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A computational method for assessing peptide-identification reliability in tandem mass spectrometry analysis with SEQUEST

A computational method for assessing peptide-identification ...

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ieee conference on bioinformatics

作者： J. Razumovskaya V. Olman Don Xu E. Uberbacher N. Verbermoes Ying Xu School of Genome Science and Technology University of Tennessee Knoxville TN USA Life Sciences Division Oak Ridge National Laboratory TN USA Life Sciences Division Oak Ridge National Laboratory Knoxville TN USA Oak Ridge National Laboratory Oak Ridge TN US Life Sci. Div. Oak Ridge Nat. Lab. TN USA Computer Sciences and Mathematics Division School of Genome Science and Technologyd Oak Ridge National Laboratory TN USA

High throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides using SEQUEST and then by combining the peptide hits for protein identification. Peptide identification is typically carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from the false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for "normalizing" SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.

关键词： Mass spectroscopy Peptides Proteins Throughput Humans Genomics bioinformatics Neural networks Sensitivity and specificity Filtering

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Statistical inference for well-ordered structures in nucleotide sequences

Statistical inference for well-ordered structures in nucleot...

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ieee conference on bioinformatics

作者： S.-Y. Le J.-H. Chen J.V. Maizel Laboratory of Experimental and Computational Biology NCI Center for Cancer Research National Cancer Institute NIH Frederick MD USA Advanced Biomedical Computing Center SAIC-NCI/FCRDC Frederick MD USA

Distinct, local structures are frequently correlated with functional RNA elements involved in post-transcriptional regulation of gene expression. Discovery of microRNAs (miRNAs) suggests that there are a large class of small noncoding RNAs in eukaryotic genomes. These miRNAs have the potential to form distinct fold-back stem-loop structures. The prediction of those well-ordered folding sequences (WFS) in genomic sequences is very helpful for our understanding of RNA-based gene regulation and the determination of local RNA elements with structure-dependent functions. In this study, we describe a novel method for discovering the local WFS in a nucleotide sequence by Monte Carlo simulation and RNA folding. In the approach the quality of a local WFS is assessed by the energy difference (E/sub diff/) between the optimal structure folded in the local segment and its corresponding optimal, restrained structure where all the previous base pairings formed in the optimal structure are prohibited. Distinct WFS can be discovered by scanning successive segments along a sequence for evaluating the difference between Ediff of the natural sequence and those computed from randomly shuffled sequences. Our results indicate that the statistically significant WFS detected in the genomic sequences of Caenorhabditis elegans (***) F49E12, T07C5, T07D1, T10H9, Y56A3A and Y71G12B are coincident with known fold-back stem-loops found in miRNA precursors. The potential and implications of our method in searching for miRNAs in genomes is discussed.

关键词： RNA bioinformatics Genomics Sequences Cancer Laboratories computational biology Gene expression Proteins Biomedical computing

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Reconstruction of ancestral gene order after segmental duplication and gene loss

Reconstruction of ancestral gene order after segmental dupli...

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ieee conference on bioinformatics

作者： J. Huan J. Prins W. Wang T. Vision Departments of Computer Science Department of Computer Science North Carolina State University Chapel Hill USA Department of Bioligy North Carolina State University Chapel Hill USA Departments of Biology University of North Carolina at Chapel Hill

As gene order evolves through a variety of chromosomal rearrangements, conserved segments provide important insight into evolutionary relationships and functional roles of genes. However, gene loss within otherwise conserved segments, as typically occurs following large-scale genome duplication, has received limited algorithmic study. This has been a major impediment to comparative genomics in certain taxa, such as plants and fish. We propose a heuristic algorithm/or the inference of ancestral gene order in a set of related genomes that have undergone large-scale duplication and gene loss. First, approximately conserved (i.e. homologous) segments are identified using pairwise local genome alignment. Second, homologous segments are iteratively clustered under the control of two parameters, (1) the minimal required number of shared genes between two clusters and (2) the maximal allowed number of rearrangement breakpoints along the lineage leading to each descendant segment. Finally, we compute an estimated ancestral gene order for each cluster that is optimal in some sense. We evaluate the performance of this algorithm on simulated data that models a genome evolving by large-scale duplication, duplicate gene loss, transposition, translocation, and inversion. The results suggest that long segments of ancestral gene order may be reconstructed following moderate levels of rearrangement with only minor loss of accuracy.

