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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

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Gene teams with relaxed proximity constraint

Gene teams with relaxed proximity constraint

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： S. Kim J.-H. Choi J. Yang School ofInformatics Center for Genomics and Bioinformatics Indiana University IN USA School of Informatics Indiana University IN USA EECS Case Western Reserve University OH USA

ISBN: (纸本)0769523447

Functionally related genes co-evolve, probably due to the strong selection pressure in evolution. Thus we expect that they are present in multiple genomes. Physical proximity among genes, known as gene team, is a very useful concept to discover functionally related genes in multiple genomes. However there are also many gene sets that do not preserve physical proximity. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraint. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. Especially, analysis of gene clusters that belong to B. subtilis and E. coli demonstrated that our model predicted many experimentally verified operons and functionally related clusters. Our program is fast enough to provide a service on the web at http://***/platcom/. Users can select any combination of 97 genomes to predict gene teams.

关键词： bioinformatics Genomics Informatics Sun Testing Predictive models Protein sequence Databases Gene expression Physics computing

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Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching

Estimating time-dependent gene networks from time series mic...

引用

Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： R. Yoshida S. Imoto T. Higuchi Institute for Statistical Mathematics Minato Tokyo Japan Human Genome Center Institute of Medical Science University of Tokyo Minato Tokyo Japan

In gene network estimation from time series microarray data, dynamic models such as differential equations and dynamic Bayesian networks assume that the network structure is stable through all time points, while the real network might changes its structure depending on time, affection of some shocks and so on. If the true network structure underlying the data changes at certain points, the fitting of the usual dynamic linear models fails to estimate the structure of gene network and we cannot obtain efficient information from data. To solve this problem, we propose a dynamic linear model with Markov switching for estimating time-dependent gene network structure from time series gene expression data. Using our proposed method, the network structure between genes and its change points are automatically estimated. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle time series data.

关键词： Bayesian methods Vectors Mathematical model Differential equations Gene expression Mathematics bioinformatics Time series analysis State estimation Statistical analysis

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Tree decomposition based fast search of RNA structures including pseudoknots in genomes

Tree decomposition based fast search of RNA structures inclu...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Y. Song C. Liu R. Malmberg F. Pan L. Cai Department of Computer Science University of Georgia Athens GA USA Department of Plant Biology University of Georgia Athens GA USA

Searching genomes for RNA secondary structure with computational methods has become an important approach to the annotation of non-coding RNAs. However, due to the lack of efficient algorithms for accurate RNA structure-sequence alignment, computer programs capable of fast and effectively searching genomes for RNA secondary structures have not been available. In this paper, a novel RNA structure profiling model is introduced based on the notion of a conformational graph to specify the consensus structure of an RNA family. Tree decomposition yields a small tree width t for such conformation graphs (e.g., t=2 for stem loops and only a slight increase for pseudo-knots). Within this modelling framework, the optimal alignment of a sequence to the structure model corresponds to finding a maximum valued isomorphic subgraph and consequently can be accomplished through dynamic programming on the tree decomposition of the conformational graph in time O(k/sup t/N/sup 2/), where k is a small parameter, and N is the size of the profiled RNA structure. Experiments show that the application of the alignment algorithm to search in genomes yields the same search accuracy as methods based on a covariance model with a significant reduction in computation time. In particular, very accurate searches of tmRNAs in bacteria genomes and of telomerase RNAs in yeast genomes can be accomplished in days, as opposed to months required by other methods. The tree decomposition based searching tool is free upon request and can be downloaded at our site http://***/RNA-Informatics/software/***.

关键词： RNA Genomics bioinformatics Computer science Tree graphs Plants (biology) computational biology Biology computing Sequences Stochastic systems

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Image data mining of fruitfly gene expression patterns

Image data mining of fruitfly gene expression patterns

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ieee computational systems bioinformatics conference, CSB Workshops and Poster Abstracts

作者： H. Peng Genomics Division Lawrence Berkeley National Laboratory University of California Berkeley CA USA

Understanding the spatio-temporal gene expression patterns is critical in studying genes roles and their complicated relationships. With the availability of high-resolution digital images of in situ mRNA hybridization expression pattern of Drosophila embryos collected through both the Berkeley Drosophila Transcriptional Network Project (BDTNP) and the Berkeley Drosophila Genome Project (BDGP), we will be able to answer a series of interesting questions, e.g., what are the variations of gene expression patterns, what are the patterning process of gene expression, etc. To achieve these goals at a large scale, it is extremely important to automate the image mining and informatics processes of embryogenesis expression patterns. We developed computer programs to register, compare and analyze these spatio-temporal pattern images. In this talk, I will review four projects on image mining of fruitfly embryo gene expression patterns: (a) Gaussian mixture model based embryonic expression pattern extraction and comparison, (b) Gene expression pattern clustering using novel MSTCUT and probabilistic ensemble clustering techniques, (c) Surface/volume models for 3D modeling and registration of early embryos, and (d) A manifold learning method for spatio-temporal registration of 3D gene expression patterns and reconstruction of the embryonic developmental time series.

