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检索条件"任意字段=2005 IEEE Computational Systems Bioinformatics Conference, CSB 2005"

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A cepstral distortion measure for protein comparison and identification

A cepstral distortion measure for protein comparison and ide...

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International conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Pham, Tuan D. Shim, Byung-Sub Bioinformatics Applications Research Center James Cook University Townsville QLD 4811 Australia School of Information Technology James Cook University Townsville QLD 4811 Australia

ISBN: (纸本)078039092X

Protein sequence comparison is the most powerful tool for the identification of novel protein structure and function. This type of inference is commonly based on the similar sequence-similar structure-similar function paradigm, and derived by sequence similarity searching on databases of protein sequences. As entire genomes have been being determined at a rapid rate, computational methods for comparing protein sequences will be more essential for probing the complexity of molecular machines. In this paper we introduce a pattern-comparison algorithm, which is based on the mathematical concept of linear-predictive-coding based cepstral distortion measure, for comparison and identification of protein sequences. Experimental results on a real data set of functionally related and functionally non-related protein sequences have shown the effectiveness of the proposed approach on both accuracy and computational efficiency. © 2005 ieee.

关键词： Proteins

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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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The synthetic gene designer: a flexible Web platform to explore sequence space of synthetic genes for heterologous expression

The synthetic gene designer: a flexible Web platform to expl...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： G. Wu N. Bashir-Bello S. Freeland Department of Biological Sciences University of Maryland Baltimore MD USA

"Codon optimization" is a general approach that seeks to improve heterologous expression when a gene is moved into a foreign genome that exhibits different codon usage from its native genome. However, it is still unclear exactly what it means to optimize codons for protein expression: diverse interpretations exist, and exploratory research continues. Thus, to facilitate flexible redesign of genes for heterologous expression, we have developed a bona fide web application: the "synthetic gene designer". Compared with existing codon optimization software, the synthetic gene designer provides greater flexibility in several key features of codon optimization strategy. The software, together with online tutorials and help, is available at http://***. net/codon/***.

关键词： Space exploration Sequences bioinformatics Application software Design optimization Genomics Genetics Organisms Proteins Biology

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Sequential classification for microarray and clinical data

Sequential classification for microarray and clinical data

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： G. Tusch Grand Rapids Medical Education and Research Center for Health Professions (MERC) Grand Rapids MI USA Grand Valley State University Allendale MI USA

Sequential classification uses in a stepwise process only part of the data (evidence) for partial classification, i.e., classifying only objects with sufficient evidence and leaving the rest unclassified. In the following steps the procedure is repeated using additional data until all objects are classified. This is especially useful when data become available only at certain points in time, as in surgical decision making, i.e., clinical patient data, lab data, or cDNA microarray expression data from tissue samples become available before, during and after the operation. Surgeons are interested in classifying patients into low or high risk groups, which might need special measures, e.g., prolonged intensive care.

关键词： Surgery Error analysis Decision making Neoplasms Costs Statistical analysis Clinical trials Neurons Artificial neural networks bioinformatics

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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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A robust meta-classification strategy for cancer diagnosis from gene expression data

A robust meta-classification strategy for cancer diagnosis f...

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Proceedings of the ieee computational systems bioinformatics conference, csb

作者： G. Alexe G. Bhanot B. Venkataraghavan R. Ramaswamy J. Lepre A.J. Levine G. Stolovitzky IBM Computational Biology Center IBM Thomas J. Watson Research Center Yorktown Heights NY USA IBM T.J. Watson Research IBM Computational Biology Center Yorktown Heights NY USA IBM Computational Biology Center Institute for Advanced Study Einstein Drive Princeton NJ USA Center for Systems Biology Institute for Advanced Study Einstein Drive Princeton NJ USA Center for Systems Biology Institute of Advanced Study in Science and Technology Princeton NJ USA Center for Systems Biology Cancer Institute of New Jersey Einstein Drive Princeton New Brunswick USA

One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose a meta-classification scheme which uses a robust multivariate gene selection procedure and integrates the results of several machine learning tools trained on raw and pattern data. We validate our method by applying it to distinguish diffuse large B-cell lymphoma (DLBCL) from follicular lymphoma (FL) on two independent datasets: the HuGeneFL Affmetrixy dataset of Shipp et al. (www. genome. wi. mit. du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our meta-classification technique achieves higher predictive accuracies than each of the individual classifiers trained on the same dataset and is robust against various data perturbations. We also find that combinations of p53 responsive genes (e.g., p53, PLK1 and CDK2) are highly predictive of the phenotype.

