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Enabling graph appliance for genome assembly

Enabling graph appliance for genome assembly

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ieee International conference on Big Data

作者： Rina Singh Jeffrey A. Graves Sangkeun Lee Sreenivas R. Sukumar Mallikarjun Shankar Knowledge Discovery Lab Tennessee Technological University TN USA Computational Sciences and Engineering Division Oak Ridge National Laboratory TN USA

In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers that have developed use de Bruijn graph techniques. A de Bruijn graph represents a collection of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multithreaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray's Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing de Bruin graphs as RDF graphs and propose an iterative querying approach for searching cycles to find Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.

关键词： bioinformatics Genomics Resource description framework Algorithm design and analysis Sequential analysis Big data computational complexity Error analysis

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High performance computing of gene regulatory networks using a message-passing model

High performance computing of gene regulatory networks using...

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ieee conference on High Performance Extreme Computing (HPEC)

作者： Kimberly Glass John Quackenbush Jeremy Kepner Brigham and Women's Hospital and Harvard Medical School Boston MA USA Dana-Farber Cancer Institute and Harvard School of Public Health Boston MA USA Lincoln Laboratory and Massachusetts Institute of Technology Cambridge MA USA

Gene regulatory network reconstruction is a fundamental problem in computational biology. We recently developed an algorithm, called PANDA (Passing Attributes Between Networks for Data Assimilation), that integrates multiple sources of omics data and estimates regulatory network models. This approach was initially implemented in the C++ programming language and has since been applied to a number of biological systems. In our current research we are beginning to expand the algorithm to incorporate larger and most diverse data-sets, to reconstruct networks that contain increasing numbers of elements, and to build not only single network models, but sets of networks. In order to accomplish these “Big Data” applications, it has become critical that we increase the computational efficiency of the PANDA implementation. In this paper we show how to recast PANDA's similarity equations as matrix operations. This allows us to implement a highly readable version of the algorithm using the MATLAB/Octave programming language. We find that the resulting M-code much shorter (103 compared to 1128 lines) and more easily modifiable for potential future applications. The new implementation also runs significantly faster, with increasing efficiency as the network models increase in size. Tests comparing the C-code and M-code versions of PANDA demonstrate that this speed-up is on the order of 20-80 times faster for networks of similar dimensions to those we find in current biological applications.

关键词： MATLAB Mathematical model Biological system modeling Computer languages Genomics bioinformatics

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Information Theory Based Genome-Scale Gene Networks Construction Using MapReduce

Information Theory Based Genome-Scale Gene Networks Construc...

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International conference on High Performance Computing

作者： Sriram P. Chockalingam Maneesha Aluru Srinivas Aluru Dept. of Computer Science and Engineering Indian Institute of Technology Mumbai India School of Biology Georgia Institute of Technology Atlanta USA School of Computational Science and Engineering Georgia Institute of Technology Atlanta USA

ISBN: (纸本)9781467384872

Reverse-engineering genome-scale gene networks from gene expression data is a principal challenge in systems biology. Mutual information (MI) based methods are favored because of their ability to recover non-linear relationships, low algorithmic complexity, and their successful use in various biological applications such as gene function prediction. In this paper, we present the first ever construction of MI based genome-scale gene networks using MapReduce. We develop the solution for all the stages of a MI-based network construction algorithm using only the map and reduce operations on distributed datasets. Our solution is implemented using Spark, a software that provides a compute and memory abstraction for distributed datasets. We deploy our solution using on-demand virtual instances on Amazon EC2 cloud computing platform, thus demonstrating the use of a rent-by-the-hour ad-hoc cluster for this grand challenge problem in systems biology. Our implementation can scale with the number of virtual instances, and can be used to construct networks of sizes in the range of 2000 to 5000 genes within an hour in a cost effective manner. We demonstrate the capability to construct genome-scale networks by reverse engineering a network of over 17,000 genes for the widely studied model plant Arabidopsis Thaliana.

关键词： Correlation Genomics bioinformatics Bayes methods Gene expression Complexity theory Random variables

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The Tensor Product model transformation as the link connecting Biological and Cognitive systems with Control Theory 5

The Tensor Product model transformation as the link connecti...