关键词： Data mining Gene expression Embryo Digital images Genomics bioinformatics Large-scale systems Informatics Registers Image analysis

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Multi-metric and multi-substructure biclustering analysis for gene expression data

Multi-metric and multi-substructure biclustering analysis fo...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： S.Y. Kung Man-Wai Mak I. Tagkopoulos Princeton University USA Hong Kong Polytechnic University Hong Kong China

A good number of biclustering algorithms have been proposed for grouping gene expression data. Many of them have adopted matrix norms to define the similarity score of a bicluster. We shall show that almost all matrix metrics can be converted into vector norms while presenting the rank equivalence. Vector norms provide a much more efficient vehicle for biclustering analysis and computation. The advantages are two folds: ease of analysis and saving of computation. Most existing biclustering algorithms have also implicitly assumed the use of univariate (i.e., single metric) evaluation for identifying biclusters. Such an approach however overlooks the fundamental principle that genes (even though they may belong to the same gene group) (1) may be subdivided into different substructures; and (2) they may be co-expressed via a diversity of coherence models (a gene may participate in multiple pathways that may or may not be co-active under all conditions). The former leads to the adoption of a multi-substurcture analysis, while the latter to the multivariate analysis. This paper will show that the proposed multivariate and multi-subscluster analysis is very effective in identifying and classifying biologically relevant groups in genes and conditions. For example, it has successfully yielded highly discriminant and accurate classification based on known ribosomal gene groups.

关键词： Gene expression Cancer Neoplasms Matrix converters Drugs Data analysis bioinformatics Vehicles Coherence Fungi

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The significance of linearity of quantities in electrophoresed and blotted materials demonstrated by BandScan/spl copy/ - an analytical program

The significance of linearity of quantities in electrophores...

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ieee computational systems bioinformatics conference, CSB Workshops and Poster Abstracts

作者： R.S. Gunasekera H. Damodaran Y. Rajakarunanayake K. Hyland Department of Biology University of Houston Victoria TX USA San Ramon CA USA Institute of Metabolic Disease Baylor University Medical Center Dallas TX USA

There is still an undervalued need for quantitation in the biological, biochemical, and the molecular genetic sciences. Quantitation of electroblotted materials such as DNA, RNA, and proteins and the quantitation of molecular weights in electrophoresed gels is often necessary to an undistorted analysis. Quantitation would ensure, for instance, that the quantities of the substances being assayed fall within the linear range of that method by which they are being studied, once the linear range has been determined by a linearity study: the common phenomenon of saturation and its reverse, insufficient samples, could be easily detected. A biological data representation program is discussed that converts the results of gels and blots to numerical values and presents these values in a graphical display that is amenable to meaningful comparisons. The main goal of this work is to be able to quantitate electrophoresed and blotted molecular and biochemical results while insuring that the samples processed are within the linear range of the methods employed. This study demonstrates errors that can occur when a linearity study is not conducted. Versatile, easy-to-use quantitative graphics programs, such as the BandScan program, can further pipe numerical output to spreadsheets or statistical packages and provide the bridge between modern computation and traditional wet lab techniques. This analytical bioinformatics program that allows for statistical analysis is also a valuable tool for teaching purposes in bioinformatics and Molecular Biology. BandScan is available free for academic institutions and class room work from the corresponding author.

关键词： Linearity Biological materials bioinformatics Biology Genetics DNA RNA Proteins Displays Graphics

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Cell phenotype classification based on 3D cell image analysis

Cell phenotype classification based on 3D cell image analysi...

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ieee computational systems bioinformatics conference, CSB Workshops and Poster Abstracts

作者： F. Long H. Peng D. Sudar S. Lelievre D. Knowles Life Sciences /Genomics Division Lawrence Berkeley National Laboratory Berkeley CA USA Basic Medical Sciences Purdue University West Lafayette IN USA

Summary form only given. The accuracy of the histological classification of cells plays a determining role in disease diagnosis and treatment. Recent studies have shown that the distribution of chromatin-associated proteins reflects alterations in cell phenotype. Using 3D fluorescence images of cultured human breast epithelial cells with multiple known phenotypes, we have developed an automated method to classify the phenotype of epithelial cells based on their nuclear protein distribution. Features which describe the distribution of specific nuclear proteins are first measured, on a per nucleus basis, by our local bright feature (LBF) analysis technique. Features from thousands of nuclei with multiple, known phenotypes were then grouped by a novel voting-based clustering method into a number of clusters of similar pattern. This allows us to establish the statistical link between clusters and the phenotypes of the cells. Finally, we used this statistical link to predict the probable phenotype of individual or groups of nuclei. The results show that the combined use of 3D confocal imaging, image feature analysis, and clustering analysis provides an efficient way to predict the phenotype of epithelial cells based on the nuclear distribution of chromatin-associated proteins.

