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Evaluating Robustness of Algorithm for Microsatellite Marker Genotyping

Evaluating Robustness of Algorithm for Microsatellite Marker...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： T. Matsumoto R. Nakashige Hitachi Software Engineering Company Limited Tokyo Japan

Microsatellites provide powerful genetic tools for complex disease mapping. Microsatellite genotyping requires analyzing peak data for discrimination of the true allele. In a previous study, we developed a new algorithm for automated genotyping. Here, we evaluate our algorithm’s robustness. First, we found that our algorithm calculates the model parameter of noise peaks appropriately and infers genotypes correctly even with low selectivity and specificity in the intermediate result of its first step. Our results indicate the model robustly calculates noise peaks. Second, our algorithm adequately infers true allele peaks for small sample sets. Furthermore, we evaluated its potential risk of failing to construct noise peak model.

关键词： Inference algorithms DNA Genetics Noise robustness bioinformatics Diseases Sequences Data analysis Biological system modeling Genomics

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Predicting Single Genes Related to Immune-Relevant Processes

Predicting Single Genes Related to Immune-Relevant Processes

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： C. Spieth F. Streichert N. Speer C. Sinzger K. Eberhard A. Zell Centre for Bioinformatics University of Tübingen Tubingen Germany Institute of Medical Virology University Hospital of Tübingen Tubingen Germany

In this paper we address the problem of predicting gene activities by finding gene regulatory dependencies in experimental DNA microarray data. Only few approaches to infer the dependencies of complete gene interconnectivity networks can be found in the literature. Due to the limited number of available data, the inferring problem is under-determined and ambiguous. Therefore, we introduce a new algorithm to infer relationships only between selected genes and the unknown gene network. This method is able to predict gene activation by mathematical modeling of the network and its simulation. The parameters of the mathematical model are determined by optimization with evolutionary algorithms. In this paper we will show that our approach is able to correctly predict gene responses in immune related regulatory processes and correctly identify some of the true genomic relationships of these genes.

关键词： bioinformatics Mathematical model Immune system Genomics Gene expression DNA Evolutionary computation Systems biology Biological system modeling Hospitals

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Segment and Combine Approach for Biological Sequence Classification

Segment and Combine Approach for Biological Sequence Classif...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： P. Geurts A.B. Cuesta L. Wehenkel CBIG-Center of Biomedical Integrative Genoproteomics Department of Electrical Engineering and Computer Science University of Liège Belgium

This paper presents a new algorithm based on the segment and combine paradigm, for automatic classification of biological sequences. It classifies sequences by aggregating the information about their subsequences predicted by a classifier derived by machine learning from a random sample of training subsequences. This generic approach is combined with decision tree based ensemble methods, scalable both with respect to sample size and vocabulary size. The method is applied to three families of problems: DNA sequence recognition, splice junction detection, and gene regulon prediction. With respect to standard approaches based on n-grams, it appears competitive in terms of accuracy, flexibility, and scalability. The paper also highlights the possibility to exploit the resulting models to identify interpretable patterns specific of a given class of biological sequences.

关键词： Sequences Machine learning Machine learning algorithms Decision trees DNA Biological system modeling Vocabulary Scalability bioinformatics Support vector machines

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Network Motifs, Feedback Loops and the Dynamics of Genetic Regulatory Networks

Network Motifs, Feedback Loops and the Dynamics of Genetic R...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： J.S. Hallinan P.T. Jackway ARC Centre for Bioinformatics ARC Centre for Complex Systems Institute for Molecular Bioscience and School of ITEE University of Queensland Brisbane Australia CSIRO Mathematical and Information Sciences Queensland Bioscience Precinct Saint Lucia QLD Australia

We analyse a suite of Boolean networks which have been evolved to exhibit limit cycle-type dynamics in terms of the distribution of small network motifs and feedback loops. We find that asynchronously updated Boolean networks can be evolved to exhibit fuzzy limit cycle dynamics without significant changes to the number of nodes and links in the network. Analysis of all possible triads of nodes in the networks and all feedback loops of length one to eight reveal no significant differences between the evolved and unevolved networks. We conclude that the reductionist, motif-based approach to network analysis may be inadequate to full understanding of network dynamics, and that some dynamic behaviour is an emergent property of complex networks as a whole.

关键词： Feedback loop Genetics Limit-cycles Proteins biology computing bioinformatics Australia Biological system modeling Gene expression Information analysis

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Heuristic Algorithm for Computing Reversal Distance with MultiGene Families via Binary Integer Programming

Heuristic Algorithm for Computing Reversal Distance with Mul...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： J. Suksawatchon C. Lursinsap M. Boden Advanced Virtual and Intelligent Computing (AVIC) Center Department of Mathematics Chulalongkom University Thailand School of Information Technology and Electrical Engineering University of Queensland Australia

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting of signed genomic data. Their algorithm solves the minimum number of reversals required for rearranging a genome to another when gene duplication is nonexisting. In this paper, we show how to extend the Hannenhalli-Pevzner approach to genomes with multigene families. We propose a new heuristic algorithm to compute the reversal distance between two genomes with multigene families via the concept of binary integer programming without removing gene duplicates. The experimental results on simulated and real biological data demonstrate that the proposed algorithm is able to find the reversal distance accurately.

