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

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Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

Pair stochastic tree adjoining grammars for aligning and pre...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： H. Matsui K. Sato Y. Sakakibara Department of Biosciences and Informatics Keio University Yokohama Japan

Since the whole genome sequences for many species are currently available, computational predictions of RNA secondary structures and computational identifications of those non-coding RNA regions by comparative genomics have become important, and require more advanced alignment methods. Recently, an approach of structural alignments for RNA sequences has been introduced to solve these problems. By structural alignments, we mean a pair-wise alignment to align an unfolded RNA sequence into a folded RNA sequence of known secondary structure. Pair HMMs on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignments of RNA secondary structures, but are incapable of handling pseudoknots. On the other hand, tree adjoining grammars (TAGs) is a subclass of context-sensitive grammar, which is suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots. We propose the pair stochastic tree adjoining grammars (PSTAGs) for modeling RNA secondary structures including pseudoknots and show the strong experimental evidences that modeling pseudoknot structures significantly improves the prediction accuracies of RNA secondary structures. First, we extend the notion of PHMMTSs defined on alignments of 'trees' to PSTAGs defined on alignments of "TAG (derivation) trees", which represent a top-down parsing process of TAGs and are functionally equivalent to derived trees of TAGs. Second, we modify PSTAGs so that it takes as input a pair of a linear sequence and a TAG tree representing a pseudoknot structure of RNA to produce a structural alignment. Then, we develop a polynomial-time algorithm for obtaining an optimal structural alignment by PSTAGs, based on dynamic programming parser. We have done several computational experiments for predicting pseudoknots by PSTAGs, and our computational experiments suggests that prediction of RNA pseudoknot structures by our method are more ef

关键词： Stochastic processes RNA Genomics bioinformatics Predictive models Technical Activities Guide -TAG Biology computing Hidden Markov models Tree data structures Context modeling

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Recurrence time statistics: versatile tools for genomic DNA sequence analysis

Recurrence time statistics: versatile tools for genomic DNA ...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Yinhe Cao Wen-wen Tung Jian-Bo Gao Department of Electrical and Computer Engineering University of Florida Gainesville FL USA National Center for Atmospheric Research Boulder CO USA

With the completion of the human and a few model organisms' genomes, and the genomes of many other organisms waiting to be sequenced, it has become increasingly important to develop faster computational tools which are capable of easily identifying the structures and extracting features from DNA sequences. One of the more important structures in a DNA sequence is repeat-related. Often they have to be masked before protein coding regions along a DNA sequence are to be identified or redundant expressed sequence tags (ESTs) are to be sequenced. Here we report a novel recurrence time based method for sequence analysis. The method can conveniently study all kinds of periodicity and exhaustively find all repeat-related features from a genomic DNA sequence. An efficient codon index is also derived from the recurrence time statistics, which has the salient features of being largely species-independent and working well on very short sequences. Efficient codon indices are key elements of successful gene finding algorithms, and are particularly useful for determining whether a suspected EST belongs to a coding or noncoding region. We illustrate the power of the method by studying the genomes of E. coli, the yeast S. cervisivae, the nematode worm C. elegans, and the human, Homo sapiens. computationally, our method is very efficient. It allows us to carry out analysis of genomes on the whole genomic scale by a PC.

关键词： Statistical analysis Genomics bioinformatics Sequences Humans Organisms DNA computing Feature extraction Proteins Statistics

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Are we there yet? Genomic profiling and mechanism in cancer research

Are we there yet? Genomic profiling and mechanism in cancer ...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： P. Meltzer S. Davis K. Baird Yidong Chen Cancer Genetics Branch National Human Genome Research Institute USA

