http://www.almob.org The most accessed research articles published by Algorithms for Molecular Biology 2010-02-24T00:00:00Z 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/ Background: Supertree methods synthesize collections of small phylogenetic trees with incomplete taxon overlap into comprehensive trees, or supertrees, that include all taxa found in the input trees. Supertree methods based on the well established Robinson-Foulds (RF) distance have the potential to build supertrees that retain much information from the input trees. Specifically, the RF supertree problem seeks a binary supertree that minimizes the sum of the RF distances from the supertree to the input trees. Thus, an RF supertree is a supertree that is consistent with the largest number of clusters (or clades) from the input trees. Results: We introduce efficient, local search based, hill-climbing heuristics for the intrinsically hard RF supertree problem on rooted trees. These heuristics use novel non-trivial algorithms for the SPR and TBR local search problems which improve on the time complexity of the best known (naive) solutions by a factor of Theta(n) and Theta(n^2) respectively (where n is the number of taxa, or leaves, in the supertree). We use an implementation of our new algorithms to examine the performance of the RF supertree method and compare it to matrix representation with parsimony (MRP) and the triplet supertree method using four supertree data sets. Not only did our RF heuristic provide fast estimates of RF supertrees in all data sets, but the RF supertrees also retained more of the information from the input trees (based on the RF distance) than the other supertree methods. Conclusions: Our heuristics for the RF supertree problem, based on our new local search algorithms, make it possible for the first time to estimate large supertrees by directly optimizing the RF distance from rooted input trees to the supertrees. This provides a new and fast method to build accurate supertrees. RF supertrees may also be useful for estimating majority-rule(-) supertrees, which are a generalization of majority-rule consensus trees. http://www.almob.org/content/5/1/18 Mukul Bansal J Burleigh Oliver Eulenstein David Fernandez-Baca Algorithms for Molecular Biology 2010, 5:18 2010-02-24T00:00:00Z doi:10.1186/1748-7188-5-18 Algorithms for Molecular Biology 1748-7188 5 18 2010-02-24T00:00:00Z PDF Background: This paper describes the theory and implementation of a new software tool, called Jane, for the study of historical associations. This problem arises in parasitology (associations of hosts and parasites), molecular systematics (associations of orderings and genes), and biogeography (associations of regions and orderings). The underlying problem is that of reconciling pairs of trees subject to biologically plausible events and costs associated with these events. Existing software tools for this problem have strengths and limitations, and the new Jane tool described here provides functionality that complements existing tools. Results: The Jane software tool uses a polynomial time dynamic programming algorithm in conjunction with a genetic algorithm to find very good, and often optimal, solutions even for relatively large pairs of trees. The tool allows the user to provide rich timing information on both the host and parasite trees. In addition the user can limit host switch distance and specify multiple host switch costs by specifying regions in the host tree and costs for host switches between pairs of regions. Jane also provides a graphical user interface that allows the user to interactively experiment with modifications to the solutions found by the program. Conclusions: Jane is shown to be a useful tool for cophylogenetic reconstruction. Its functionality complements existing tools and it is therefore likely to be of use to researchers in the areas of parasitology, molecular systematics, and biogeography. http://www.almob.org/content/5/1/16 Chris Conow Daniel Fielder Yaniv Ovadia Ran Libeskind-Hadas Algorithms for Molecular Biology 2010, 5:16 2010-02-03T00:00:00Z doi:10.1186/1748-7188-5-16 Algorithms for Molecular Biology 1748-7188 5 16 2010-02-03T00:00:00Z XML Background: The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a classification that is likely to cause heterogeneity within the two groups. Rather than dividing the study population into cases and controls, it is better to identify the phenotype of a complex disease by a set of intermediate risk factors. But these risk factors often vary over time and are therefore repeatedly measured. Results: We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Via a two-step approach, we summarized the time courses of each individual and, secondly apply these to penalized nonlinear canonical correlation analysis to obtain sparse results. Conclusions: Application of this method to two datasets which study the genetic background of cardiovascular diseases, show that compared to progression over time, mainly the constant levels in time are associated with sets of SNPs. http://www.almob.org/content/5/1/17 Sandra Waaijenborg Aeilko Zwinderman Algorithms for Molecular Biology 2010, 5:17 2010-02-11T00:00:00Z doi:10.1186/1748-7188-5-17 Algorithms for Molecular Biology 1748-7188 5 17 2010-02-11T00:00:00Z XML Background: In bioinformatics it is common to search for a pattern of interest in a potentially large set of rather short sequences (upstream gene regions, proteins, exons, etc.). Although many methodological approaches allow