关键词： Heuristic algorithms Linear programming Genomics bioinformatics Sorting Polynomials Phylogeny Humans Partitioning algorithms Mathematics

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Preliminary Results for GAMI: A Genetic Algorithms Approach to Motif Inference

Preliminary Results for GAMI: A Genetic Algorithms Approach ...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： C.B. Congdon C.W. Fizer N.W. Smith H.R. Gaskins J. Aman G.M. Nava C. Mattingly Department of Computer Science Colby College Waterville ME USA Institute for Genomic Biology University of Illinois Urbana-Champaign Urbana IL USA Mount Desert Island Biological Laboratory Salisbury Cove ME USA

We have developed GAMI, an approach to motif inference that uses a genetic algorithms search and is designed specifically to work with divergent species and possibly long nucleotide sequences. The system design reduces the size of the search space as compared to typical window-location approaches for motif inference. This paper describes the motivation and system design for GAMI, discusses how we have designed the search space and compares this to the search space of other approaches, and presents initial results with data from the literature and from novel tasks. GAMI is able to find a host of putative conserved patterns; possible approaches for validating the utility of the conserved regions are discussed.

关键词： Genetic algorithms Sequences Genomics bioinformatics Humans biology Inference algorithms Computer science Educational institutions Laboratories

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Robust Algorithm for Finding Weak Motifs

Robust Algorithm for Finding Weak Motifs

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Xiao Yang J.C. Rajapakse BioInformatics Research Centre School of Computer Engineering Nanyang Technological University Singapore

The Challenge Problem posed by Pevzner et al. showed that special algorithms are needed to detect weak motifs in bio-sequences, where the classical approaches, such as MEME and Gibbs Sampler, fail. Though several algorithms have since been developed to solve the weak motif recognition problem, their focus has been on exact datasets and their performances show poor tolerance to the noisy datasets, i.e., for datasets bearing sequences without any motif instances. We propose a novel approach to find weak motifs that is robust to noise in the datasets. The experiments with synthetic datasets show that our algorithm has less running time and higher accuracy in detecting weak motifs over the existing approaches and is more robust to the presense of noise. The application of the algorithm on some promoter datasets from yeast genomes found previously-proven binding sites.

关键词： Sequences bioinformatics DNA Noise robustness Genomics Fungi RNA Amino acids Biological processes Detection algorithms

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LVQ Approach Using AA Indices for Protein Subcellular Localisation Prediction

LVQ Approach Using AA Indices for Protein Subcellular Locali...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Kok Sin Toh M.N. Nguyen J.C. Rajapakse BioInformatics Research Centre School of Computer Engineering Nanyang Technological University Singapore

Knowledge of subcellular localisation of proteins is important in determining their function and involvement in different pathways. A wide variety of methods has been proposed over the recent years in order to predict the subcellular localisation of proteins, mainly based on amino acid composition or single sequence inputs. We propose a Learning Vector Quantization (LVQ) method for protein subcellular localisation prediction based on N-terminal sorting signals by using the information derived from Amino Acid (AA) index database. The LVQ approach achieved overall prediction accuracies of 84.7% for 2427 eukaryotic protein sequences on Reinhardt and Hubbard dataset and upto 86.8% on the non-plant (eukaryotes) dataset of 2738 sequences from the TargetP website, which are comparable or better than the results of existing prediction methods.

关键词： Amino acids Extracellular Protein engineering Prediction methods Neural networks bioinformatics Vector quantization Sorting Humans Electron microscopy

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bioinformatics tools for HIV-1 identification in Southern Brazilian States

Bioinformatics tools for HIV-1 identification in Southern Br...

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Brazilian symposium on bioinformatics - Advances in bioinformatics and computational biology

作者： Breda, Ardala Fernandas, Claudia Lemelle De, Almeida, Sabrina Esteves Matos Franco, Heitor Moreira Rossetti, Maria Lucia Rosa Rodrigues, Rosangela Brigido, Luis Fernando Cortez-Herrera, Elizabeth

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Depth Annotation of RNA Folds for Secondary Structure Motif Search

Depth Annotation of RNA Folds for Secondary Structure Motif ...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： D. Ashlock J. Schonfeld Mathematics and Statistics University of Guelph Guelph ONT Canada Bioinformatics and Computational Biology Iowa State University Ames IA USA

The biological activity of RNA depends on the way it folds into secondary structures. Presented here is a framework for exploratory motif searching in the space of RNA secondary structures. A collection of RNA sequences, suspected of having a particular biological activity, is fragmented into overlapping pieces of a uniform size. Each piece is folded and the details of the fold are used to annotate the primary structure. Distances between annotated structures are computed. The distance matrix for the structures is then projected into the Euclidean plane for visualization and detection of clusters. A motif is taken to be a cluster in the two dimensional space. An instance of the framework is implemented for testing on a data set containing examples of the Iron Response Element in the following manner. Folding is performed with the Mfold package. A depth-of-fold that records stems and loops onto the primary sequence is used to annotate the pieces of RNA. Dynamic programming is used to find distances between pieces of annotated primary sequence. An evolutionary algorithm is then used to find a one-to-one mapping of pieces of RNA to points in the plane that has acceptable distortion of the distances found with dynamic programming. This one-to-one mapping is a form of non-linear projection that optimizes for fidelity of projected distances to the distances derived from the Iron Response Element data set.

关键词： RNA Sequences Iron Dynamic programming Character generation Mathematics Statistics bioinformatics computational biology biology computing

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