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5th ieee International conference on Cognitive Infocommunications (CogInfoCom)

作者： Szoellosi, Alexandra Baranyi, Peter Hungarian Acad Sci Comp & Automat Res Inst Internet Based Control & Commun Lab 3D H-1051 Budapest Hungary

ISBN: (纸本)9781479972807

The paper investigates and discusses the fact that there exists a significant difference and gap between the conventional mathematical modeling and computational techniques of control theory and the mathematical modeling and computational techniques beneficial of modeling cognitive processes or generally overall biological processes. This difference and gap represents a significant obstacle in designing control systems and theorems for cognitive or overall biological processes and systems. In this context the paper discusses and concludes pointing out the fact that the Tensor Product (TP) model transformation is a possible effective link and connector between the above mentioned two different mathematical modeling concepts.

关键词： Tensor Product model transformation Control Theory Biological systems Cognitive systems bioinformatics Intelligent Control Coginformatics

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Comparison and Evaluation of Signal Representations for a Carbon Nanotube computational Device

Comparison and Evaluation of Signal Representations for a Ca...

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ieee International conference on Evolvable systems (ICES)

作者： Lykkebo, Odd Rune Tufte, Gunnar Norwegian Univ Sci & Technol Dept Comp & Informat Sci N-7491 Trondheim Norway

ISBN: (纸本)9781479944798

Evolution in Materio (ElM) exploits properties of physical systems for computation. Evolution manipulates physical processes by stimulating materials by applying some sort of configuration signal. For materials such as liquid crystal and carbon nanotubes the properties of configuration data is rather open. In this work we investigate what kind of configuration data that most likely will be favourable for a carbon nanotube based device. An experimental approach targeting graph colouring is used to test three different types of signal representation: static voltages, square waves and a mixed signal representation. The results show that all signal representation was capable of producing a working device. In the experiments square wave representation produced the best result.

关键词： carbon nanotubes graph colouring liquid crystal devices mixed analogue-digital integrated circuits C EIM carbon nanotube computational device configuration data configuration signal evolution in materio liquid crystal material stimulation mixed signal representation physical computation systems physical process signal representations square wave representation static voltages bioinformatics Genomics colored graph signal representation Mixed analog digital integrated circuits Carbon Nanotubes enterprise instant messaging Epilepsy, Myoclonic, Infantile Physical Processes LIQUID CRYSTAL DISPLAY Liquid Crystals bioinformatics Genomics

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A Literature Search Tool for Identifying Disease-associated Genes using Hidden Markov Model 1

A Literature Search Tool for Identifying Disease-associated ...

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First International conference on computational systems and Communications (ICCSC)

作者： Sreekala, S. Nazeer, K. A. Abdul Natl Inst Technol Dept Comp Sci & Engn Calicut Kerala India

ISBN: (纸本)9781479960132

Understanding the role of genetics is very important for the in-depth study of a disease. Even though lots of information about gene-disease association is available, it is difficult even for an expert user to manually extract it from the huge volume of literature. Therefore, this work introduces a novel extraction tool that can identify disease associated genes from the literature using text-mining algorithm. Here, Hidden Markov Model is combined with a rule-based Named Entity Recognition approach to identify gene symbols from the literature. This will predict the good candidate genes for the disease which will help in the further analysis of the disease.

关键词： bioinformatics data mining diseases genetics hidden Markov models search problems text analysis disease-associated gene identification extraction tool gene-disease association hidden Markov model literature search tool rule-based named entity recognition approach text-mining algorithm Abstracts bioinformatics Diseases Genetics Hidden Markov models Navigation Probability

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T-HMM: A Novel Biomedical Text Classifier Based on Hidden Markov Models 8

T-HMM: A Novel Biomedical Text Classifier Based on Hidden Ma...

