Bioinformatics Jobs Computational Biology Genomics http://bioinformaticsdirectory.com Bioinformatics Jobs Computational Biology Genomics Wed, 19 Oct 2011 21:50:34 +0000 en hourly 1 http://wordpress.org/?v=3.0.1 PRIMe: a method for characterization and evaluation of pleiotropic regions from multiple genome-wide association studies http://bioinformaticsdirectory.com/5306/prime-a-method-for-characterization-and-evaluation-of-pleiotropic-regions-from-multiple-genome-wide-association-studies/ http://bioinformaticsdirectory.com/5306/prime-a-method-for-characterization-and-evaluation-of-pleiotropic-regions-from-multiple-genome-wide-association-studies/#comments Mon, 02 May 2011 00:12:41 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5306/prime-a-method-for-characterization-and-evaluation-of-pleiotropic-regions-from-multiple-genome-wide-association-studies/ Motivation: The concept of pleiotropy was proposed a century ago, though up to now there have been insufficient efforts to design robust statistics and software aimed at visualizing and evaluating pleiotropy at a regional level. The Pleiotropic Region Identification Method (PRIMe) was developed to evaluate potentially pleiotropic loci based upon data from multiple genome-wide association studies (GWAS).

Methods: We first provide a software tool to systematically identify and characterize genomic regions where low association P-values are observed with multiple traits. We use the term Pleiotropy Index to denote the number of traits with low association P-values at a particular genomic region. For GWAS assumed to be uncorrelated, we adopted the binomial distribution to approximate the statistical significance of the Pleiotropy Index. For GWAS conducted on traits with known correlation coefficients, simulations are performed to derive the statistical distribution of the Pleiotropy Index under the null hypothesis of no genotype–phenotype association. For six hematologic and three blood pressure traits where full GWAS results were available from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, we estimated the trait correlations and applied the simulation approach to examine genomic regions with statistical evidence of pleiotropy. We then applied the approximation approach to explore GWAS summarized in the National Human Genome Research Institute (NHGRI) GWAS Catalog.

Results: By simulation, we identified pleiotropic regions including SH2B3 and BRAP (12q24.12) for hematologic and blood pressure traits. By approximation, we confirmed the genome-wide significant pleiotropy of these two regions based on the GWAS Catalog data, together with an exploration on other regions which highlights the FTO, GCKR and ABO regions.

Availability and Implementation: The Perl and R scripts are available at http://www.framinghamheartstudy.org/research/gwas_pleiotropictool.html.


Supplementary information: Supplementary data are available at Bioinformatics online.

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]]> http://bioinformaticsdirectory.com/5306/prime-a-method-for-characterization-and-evaluation-of-pleiotropic-regions-from-multiple-genome-wide-association-studies/feed/ 0 aCGH.Spline–an R package for aCGH dye bias normalization http://bioinformaticsdirectory.com/5305/acgh-spline-an-r-package-for-acgh-dye-bias-normalization/ http://bioinformaticsdirectory.com/5305/acgh-spline-an-r-package-for-acgh-dye-bias-normalization/#comments Mon, 02 May 2011 00:12:39 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5305/acgh-spline-an-r-package-for-acgh-dye-bias-normalization/ Motivation: The careful normalization of array-based comparative genomic hybridization (aCGH) data is of critical importance for the accurate detection of copy number changes. The difference in labelling affinity between the two fluorophores used in aCGH—usually Cy5 and Cy3—can be observed as a bias within the intensity distributions. If left unchecked, this bias is likely to skew data interpretation during downstream analysis and lead to an increased number of false discoveries.

Results: In this study, we have developed aCGH.Spline, a natural cubic spline interpolation method followed by linear interpolation of outlier values, which is able to remove a large portion of the dye bias from large aCGH datasets in a quick and efficient manner.

Conclusions: We have shown that removing this bias and reducing the experimental noise has a strong positive impact on the ability to detect accurately both copy number variation (CNV) and copy number alterations (CNA).