关键词： Cancer Robustness Gene expression USA Councils computational biology systems biology Biological system modeling Laboratories bioinformatics Data mining

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Operon prediction in cyanobacteria using comparative genomics

Operon prediction in cyanobacteria using comparative genomic...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： N. Khuri N.S. Rao Carnegie Institution Wa Dpb Stanford CA USA College of Engineering San Jose State University Foundation San Jose CA USA

In this work, we performed a comprehensive analysis of the genome organization of eight publicly available cyanobacterial genomes and of the extensively studied Escherichia coli K12. Our comparison revealed striking similarities in organization of different microbial genomes. We examined the orientation of putative CDS, and distance distribution patterns between adjacent putative CDS in the whole genome as well as between genes within operons and across operon borders. Our results enabled us to calculate distance log-likeihoods for gene pairs to be part of the same operon and to apply this metric to predict operons in two newly sequenced cyanobacterial genomes, Synechococcus OS-A and OS-B'.

关键词： Genomics bioinformatics Educational institutions Performance analysis Proteins Prediction methods Organisms Predictive models DNA conferences

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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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ssahaSNP - a polymorphism detection tool on a whole genome scale

ssahaSNP - a polymorphism detection tool on a whole genome s...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： Z. Ning M. Caccamo J.C. Mullikin Wellcome Trust Genome Campus Cambridge UK Genome Technology Branch/NHGRI Bethesda MD USA

We present a software package which can detect homozygous SNPs and indels on a eukaryotic genome scale from millions of shotgun reads. Matching seeds of a few kmer words are found to locate the position of the read on the genome. Full sequence alignment is performed to detect base variations. Quality values of both variation bases and neighbouring bases are checked to exclude possible sequence base errors. To analyze polymorphism level in the genome, we used the package to detect indels from 20 million WGS reads against the draft WGS assembly. From the dataset, we detected a total number of 663,660 indels, giving an estimated average indel density at about one indel every 2.48 kilobases. Distribution of indels length and variation of indel mapped times are also analyzed.

关键词： Genomics bioinformatics Packaging Assembly Software packages Error analysis Cancer detection Diseases Databases Data structures

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Visualization of logic analysis of phylogenetic profiles (LAPP)

Visualization of logic analysis of phylogenetic profiles (LA...

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ieee computational systems bioinformatics conference, csb Workshops and Poster Abstracts

作者： K.A. Robbins L. Zhao Department of Computer Science Cajal Neuroscience Research Institute University of Technology San Antonio USA Department of Computer Science University of Technology San Antonio USA

Recently Bowers et al analyzed triplet logic relationships among 4873 clusters of orthologous groups (COGS) from 67 fully sequenced organisms by calculating how well logic relationships between proteins a and b predicted the presence or absence of protein c (the uncertainty). The log of the normalized uncertainty distribution follows an approximately linear relationship for uncertainties in the interval [0.1, 0.9]. Using fitted parameters of this relationship as a characterization, we develop four types of visual analysis for LAPP data: distributions of uncertainty over logical relation type, distributions of uncertainty over functional categories, relationships of uncertainty of the overall population to known network relationships of a particular organism, and relationships of uncertainty distributions to groups obtained by standard clustering techniques. The purpose of this study is two-fold: to better understand the implications of uncertainty predictions for automatic protein network generation and to create new visualization tools for looking at this type of data.

关键词： Visualization Logic Phylogeny Uncertainty Proteins bioinformatics Organisms Genomics Computer science Databases

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