关键词： Genomics bioinformatics Large-scale systems Inference algorithms Clustering algorithms Biological cells Impedance Marine animals Heuristic algorithms Iterative algorithms

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3D structural homology detection via unassigned residual dipolar couplings

3D structural homology detection via unassigned residual dip...

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ieee conference on bioinformatics

作者： C.J. Langmead B.R. Donald Dartmouth Computer Science Department Hanover NH USA

Recognition of a protein's fold provides valuable information about its function. While many sequence-based homology prediction methods exist, an important challenge remains: two highly dissimilar sequences can have similar folds - how can we detect this rapidly, in the context of structural genomics? High-throughput NMR experiments, coupled with novel algorithms for data analysis, can address this challenge. We report an automated procedure for detecting 3D structural homologies from sparse, unassigned protein NMR data. Our method identifies the 3D structural models in a protein structural database whose geometries best fit the Unassigned experimental NMR data. It does not use sequence information and is thus not limited by sequence homology. The method can also be used to confirm or refute structural predictions made by other techniques such as protein threading or sequence homology. The algorithm runs in O(pnk/sup 3/) time, where p is the number of proteins in the database, n is the number of residues in the target protein, and k is the resolution of a rotation search. The method requires only uniform /sup 15/N-labelling of the protein and processes unassigned /sup H/N-/sup 15/N residual dipolar couplings, which can be acquired in a couple of hours. Our experiments on NMR data from 5 different proteins demonstrate that the method identifies closely related protein folds, despite low-sequence homology between the target protein and the computed model.

关键词： Proteins Sequences Nuclear magnetic resonance Amino acids Spine bioinformatics Bonding Genomics Biology computing Computer science

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Representing and reasoning about signal networks: an illustration using NF/spl kappa/B dependent signaling pathways

Representing and reasoning about signal networks: an illustr...

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ieee conference on bioinformatics

作者： C. Baral K. Chancellor N. Tran Dept. of Computer Sc. and Engg. Arizona State University Tempe AZ Translational Genomics Research Institute Phoenix AZ

We propose a formal language to represent and reason about signal transduction networks. The existing approaches such as ones based on Petri nets, and /spl pi/-calculus fall short in many ways and our work suggests that an artificial intelligence (AI) based approach may be well suited for many aspects. We apply a form of action language to represent and reason about NF/spl kappa/B dependent signaling pathways. Our language supports several essential features of reasoning with signal transduction knowledge, such as: reasoning with partial (or incomplete) knowledge, and reasoning about triggered evolutions of the world and elaboration tolerance. Because of its growing important role in cellular functions, we select NF/spl kappa/B dependent signaling to be our test bed. NF/spl kappa/B is a central mediator of the immune response, and it can regulate stress responses, as well as cell death/survival in several cell types. While many extracellular signals may lead to the activation of NF/spl kappa/B, few related pathways are elucidated. We study the tasks of representation of pathways, reasoning with pathways, explaining observations, and planning to alter the outcomes; and show that all of them can be well formulated in our framework. Thus our work shows that our AI based approach is a good candidate for feasible and practical representation of and reasoning about signal networks.

关键词： Noise measurement Artificial intelligence bioinformatics Petri nets Extracellular Electric breakdown Knowledge representation Computer networks Genomics Formal languages

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Gene selection for multiclass prediction of microarray data

Gene selection for multiclass prediction of microarray data

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ieee conference on bioinformatics

作者： D. Chen D. Hua J. Reifman X. Cheng Uniformed Services University of Health Sciences USA George Washington University USA US army medical reasearch and materiel command USA