The advent of the human genome sequence and genomic profiling technologies has impacted all of biomedical research. The effect has been particularly great on cancer research because cancer is a disease uniformly characterized by disturbed genome function. Studies which take a whole genome view of cancer are transforming the classification of tumors, enhancing understanding of tumorigenesis and promising to affect patient care. However, the development of large sets of genomic data has challenged cancer biologists who as a rule have never encountered data on this scale. Implementation of conventional statistical techniques has brought a level of order to the biologist's desktop, but significant problems remain to be solved. The challenge remains to extract the maximum useful information from genomic data. This requires both new algorithmic approaches which can accommodate the complexity of the underlying rules of genome function and effective strategies for linking different types of genomic data to each other and to external sources of gene and genome annotation. Progress in this arena will be necessary to fully realize the potential of genomics to impact on our ability to intervene in the clinical arena. Of particular importance will be approaches which help identify critical genes and pathways which are essential to tumor growth and survival. Examples will be presented which illustrate the boundaries of conventional analyses based on studies of tumor profiling at the expression and gene copy number level as well as experimental models designed to explore the function of specific pathways.

关键词： Genomics bioinformatics Cancer Neoplasms Humans Genetics Diseases Data mining Joining processes

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Gene recovery of two genome-filtration sequencing techniques when applied to the maize genome

Gene recovery of two genome-filtration sequencing techniques...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Xiequn Xu W.B. Barbazuk Yucheng Feng K. Schubert A.P. Chan G. Pertea Li Zheng Foo Cheung Yuandan Lee Donald Danforth Plant Science Center Saint Louis USA Donald Danforth Plant Science Center The Institute of Genomics Research Rockville USA

The large size of the maize genome and the expectation that most of the genome is represented by repetitive elements is a challenge to standard genome sequencing techniques. Genome-filtration sequencing techniques may target gene-rich regions in the genome. Two such approaches, methylation filtration and high Cot selection, may provide rapid and cost-effective alternative to sequencing the maize genome. Approximately 450k sequence reads have been obtained from both methylation filtration (MF) and high Cot (HC) libraries, and these sequences have been clustered and assembled. An analysis was undertaken to examine whether MF and MC enrich for maize genes and target non-identical sequence space. Simple sequence repeat analysis and mapped marker coverage provide gauges to examine whether MF and HC sample identical sequence space. Marker hits also provide evidence that MF and HC enrich for unique genic portions of the genome. Finally, the identification of maize and other protein sequences in the MF and HC sequence sets can indicate the expected fraction and coverage of the maize gene space by MF and HC.

关键词： Genomics bioinformatics Assembly Libraries Filtration Proteins Cloning Kinetic theory Helium

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Dynamic algorithm for inferring qualitative models of gene regulatory networks

Dynamic algorithm for inferring qualitative models of gene r...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Zheng Yun Kwoh Chee Keong BIRC School of Computer Engineering Nanyang Technological University Singapore School of Computer Engineering Nanyang Technological University Singapore

It is still an open problem to identify functional relations with o(N /spl middot/ n/sup k/) time for any domain, where N is the number of learning instances, n is the number of genes (or variables) in the gene regulatory network (GRN) models and k is the indegree of the genes. To solve the problem, we introduce a novel algorithm, DFL (discrete function learning), for reconstructing qualitative models of GRNs from gene expression data in this paper. We analyze the complexity of O(k /spl middot/ N /spl middot/ n/sup 2/) on the average and its data requirements. We also perform experiments on both synthetic and Cho et al. yeast cell cycle gene expression data to validate the efficiency and prediction performance of the DFL algorithm. The experiments of synthetic Boolean networks show that the DFL algorithm is more efficient than current algorithms without loss of prediction performances. The results of yeast cell cycle gene expression data show that the DFL algorithm can identify biologically significant models with reasonable accuracy, sensitivity and high precision with respect to the literature evidences. We further introduce a method called /spl epsi/ function to deal with noises in data sets. The experimental results show that the /spl epsiv/ function method is a good supplement to the DFL algorithm.