practitioners to compute the distribution of a pattern count in a random sequence generated by a Markov source, no specific developments have taken into account the counting of occurrences in a set of independent sequences. We aim to address this problem by deriving efficient approaches and algorithms to perform these computations both for low and high complexity patterns in the framework of homogeneous or heterogeneous Markov models. Results: The latest advances in the field allowed us to use a technique of optimal Markov chain embedding based on deterministic finite automata to introduce three innovative algorithms. Algorithm 1 is the only one able to deal with heterogeneous models. It also permits to avoid any product of convolution of the pattern distribution in individual sequences. When working with homogeneous models, Algorithm 2 yields a dramatic reduction in the complexity by taking advantage of previous computations to obtain moment generating functions efficiently. In the particular case of low or moderate complexity patterns, Algorithm 3 exploits power computation and binary decomposition to further reduce the time complexity to a logarithmic scale. All these algorithms and their relative interest in comparison with existing ones were then tested and discussed on a toy-example and three biological data sets: structural patterns in protein loop structures, PROSITE signatures in a bacterial proteome, and transcription factors in upstream gene regions. On these data sets, we also compared our exact approaches to the tempting approximation that consists in concatenating the sequences in the data set into a single sequence. Conclusions: Our algorithms prove to be effective and able to handle real data sets with multiple sequences, as well as biological patterns of interest, even when the latter display a high complexity (PROSITE signatures for example). In addition, these exact algorithms allow us to avoid the edge effect observed under the single sequence approximation, which leads to erroneous results, especially when the marginal distribution of the model displays a slow convergence toward the stationary distribution. We end up with a discussion on our method and on its potential improvements. http://www.almob.org/content/5/1/15 Gregory Nuel Leslie Regad Juliette Martin Anne-Claude Camproux Algorithms for Molecular Biology 2010, 5:15 2010-01-26T00:00:00Z doi:10.1186/1748-7188-5-15 Algorithms for Molecular Biology 1748-7188 5 15 2010-01-26T00:00:00Z XML Background: Accurate classification of microarray data is critical for successful clinical diagnosis and treatment.However, the "curse of dimensionality" problem, and noise in the data undermines the performance of many algorithms.MethodIn order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that definethe domain knowledge are difficult to obtain since usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data. Results: The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and k nearest neighbor (kNN), especially when the data contains a great number of noise.AvailabilityThe source code is attached as an additional file of this paper. http://www.almob.org/content/5/1/14 Bangpeng Yao Shao Li Algorithms for Molecular Biology 2010, 5:14 2010-01-06T00:00:00Z doi:10.1186/1748-7188-5-14 Algorithms for Molecular Biology 1748-7188 5 14 2010-01-06T00:00:00Z PDF Background: Automated software tools for multiple alignment often fail to produce biologically meaningful results. In such situations, expert knowledge can help to improve the quality of alignments. Results: Herein, we describe a semi-automatic version of the alignment program DIALIGN that can take pre-defined constraints into account. It is possible for the user to specify parts of the sequences that are assumed to be homologous and should therefore be aligned to each other. Our software program can use these sites as anchor points by creating a multiple alignment respecting these constraints. This way, our alignment method can produce alignments that are biologically more meaningful than alignments produced by fully automated procedures. As a demonstration of how our method works, we apply our approach to genomic sequences around the Hox gene cluster and to a set of DNA-binding proteins. As a by-product, we obtain insights about the performance of the greedy algorithm that our program uses for multiple alignment and about the underlying objective function. This information will be useful for the further development of DIALIGN. The described alignment approach has been integrated into the TRACKER software system. http://www.almob.org/content/1/1/6 Burkhard Morgenstern Sonja Prohaska Dirk Pohler Peter Stadler Algorithms for Molecular Biology 2006, 1:6 2006-04-19T00:00:00Z doi:10.1186/1748-7188-1-6 Algorithms for Molecular Biology 1748-7188 1 6 2006-04-19T00:00:00Z XML This is a meeting report for the 6th SIGKDD Workshop on Data Mining in Bioinformatics. http://www.almob.org/content/2/1/4 Mohammed Zaki George Karypis Jiong Yang Algorithms for Molecular Biology 2007, 2:4 2007-04-11T00:00:00Z doi:10.1186/1748-7188-2-4 Algorithms for Molecular Biology 1748-7188 2 4 2007-04-11T00:00:00Z XML Background: The problem of computationally predicting the secondary structure (or folding) of RNA molecules was first introduced more than thirty years ago and yet continues to be an area of active research and development. The basic RNA-folding problem of finding a maximum cardinality, non-crossing, matching of complimentary nucleotides in an RNA sequence of length n, has