Gene expression data from microarrays have been successfully applied to class prediction, where the purpose is to classify and predict the diagnostic category of a sample by its gene expression profile. A typical microarray dataset consists of expression levels for a large number of genes on a relatively small number of samples. As a consequence, one basic and important question associated with class prediction is: how do we identify a small subset of informative genes contributing the most to the classification task? Many methods have been proposed but most focus on two-class problems, such as discrimination between normal and disease samples. This paper addresses selecting informative genes for multiclass prediction problems by jointly considering all the classes simultaneously. Our approach is based on the power of the genes in discriminating among the different classes (e.g., tumor types) and the existing correlation between genes. We formulate the expression levels of a given gene by a one-way analysis of variance model with heterogeneity of variances, and determine the discriminatory power of the gene by a test statistic designed to test the equality of the class means. In other words, the discriminatory power of a gene is associated with a Behrens-Fisher problem. Informative genes are chosen such that each selected gene has a high discriminatory power and the correlation between any pair of selected genes is low. Test statistics considered in this paper include the ANOVA F test statistic, the Brown-Forsythe test statistic, the Cochran test statistic, and the Welch test statistic. Their performances are evaluated over several classification methods applied to two publicly available microarray datasets. The results show that Brown-Forsythe test statistic achieves the best performance.

关键词： bioinformatics

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Anticlustal: multiple sequence alignment by antipole clustering and linear approximate 1-median computation

Anticlustal: multiple sequence alignment by antipole cluster...

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ieee conference on bioinformatics

作者： C. Di Pietro V. Di Pietro G. Emmanuele A. Ferro T. Maugeri E. Modica G. Pigola A. Pulvirenti M. Purrello M. Ragusa M. Scalia D. Shasha S. Travali V. Zimmitti Dipartimento di Scienze Biomediche Università di Catania Dipartimento di Scienze Biomediche Università di Catania Italy Dipartimento di Matematica e Informatica Università di Catania Italy Dipartimento di Matematica e Informatica Università di Catania Courant Institute of Mathematical Science New York University USA

In this paper we present a new multiple sequence alignment (MSA) algorithm called AntiClustAl. The method makes use of the commonly used idea of aligning homologous sequences belonging to classes generated by some clustering algorithm, and then continue the alignment process in a bottom-up way along a suitable tree structure. The final result is then read at the root of the tree. Multiple sequence alignment in each cluster makes use of the progressive alignment with the 1-median (center) of the cluster. The 1-median of set S of sequences is the element of S which minimizes the average distance from any other sequence in S. Its exact computation requires quadratic time. The basic idea of our proposed algorithm is to make use of a simple and natural algorithmic technique based on randomised tournaments which has been successfully applied to large size search problems in general metric spaces. In particular a clustering algorithm called antipole tree and an approximate linear 1-median computation are used. Our algorithm compared with Clustal W, a widely used tool to MSA, shows a better running time results with fully comparable alignment quality. A successful biological application showing high amino acid conservation during evolution of Xenopus laevis SOD2 is also cited.

关键词： Linear approximation bioinformatics

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Efficient reconstruction of phylogenetic networks with constrained recombination

Efficient reconstruction of phylogenetic networks with const...

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ieee conference on bioinformatics

作者： D. Gusfield Satish Eddhu C. Langley Department of Computer Science University of California Davis USA Division of Evolution and Ecology University of California Davis USA

A phylogenetic network is a generalization of a phylogenetic tree, allowing structural properties that are not treelike. With the growth of genomic data, much of which does not fit ideal tree models, there is greater need to understand the algorithmics and combinatorics of phylogenetic networks. We consider the problem of determining whether the sequences can be derived on a phylogenetic network where the recombination cycles are node disjoint. In this paper, we call such a phylogenetic network a "galled-tree". By more deeply analysing the combinatorial constraints on cycle-disjoint phylogenetic networks, we obtain an efficient algorithm that is guaranteed to be both a necessary and sufficient test for the existence of a galled-tree for the data. If there is a galled-tree, the algorithm constructs one and obtains an implicit representation of all the galled trees for the data, and can create these in linear time for each one. We also note two additional results related to galled trees: first, any set of sequences that can be derived on a galled tree can be derived on a true tree (without recombination cycles), where at most one back mutation is allowed per site; second, the site compatibility problem (which is NP-hard in general) can be solved in linear time for any set of sequences that can be derived on a galled tree. The combinatorial constraints we develop apply (for the most part) to node-disjoint cycles in any phylogenetic network (not just galled-trees), and can be used for example to prove that a given site cannot be on a node-disjoint cycle in any phylogenetic network. Perhaps more important than the specific results about galled-trees, we introduce an approach that can be used to study recombination in phylogenetic networks that go beyond galled-trees.