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8th International conference on Practical Applications of computational Biology & bioinformatics (PACBB)

作者： Seara Vieira, A. Iglesias, E. L. Borrajo, L. Univ Vigo Escola Super Enxeneria Informat Dept Comp Sci Orense Spain

ISBN: (纸本)9783319075815;9783319075808

In this paper, we propose an original model for the classification of biomedical texts stored in large document corpora. The model classifies scientific documents according to their content using information retrieval techniques and Hidden Markov Models. To demonstrate the efficiency of the model, we present a set of experiments which have been performed on OHSUMED biomedical corpus, a subset of the MEDLINE database, and the Allele and GO TREC corpora. Our classifier is also compared with Naive Bayes, k-NN and SVM techniques. Experiments illustrate the effectiveness of the proposed approach. Results show that the model is comparable to the SVM technique in the classification of biomedical texts.

关键词： Hidden Markov Model Text classification bioinformatics

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BEW: bioinformatics Workbench for Analysis of Biofilms Experimental Data 8

BEW: Bioinformatics Workbench for Analysis of Biofilms Exper...

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8th International conference on Practical Applications of computational Biology & bioinformatics (PACBB)

作者： Perez Rodriguez, Gael Glez-Pena, Daniel Azevedo, Nuno F. Pereira, Maria Olivia Fdez-Riverola, Florentino Lourenco, Analia ESEI Escuela Super Ingn Informat Orense 32004 Spain Univ Porto Fac Engn LEPABE Dept Chem Engn P-4200465 Oporto Portugal Univ Minho Ctr Biol Engn IBB P-4710057 Braga Portugal

ISBN: (纸本)9783319075815;9783319075808

Biofilms research has evolved considerably in the last decade and is now generating large volumes of heterogeneous data. MIABiE, the international initiative on Biofilms, is devising guidelines for data interchange, and some databases provide access to biofilms experiments. However, the field is lacking appropriate bioinformatics tools in support of increasing operational and analytical needs. This paper presents a flexible and extensible open-source workbench for the operation and analysis of biofilms experiments, as follows: (i) the creation of customised experiments, (ii) the collection of various analytical results, following community standardisation guidelines and (iii) ondemand reporting and statistical evaluation.

关键词： Biofilms standard operating procedures data interchange data analysis

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BioClass: A Tool for Biomedical Text Classification 8

BioClass: A Tool for Biomedical Text Classification

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8th International conference on Practical Applications of computational Biology & bioinformatics (PACBB)

作者： Romero, R. Seara Vieira, A. Iglesias, E. L. Borrajo, L. Univ Vigo Dept Comp Sci Escola Super Enxeneria Informat Orense Spain

ISBN: (纸本)9783319075815;9783319075808

Traditional search engines are not efficient enough to extract useful information from scientific text databases. Therefore, it is necessary to develop advanced information retrieval software tools that allow for further classification of the scientific texts. The aim of this work is to present BioClass, a freely available graphic tool for biomedical text classification. With BioClass an user can parameterize, train and test different text classifiers to determine which technique performs better according to the document corpus. The framework includes data balancing and attribute reduction techniques to prepare the input data and improve the classification efficiency. Classification methods analyze documents by content and differentiate those that are best suited to the user requeriments. BioClass also offers graphical interfaces to get conclusions simply and easily.

关键词： Biomedical text mining tool Text classification bioinformatics Computer-based software development

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Beyond Databases, Architectures, and Structures: 10th International conference, BDAS 2014, Ustron, Poland, May 27-30, 2014. Proceedings 1

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丛书名： Communications in Computer and Information Science 424

2014年

作者： Stanislaw Kozielski, Dariusz Mrozek, Pawel Kasprowski, Bożena Małysiak-Mrozek, Daniel Kostrzewa (eds.)

ISBN: (数字)9783319069326

ISBN: (纸本)9783319069319;9783319069326

This book constitutes the refereed proceedings of the 10th ieee International conference Beyond Databases, Architectures, and Structures, BDAS 2014, held in Ustron, Poland, in May 2014. This book consists of 56 carefully revised selected papers that are assigned to 11 thematic groups: query languages, transactions and query optimization; data warehousing and big data; ontologies and semantic web; computational intelligence and data mining; collective intelligence, scheduling, and parallel processing; bioinformatics and biological data analysis; image analysis and multimedia mining; security of database systems; spatial data analysis; applications of database systems; Web and XML in database systems.

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