full length or domain sequences to generate the final profile for a given family. 3PFDB is a collection of best representative PSSM profiles of 8,524 protein families from Pfam database. Conclusion: Availability of an approach to identify BRPs and a curated database of best representative PSI-BLAST derived PSSMs for 91.4% of current Pfam family will be a useful resource for the community to perform detailed and specific analysis using family-specific, best-representative PSSM profiles. 3PFDB can be accessed using the URL: http://caps.ncbs.res.in/3pfdb http://www.biodatamining.org/content/2/1/8 Khader Shameer Paramasivam Nagarajan Kumar Gaurav Ramanathan Sowdhamini BioData Mining 2009, 2:8 2009-12-04T00:00:00Z doi:10.1186/1756-0381-2-8 BioData Mining 1756-0381 2 8 2009-12-04T00:00:00Z XML Background: Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. Results: We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Conclusion: Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis. http://www.biodatamining.org/content/1/1/11 Kristian Ovaska Marko Laakso Sampsa Hautaniemi BioData Mining 2008, 1:11 2008-11-21T00:00:00Z doi:10.1186/1756-0381-1-11 BioData Mining 1756-0381 1 11 2008-11-21T00:00:00Z XML