关键词： Image analysis Proteins bioinformatics Cells (biology) Genomics Biomedical imaging Medical diagnostic imaging Diseases Medical treatment Fluorescence

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Consensus genetic maps: a graph theoretic approach

Consensus genetic maps: a graph theoretic approach

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： B.N. Jackson S. Aluru P.S. Schnable Department of Electrical and Computer Engineering Iowa State University Ames IA USA Center for Plant Genomics Iowa State University Ames IA USA

A genetic map is an ordering of genetic markers constructed from genetic linkage data for use in linkage studies and experimental design. While traditional methods have focused on constructing maps from a single population study, increasingly maps are generated for multiple lines and populations of the same organism. For example, in crop plants, where the genetic variability is high, researchers have created maps for many populations. In the face of these new data, we address the increasingly important problem of generating a consensus map an ordering of all markers in the various population studies. In our method, each input map is treated as a partial order on a set of markers. To find the most consistent order shared between maps, we model the partial orders as directed graphs. We create an aggregate by merging the transitive closure of the input graphs and taking the transitive reduction of the result. In this process, cycles may need to be broken to resolve inconsistencies between the inputs. Tlie cycle breaking problem is NP-hard, but the problem size depends upon the scope of the inconsistency between the input graphs, which will be local if the input graphs are from closely related organisms. We present results of running the resulting software on maps generated from seven populations of the crop plant Zea Mays.

关键词： Biological cells Organisms bioinformatics Genomics Couplings Data engineering Genetic engineering Design engineering Crops Design for experiments

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Discriminative discovery of transcription factor binding sites from location data

Discriminative discovery of transcription factor binding sit...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Y. Kawada Y. Sakakibara Department of Biosciences and Informatics Keio University Yokohama Japan

The availability of genome-wide location analyses based on chromatin immunoprecipitation (CMP) data gives a new insight for in silico analysis of transcriptional regulations. We propose a novel discriminative discovery framework for precisely identifying transcriptional regulatory motifs from both positive and negative samples (sets of upstream sequences of both bound and unbound genes by a transcription factor (TF)) based on the genome-wide location data. In this framework, our goal is to find such discriminative motifs that best explain the location data in the sense that the motifs precisely discriminate the positive samples from the negative ones. First, in order to discover an initial set of discriminative substrings between positive and negative samples, we apply a decision tree learning method which produces a text-classification tree. We extract several clusters consisting of similar substrings from the internal nodes of the learned tree. Second, we start with initial profile-HMMs constructed from each cluster for representing putative motifs and iteratively refine the profile-HMMs to improve the discrimination accuracies. Our genome-wide experimental results on yeast show that our method successfully identifies the consensus sequences for known TFs in the literature and further presents significant performances for discriminating between positive and negative samples in all the TFs, while most other motif detecting methods show very poor performances on the problem of discriminations. Our learned profile-HMMs also improve false negative predictions of ChIP data.

关键词： bioinformatics Genomics Decision trees Data mining Fungi Machine learning Hidden Markov models Histograms Informatics Availability

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Clustering genes using gene expression and text literature data

Clustering genes using gene expression and text literature d...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： C. Yang E. Zeng T. Li G. Narasimhan Bioinformatics Research Group (BioRG) School of Computer Science Florida International University Miami FL USA

Clustering of gene expression data is a standard technique used to identify closely related genes. In this paper, we develop a new clustering algorithm, MSC (Multi-Source Clustering), to perform exploratory analysis using two or more diverse sources of data. In particular, we investigate the problem of improving the clustering by integrating information obtained from gene expression data with knowledge extracted from biomedical text literature. In each iteration of algorithm MSC, an EM-type procedure is employed to bootstrap the model obtained from one data source by starting with the cluster assignments obtained in the previous iteration using the other data sources. Upon convergence, the two individual models are used to construct the final cluster assignment. We compare the results of algorithm MSC for two data sources with the results obtained when the clustering is applied on the two sources of data separately. We also compare it with that obtained using the feature level integration method that performs the clustering after simply concatenating the features obtained from the two data sources. We show that the z-scores of the clustering results from MSC are better than that from the other methods. To evaluate our clusters better, function enrichment results are presented using terms from the Gene Ontology database. Finally, by investigating the success of motif detection programs that use the clusters, we show that our approach integrating gene expression data and text data reveals clusters that are biologically more meaningful than those identified using gene expression data alone.

关键词： Gene expression Clustering algorithms Data mining bioinformatics Data analysis Databases Information analysis Computer science Performance analysis Algorithm design and analysis

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