关键词： Phylogeny bioinformatics Genomics Genetic mutations Combinatorial mathematics Computer science Environmental factors Biological system modeling Algorithm design and analysis Testing

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Gene function, metabolic pathways and comparative genomics in yeast

Gene function, metabolic pathways and comparative genomics i...

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ieee conference on bioinformatics

作者： Q. Dong Rama Balakrishnan G. Binkley K.R. Christie M. Costanzo K. Dolinski S.S. Dwight S. Engel D.G. Fisk J. Hirschman E.L. Hong R. Nash L. Issel-Tarver Anand Sethuraman C.L. Theesfeld S. Weng D. Botstein J.M. Cherry Saccharomyces Genome Database Department of Genetics Stanford University School of Medicine Stanford CA USA

The budding yeast, Saccharomyces cerevisiae, has been experimentally manipulated for several decades. Much of the information generated is available in the Saccharomyces genome database (SGD, http://***/). SGD contains large datasets of both genomic and proteomic information, as well as tools for data analysis. This paper will highlight three datasets that are maintained by SGD. First, a large dataset of hand-curated information is provided in machine readable format for each gene of the Saccharomyces genome. These hand-curated annotations use the gene ontology (GO) controlled vocabularies for biological process, molecular function and cellular component and each contains categorical evidence codes and literature references. A second area of focus is on metabolic pathways. A new dataset of hand-curated information on metabolic pathways within budding yeast was released in May 2003. This resource can be searched to view biochemical reactions and pathways and their component gene products. This resource also maps data from genome-wide expression analyses onto the pathway overview providing a visualization of the changes in gene expression in the context of cellular metabolism. These pathways are created and edited using the pathway tools software but the content is reviewed and updated by SGD. A third dataset has recently become available as the result of two comparative genomic analyses. Two groups sequenced the genomes of several yeasts closely related to S. cerevisiae, and then completed a gene-by-gene comparison of these genomes. These genome comparisons were combined with available experimental evidence by SGD. Using these data the annotations for the *** reference genome were improved. All these datasets are freely available from the SGD ftp site.

关键词： Genomics bioinformatics Fungi Databases Proteomics Data analysis Ontologies Biological control systems Vocabulary Biological processes

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Reconstruction of ancient operons from complete microbial genome sequences

Reconstruction of ancient operons from complete microbial ge...

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ieee conference on bioinformatics

作者： Yuhong Wang J.P. Rose B.-C. Wang D. Lin Department of Molecular Biology Jilin University Changchun China Southeast Colloboratory Biochemistry and Molecular Biology Department University of Georgia Athens GA USA

Completed genomes not only provide DNA sequence information, but also reveal the relative locations of genes. In this paper, we propose a new method for reconstruction of "ancient operons" by taking advantages of the evolutionary information in both orthologous genes and their locations in a genome. The basic assumption is that the closer two genes were in an ancient genome, the more likely they will stay close in the current genome. An assembly of nonrandom neighboring pairs of genes in current genomes should be able to reconstruct the gene groups that were together at a certain point of time during evolution. Given the fact that genes that are close neighbors are more likely functionally related, the gene groups generated by this assembly process are named "ancient operons". The assembly is only meaningful when enough nonrandom pairs can be found. This was made possible by over 100 microbial genomes available in recent years. For proof of concept, we chose 63 nonredundant complete microbial genomes from RefSeq database [May 2003 release} at NCBI. In order to normalize the effect of protein sequence mutations and other changes due to evolution, we only consider assembly of COGs (cluster of orthologous group) in these genomes. There are total 4901 COGs from NCBI COG database are used. The assembly process is similar to the one that assembles DNA sequences into contigs. In our case, the neighbor COG pairs are used as basic assembly units. A target Junction is defined based on neighbor frequency of pair-wise link among all 4901 COGs after analysis for all 63 genomes. We used random cost algorithm, a global optimization algorithm to minimize the target function and assembled COGs into contigs. The significance of these contigs are then assessed by statistical methods. The results suggest that the assembled contigs are statistically and biologically significant. This method and the assembled ancient operons provides a new way for studying microbial genomes, their evolut

关键词： Genomics bioinformatics Assembly Evolution (biology) DNA Databases Protein sequence Genetic mutations Frequency Cost function

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