an O(n3)-time dynamic programming solution that is widely applied. It is known that an o(n3) worst-case time solution is possible, but the published and suggested methods are complex and have not been established to be practical. Significant practical improvements to the original dynamic programming method have been introduced, but they retain the O(n3) worst-case time bound when n is the only problem-parameter used in the bound. Surprisingly, the most widely-used, general technique to achieve a worst-case (and often practical) speed up of dynamic programming, the Four-Russians technique, has not been previously applied to the RNA-folding problem. This is perhaps due to technical issues in adapting the technique to RNA-folding. Results: In this paper, we give a simple, complete, and practical Four-Russians algorithm for the basic RNA-folding problem, achieving a worst-case time-bound of O(n3/log(n)). Conclusions: We show that this time-bound can also be obtained for richer nucleotide matching scoring-schemes, and that the method achieves consistent speed-ups in practice. The contribution is both theoretical and practical, since the basic RNA-folding problem is often solved multiple times in the inner-loop of more complex algorithms, and for long RNA molecules in the study of RNA virus genomes. http://www.almob.org/content/5/1/13 Yelena Frid Dan Gusfield Algorithms for Molecular Biology 2010, 5:13 2010-01-04T00:00:00Z doi:10.1186/1748-7188-5-13 Algorithms for Molecular Biology 1748-7188 5 13 2010-01-04T00:00:00Z XML Background: DIALIGN-T is a reimplementation of the multiple-alignment program DIALIGN. Due to several algorithmic improvements, it produces significantly better alignments on locally and globally related sequence sets than previous versions of DIALIGN. However, like the original implementation of the program, DIALIGN-T uses a a straight-forward greedy approach to assemble multiple alignments from local pairwise sequence similarities. Such greedy approaches may be vulnerable to spurious random similarities and can therefore lead to suboptimal results. In this paper, we present DIALIGN-TX, a substantial improvement of DIALIGN-T that combines our previous greedy algorithm with a progressive alignment approach. Results: Our new heuristic produces significantly better alignments, especially on globally related sequences, without increasing the CPU time and memory consumption exceedingly. The new method is based on a guide tree; to detect possible spurious sequence similarities, it employs a vertex-cover approximation on a conflict graph. We performed benchmarking tests on a large set of nucleic acid and protein sequences For protein benchmarks we used the benchmark database BALIBASE 3 and an updated release of the database IRMBASE 2 for assessing the quality on globally and locally related sequences, respectively. For alignment of nucleic acid sequences, we used BRAliBase II for global alignment and a newly developed database of locally related sequences called DIRM-BASE 1. IRMBASE 2 and DIRMBASE 1 are constructed by implanting highly conserved motives at random positions in long unalignable sequences. Conclusion: On BALIBASE3, our new program performs significantly better than the previous program DIALIGN-T and outperforms the popular global aligner CLUSTAL W, though it is still outperformed by programs that focus on global alignment like MAFFT, MUSCLE and T-COFFEE. On the locally related test sets in IRMBASE 2 and DIRM-BASE 1, our method outperforms all other programs while MAFFT E-INSi is the only method that comes close to the performance of DIALIGN-TX. http://www.almob.org/content/3/1/6 Amarendran Subramanian Michael Kaufmann Burkhard Morgenstern Algorithms for Molecular Biology 2008, 3:6 2008-05-27T00:00:00Z doi:10.1186/1748-7188-3-6 Algorithms for Molecular Biology 1748-7188 3 6 2008-05-27T00:00:00Z XML Background: To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility. Results: We compared eight conventional methods for ranking genes: weighted average difference (WAD), average difference (AD), fold change (FC), rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT) with six preprocessing algorithms (PLIER, VSN, FARMS, multi-mgMOS (mmgMOS), MBEI, and GCRMA). A total of 36 real experimental datasets was evaluated on the basis of the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the RP method performed well for VSN-, FARMS-, MBEI-, and GCRMA-preprocessed data, and the WAD method performed well for mmgMOS-preprocessed data. Our analysis of the MicroArray Quality Control (MAQC) project's datasets showed that the FC-based gene ranking methods (WAD, AD, FC, and RP) had a higher level of reproducibility: The percentages of overlapping genes (POGs) across different sites for the FC-based methods were higher overall than those for the t-statistic-based methods (modT, samT, shrinkT, and ibmT). In particular, POG values for WAD were the highest overall among the FC-based methods irrespective of the choice of preprocessing algorithm. Conclusion: Our results demonstrate that to increase sensitivity, specificity, and reproducibility in microarray analyses, we need to select suitable combinations of preprocessing algorithms and gene ranking methods. We recommend the use of FC-based methods, in particular RP or WAD. http://www.almob.org/content/4/1/7 Koji Kadota Yuji Nakai Kentaro Shimizu Algorithms for Molecular Biology 2009, 4:7 2009-04-22T00:00:00Z doi:10.1186/1748-7188-4-7 Algorithms for Molecular Biology 1748-7188 4 7 2009-04-22T00:00:00Z XML