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检索条件"任意字段=Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004"

共 371 条记录，以下是91-100 订阅

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Pathway mapping with operon information: an integer-programming method

Pathway mapping with operon information: an integer-programm...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Fenglou Mao V. Olman Ying Xu Zhengchang Su D. Chuang Computational Systems Biology Lab Biochemistry & Molecular Biology Department Computational Biology Institute Oak Ridge National Laboratory University of Georgia USA

Biological pathway mapping is an important problem in the post-genomic era. We now present a new algorithm for pathway mapping in microbes. The algorithm considers not only sequence similarity among the template and target genes, but also the operon structures in the target genome. We formulated the mapping problem as a graph finding problem, and solved it by an integer-programming (IP) method. The goal is to minimize a linear object function subject to six constraints, such that maximal sequence similarity among the template and target genes are achieved, and at the same time, a minimal number of operons are covered in the target genome. Compared to our previous minimal spanning tree (MST) algorithm, the IP method has the following advantages: i) It is much faster and thus can map larger pathway involving a much large set of genes. ii) The IP method looks into the details of genes in the operons, and consequently avoids the many-to-one mapping mistakes that sometimes occur in the MST algorithm. We have compiled a large pathway training set to optimize the parameters of the program, and tested it by mapping 16 complex pathways from BioCyc onto *** K12 genome and the results are very promising.

关键词： Genomics bioinformatics Sequences computational systems biology Biochemistry computational biology Laboratories Testing Phylogeny Robustness

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A new hardware architecture for genomic and proteomic sequence alignment

A new hardware architecture for genomic and proteomic sequen...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： G. Knowles P. Gardner-Stephen Embedded Systems Laboratory School of Informatics and Engineering Flinders University Adelaide Australia

We describe a novel hardware architecture for genomic and proteomic sequence alignment which achieves a speed-up of two to three orders of magnitude over Smith-Waterman dynamic programming (DP) in hardware. In our previous papers, we introduce several features of our search algorithm, DASH, which outperforms NCII-Blast (BLAST) by an order of magnitude in software, and has better sensitivity. Indeed, DASH has been shown to have excellent sensitivity compared to Smith-Waterman. It is designed around the principle of considering genomic and proteomic sequence alignments to typically consist of regions of high homology (the diagonals) interspersed with regions of low homology. In DASH, the optimal solution consists of such diagonals joined by regions of exact DP. This is affordable due to the small area of these interconnecting regions. Accordingly, we have designed a chip which finds the diagonals and performs the inter-region DP directly in hardware. On a Xilinx Vitex II, XC2V6000, FPGA, it performs over 10/sup 12/ base comparisons/second.

关键词： Hardware Genomics bioinformatics Proteomics Sequences Field programmable gate arrays Computer architecture Throughput Pipelines Proteins

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A self-tuning method for one-chip SNP identification

A self-tuning method for one-chip SNP identification

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： M. Molla J. Shavlik T. Albert T. Richmond S. Smith University of Wisconsin Madison USA NimbleGen Systems Inc.

Current methods for interpreting oligonucleotide-based SNP-detection microarrays, SNP chips, are based on statistics and require extensive parameter tuning as well as extremely high-resolution images of the chip being processed. We present a method, based on a simple data-classification technique called nearest-neighbors that, on haploid organisms, produces results comparable to the published results of the leading statistical methods and requires very little in the way of parameter tuning. Furthermore, it can interpret SNP chips using lower-resolution scanners of the type more typically used in current microarray experiments. Along with our algorithm, we present the results of a SNP-detection experiment where, when independently applying this algorithm to six identical SARS SNP chips, we correctly identify all 24 SNPs in a particular strain of the SARS virus, with between 6 and 13 false positives across the six experiments.

关键词： Probes bioinformatics Organisms Genomics Tuning Statistical analysis Capacitive sensors DNA Statistics Databases

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DASH: localising dynamic programming for order of magnitude faster, accurate sequence alignment

DASH: localising dynamic programming for order of magnitude ...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： P. Gardner-Stephen G. Knowles Embedded Systems Laboratory School of Informatics & Engineering Flinders University Adelaide Australia

In this paper we present our genomic and proteomic sequence alignment algorithm, DASH, which results in order of magnitude speed improvement when compared to NCBI-BLAST 2.2.6, with superior sensitivity. Dynamic programming (DP) is the predominant contributor to search time for algorithms such as BLAST and FastA/P. Improving the efficiency of DP provides an opportunity to increase sensitivity, or significantly reduce search times and help offset the effects of the continuing exponential growth in database sizes. Specifically, for nucleotide searching we have demonstrated an order of magnitude speed improvement with significantly improved sensitivity, or alternatively moderate speed up with further sensitivity gains, depending on the parameters selected. Smith-Waterman complete DP is used as the sensitivity benchmark. Similar speed and sensitivity results are presented for protein searching. Since our algorithm is highly parallel, we have developed dedicated hardware which we will present in a companion paper, and a distributed version of our software (DDASH), which we expect to provide linear speedup on a cluster.

关键词： Dynamic programming Databases bioinformatics Genomics Proteomics Clustering algorithms Heuristic algorithms Embedded system Laboratories Informatics

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A hidden Markov model for gene function prediction from sequential expression data

A hidden Markov model for gene function prediction from sequ...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Xutao Deng H. Ali College of Information Science and Technology University of Nebraska Omaha USA

Hidden Markov models (HMMs) have demonstrated great successes in modeling noisy sequential data sets in the area of speech recognition and protein sequence profiling. Results from association test showed significant Markov dependency in time-series gene expression data, and therefore HMMs would be especially appropriate for modeling gene expressions. In this project, we developed a gene function prediction tool based on profile HMMs. Each function class is associated with a distinct HMM whose parameters are trained using yeast time-series gene expression data. The function annotations of the HMM training set were obtained from Munich Information Centre for Protein Sequences (MIPS) data base. We designed several structural variants of HMMs (single, double-split) and tested each of them on forty function classes each of which includes more than one hundred instances. The highest prediction sensitivity we achieved is 51% by using double-split HMM with 3-fold cross-validation.