关键词： Heuristic algorithms Gene expression Biological system modeling Fungi bioinformatics Differential equations Cells (biology) Genomics RNA Proteins

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Segmental duplications containing tandem repeated genes encoding putative deubiquitinating enzymes

Segmental duplications containing tandem repeated genes enco...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Hong Liu Li Li A. Zilberstein C.S. Hahn Aventis Pharmaceuticals Inc. Bridgewater NJ USA

Both inter- and intra-chromosomal segmental duplications are known to occur in human genome during evolution. Few cases of such segments involving functional genes have been reported. While searching for the human orthologs of murine hematopoietic deubiquitinating enzymes (DUBs), we identified four clusters of DUB-like genes on chromosome 4p15 and chromosome 8p22-23 that are over 90% identical to each other at the DNA level. These genes are expressed in a cell type- and activation-specific manner, with different clusters possessing potentially distinct expression profiles. Examining the surrounding sequences of these gene duplication events, we have identified previously unreported conserved sequence elements that are as large as 35 to 74 kb encircling the gene clusters. Traces of these elements are also found on chromosome 12p13 and chromosome 11q13. The coding and immediate upstream sequences for DUB - like genes as well as the surrounding conserved elements, are present in the chimpanzee trace database, but not in rodent genome. We hypothesize that the segments containing these DUB clusters and surrounding elements arose relatively recently in evolution through inter- and intra-chromosomal duplicative transpositions, following the divergence of primates and rodents. Genome wide systematical search of the segmental duplication containing duplicated gene cluster has been performed.

关键词： Encoding Biochemistry Biological cells Genomics bioinformatics Sequences Humans Rodents DNA Databases

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Minimum entropy clustering and applications to gene expression analysis

Minimum entropy clustering and applications to gene expressi...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Haifeng Li Keshu Zhang Tao Jiang University of California Riverside CA USA Rensselaer Polytechnic Institute Troy NY USA

Clustering is a common methodology for analyzing the gene expression data. We present a new clustering algorithm from an information-theoretic point of view. First, we propose the minimum entropy (measured on a posteriori probabilities) criterion, which is the conditional entropy of clusters given the observations. Pane's inequality indicates that it could be a good criterion for clustering. We generalize the criterion by replacing Shannon's entropy with Havrda-Charvat's structural /spl alpha/-entropy. Interestingly, the minimum entropy criterion based on structural /spl alpha/-entropy is equal to the probability error of the nearest neighbor method when /spl alpha/ = 2. This is another evidence that the proposed criterion is good for clustering. With a nonparametric approach for estimating a posteriori probabilities, an efficient iterative algorithm is then established to minimize the entropy. The experimental results show that the clustering algorithm performs significantly better than k-means/medians, hierarchical clustering, SOM, and EM in terms of adjusted Rand index. Particularly, our algorithm performs very well even when the correct number of clusters is unknown. In addition, most clustering algorithms produce poor partitions in presence of outliers while our method can correctly reveal the structure of data and effectively identify outliers simultaneously.

关键词： Entropy Gene expression Clustering algorithms Iterative algorithms Partitioning algorithms Data analysis Biological processes bioinformatics Nearest neighbor searches Cells (biology)

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Improved Fourier transform method for unsupervised cell-cycle regulated gene prediction

Improved Fourier transform method for unsupervised cell-cycl...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： K.R. Krishna Murthy Liu Jiah Hua Genome Institute of Singapore Singapore

Cell-cycle regulated gene prediction using microarray time-course measurements of the mRNA expression levels of genes has been used by several researchers. The popularly employed approach is Fourier transform (FT) method in conjunction with the set of known cell-cycle regulated genes. In the absence of training data, Fourier transform method is sensitive to noise, additive monotonic component arising from cell population growth and deviation from strict sinusoidal form of expression. Known cell cycle regulated genes may not be available for certain organisms or using them for training may bias the prediction. In This work we propose an improved Fourier transform (IFT) method which takes care of several factors such as monotonic additive component of the cell-cycle expression, irregular or partial-cycle sampling of gene expression. The proposed algorithm does not need any known cell-cycle regulated genes for prediction. Apart from alleviating need for training set, it also removes bias towards genes similar to the training set. We have evaluated the developed method on two publicly available datasets: yeast cell-cycle data and HeLa cell-cycle data. The proposed algorithm has performed competitively on both datasets with that of the supervised Fourier transform method used. It outperformed other unsupervised methods such as partial least squares (PLS) and single pulse modeling (SPM). This method is easy to comprehend and implement, and runs faster.