关键词： Hidden Markov models Gene expression Testing bioinformatics Fungi Genomics Accuracy Bayesian methods Educational institutions Information science

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A theoretical analysis of gene selection

A theoretical analysis of gene selection

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： S. Mukherjee S.J. Roberts Department of Engineering Science University of Oxford Oxford UK

A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ('gene selection'). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample-sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting relevant genes, using a given gene ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to empirical methods, the analysis can easily be used to examine the behaviour of gene selection algorithms under a wide variety of conditions, even when the numbers of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three well-known gene ranking functions.

关键词： Diseases Cells (biology) bioinformatics Data engineering Probability Sampling methods Algorithm design and analysis Data analysis Needles Testing

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Selection of putative cis-regulatory motifs through regional and global conservation

Selection of putative cis-regulatory motifs through regional...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Youlian Pan B. Smith Hung Fang F.A. Famili M. Sikorska R. Walker Institute for Information Technology National Research Council Canada Ottawa ONT Canada Institute for Biological Sciences National Research Council Canada Ottawa ONT Canada

Cis-regulatory motifs are often overrepresented in promoters and may exhibit frequency biases in subpromoter regions (SPRs). Many probabilistic algorithms have been used to predict such motifs, but they tend to generate many false positives. We devised a novel algorithm, MotifFilter, that computes representation indices (RIs) for putative motifs. MotifFilter's RI is a ratio of the actual over expected frequency of a motif in promoters, SPRs or random genomic DNA that takes into account of the nucleotide probability distributions in these regions. This approach was applied to a genome-wide survey of putative cAMP-response elements (CREs) for motifs generated by a profile hidden Markov model. Twenty of 144 putative CRE motifs found in the survey were retained by the MotifFilter.

关键词： bioinformatics Genomics Sequences Frequency Hidden Markov models DNA Probability distribution Filtering algorithms Humans Information technology

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A new hierarchical method for identification of dynamic regulatory pathways from time-series DNA microarray data

A new hierarchical method for identification of dynamic regu...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： A. Darvish E. Bak K. Gopalakrishhan R.H. Zadeh K. Najarian University of North Carolina at Charlotte USA College of Information Technology Electrical engineering Department Physics Department

A new hierarchical method is proposed to analyze timeseries DNA microarray data to identify dynamic genetic pathways. Initially the hierarchical method applies a specialized clustering technique to incorporate the available heuristic information about biological system. Then, the prototypes of the resulting clusters are used as time-variables to develop an auto regressive model to relate the expression of the prototypes to each other. The resulting model also allows the prediction of gene expressions for the next time steps. The developed AR model can then be used to relate the expression value of each single gene to the genes of other clusters. The proposed method was applied to the cell-cycle dataset containing the DNA microarray time-series of a large number of genes involved in the eukaryotic cell-cycle. The technique resulted to a network of interactions among five clusters of genes in which the genes of each cluster have a biologically-meaningful trend in time.

关键词： DNA computing bioinformatics

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Extracting characteristic patterns from genome-wide expression data by non-negative matrix factorization

Extracting characteristic patterns from genome-wide expressi...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： Nini Rao S.J. Shepherd School of Life Science and Technology University of Electronic Science and Technology Chengdu China School of Engineering Design and Technology University of Bradford Bradford UK

In this paper, we propose a novel approach, which is called as nonnegative matrix factorization (NMF), to analyze genome wide expression data. One of NMF advantages is that it can directly process these data without normalization. Firstly, we design an optimal algorithm for NMF approach. Compared with the existing NMF algorithms, our algorithm is more stable and converges very fast. We have coded the final algorithm in highly optimized C. Secondly, we describe the use of NMF in the extraction of the characteristic patterns from genome wide expression data. Thirdly, some simulation experiments are made in order to verify the efficiency of NMF algorithm, our conclusions are that NMF can be used as a powerful tool to extract the biologically-meaningful expression patterns from genomic wide expression data.

关键词： Data mining Genomics bioinformatics Signal processing algorithms Algorithm design and analysis Independent component analysis Noise reduction Gene expression Biological system modeling Biological information theory

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RepeatAssembler: a package for annotation of full-length repetitive DNA sequences in fungal genomes

RepeatAssembler: a package for annotation of full-length rep...

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proceedings of the ieee computational systems bioinformatics conference, csb

作者： M.L. Farman J.W. Jaromczyk J.W. Gilkerson C. Staben Department of Plant Pathology University of Kentucky USA Department of Computer Science University of Kentucky USA Department of Biology University of Kentucky USA

REPEATASSEMBLER aims to automate the identification of full-length consensus sequences for repetitive DNA fragments or the genetic material that is repeated many times throughout the genome (so-called repeats). Although there are packages with similar objectives, e.g. RECON, none fully meets the needs of researchers in comparative genomics. Joining short repeating elements into fall-length repeats is particularly intricate and handling this task properly is both the goal and the strength of our solution. REPEATASSEMBLER analyzes as few sequences as possible to screen out all repetitive sequences. Then it builds a set of non-redundant repeats and reports the full-length consensus sequence for each element in the set. REPEATASSEMBLER consistently delivers repetitive sequences that agree with known full-length repeats for several fungal genomes.

关键词： Packaging DNA Sequences Genomics bioinformatics Biological cells Genetics Biological materials Computer science Proteins

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