关键词： Fourier transforms bioinformatics Genomics Sampling methods Gene expression Fungi Additive noise Organisms Least squares methods Phase estimation

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Comparative analysis of gene sets in the gene ontology space under the multiple hypothesis testing framework

Comparative analysis of gene sets in the gene ontology space...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Sheng Zhong Lu Tian Cheng Li K.-F. Storch W.H. Wong Department of Biostatistics Harvard University USA Department of Biostatistical Sciences Dana Farber Cancer Institute USA Department of Neurobiology Harvard Medical School USA Department of Statistics Harvard University USA

The gene ontology (GO) resource can be used as a powerful tool to uncover the properties shared among, and specific to, a list of genes produced by high-throughput functional genomics studies, such as microarray studies. In the comparative analysis of several gene lists, researchers maybe interested in knowing which GO terms are enriched in one list of genes but relatively depleted in another. Statistical tests such as Fisher's exact test or Chi-square test can be performed to search for such GO terms. However, because multiple GO terms are tested simultaneously, individual p-values from individual tests do not serve as good indicators for picking GO terms. Furthermore, these multiple tests are highly correlated, usual multiple testing procedures that work under an independence assumption are not applicable. In this paper we introduce a procedure, based on false discovery rate (FDR), to treat this correlated multiple testing problem. This procedure calculates a moderately conserved estimator of q-value for every GO term. We identify the GO terms with q-values that satisfy a desired level as the significant GO terms. This procedure has been implemented into the GoSurfer software. GoSurfer is a windows based graphical data mining tool. It is freely available at http://***.

关键词： Ontologies Testing bioinformatics Cancer Genomics Data mining Medical tests Statistical analysis Performance evaluation Data visualization

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Separation of ion types in tandem mass spectrometry data interpretation - a graph-theoretic approach

Separation of ion types in tandem mass spectrometry data int...

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Proceedings of the ieee computational systems bioinformatics conference, CSB

作者： Bo Yan Chongle Pan V.N. Olman R.L. Hettich Ying Xu Department of Biochemical and Molecular Biology University of Georgia GA USA Genome Science and Technology Graduate School University of Tennessee TN USA Chemical Sciences Division Oak Ridge National Laboratory TN USA

Mass spectrometry is one of the most popular analytical techniques for identification of individual proteins in a protein mixture, one of the basic problems in proteomics. It identifies a protein through identifying its unique mass spectral pattern. While the problem is theoretically solvable, it remains a challenging problem computationally. One of the key challenges comes from the difficulty in distinguishing the N- and C-terminus ions, mostly b- and y-ions respectively. In this paper, we present a graph algorithm for solving the problem of separating b- from y-ions in a set of mass spectra. We represent each spectral peak as a node and consider two types of edges: a type-1 edge connects two peaks possibly of the same ion types and a type-2 edge connects two peaks possibly of different ion types, predicted based on local information. The ion-separation problem is then formulated and solved as a graph partition problem, which is to partition the graph into three subgraphs, namely b-, y-ions and others respectively, so to maximize the total weight of type-1 edges while minimizing the total weight of type-2 edges within each subgraph. We have developed a dynamic programming algorithm for rigorously solving this graph partition problem and implemented it as a computer program PRIME. We have tested PRIME on 18 data sets of high accurate FT-ICR tandem mass spectra and found that it achieved /spl sim/90% accuracy for separation of b- and y-ions.

关键词： Mass spectroscopy Peptides Databases bioinformatics Proteomics Protein sequence computational biology Chemical technology Genomics Sequences

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