Contact: l.larcombe@cranfield.ac.uk; tf2@sanger.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics – recent issues

]]> http://bioinformaticsdirectory.com/5305/acgh-spline-an-r-package-for-acgh-dye-bias-normalization/feed/ 0 Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software http://bioinformaticsdirectory.com/5303/improved-structure-function-and-compatibility-for-cellprofiler-modular-high-throughput-image-analysis-software/ http://bioinformaticsdirectory.com/5303/improved-structure-function-and-compatibility-for-cellprofiler-modular-high-throughput-image-analysis-software/#comments Thu, 14 Apr 2011 14:15:27 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5303/improved-structure-function-and-compatibility-for-cellprofiler-modular-high-throughput-image-analysis-software/ Summary: There is a strong and growing need in the biology research community for accurate, automated image analysis. Here, we describe CellProfiler 2.0, which has been engineered to meet the needs of its growing user base. It is more robust and user friendly, with new algorithms and features to facilitate high-throughput work. ImageJ plugins can now be run within a CellProfiler pipeline.

Availability and Implementation: CellProfiler 2.0 is free and open source, available at http://www.cellprofiler.org under the GPL v. 2 license. It is available as a packaged application for Macintosh OS X and Microsoft Windows and can be compiled for Linux.

Contact: anne@broadinstitute.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics – recent issues

]]> http://bioinformaticsdirectory.com/5303/improved-structure-function-and-compatibility-for-cellprofiler-modular-high-throughput-image-analysis-software/feed/ 0 Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications (Wiley Series in Bioinformatics) http://bioinformaticsdirectory.com/5301/algorithms-in-computational-molecular-biology-techniques-approaches-and-applications-wiley-series-in-bioinformatics/ http://bioinformaticsdirectory.com/5301/algorithms-in-computational-molecular-biology-techniques-approaches-and-applications-wiley-series-in-bioinformatics/#comments Thu, 14 Apr 2011 14:15:24 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5301/algorithms-in-computational-molecular-biology-techniques-approaches-and-applications-wiley-series-in-bioinformatics/ Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications (Wiley Series in Bioinformatics)

This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.

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Price: $ 123.65

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]]> http://bioinformaticsdirectory.com/5301/algorithms-in-computational-molecular-biology-techniques-approaches-and-applications-wiley-series-in-bioinformatics/feed/ 0 BiC: a web server for calculating bimodality of coexpression between gene and protein networks http://bioinformaticsdirectory.com/5299/bic-a-web-server-for-calculating-bimodality-of-coexpression-between-gene-and-protein-networks/ http://bioinformaticsdirectory.com/5299/bic-a-web-server-for-calculating-bimodality-of-coexpression-between-gene-and-protein-networks/#comments Thu, 14 Apr 2011 14:15:01 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5299/bic-a-web-server-for-calculating-bimodality-of-coexpression-between-gene-and-protein-networks/ Summary: Bimodal patterns of expression have recently been shown to be useful not only in prioritizing genes that distinguish phenotypes, but also in prioritizing network models that correlate with proteomic evidence. In particular, subgroups of strongly coexpressed gene pairs result in an increased variance of the correlation distribution. This variance, a measure of association between sets of genes (or proteins), can be summarized as the bimodality of coexpression (BiC). We developed an online tool to calculate the BiC for user-defined gene lists and associated mRNA expression data. BiC is a comprehensive application that provides researchers with the ability to analyze both publicly available and user-collected array data.

Availability: The freely available web service and the documentation can be accessed at http://gurkan.case.edu/software.

Contact: gurkan@case.edu

Bioinformatics – recent issues

]]> http://bioinformaticsdirectory.com/5299/bic-a-web-server-for-calculating-bimodality-of-coexpression-between-gene-and-protein-networks/feed/ 0 SiGN-SSM: open source parallel software for estimating gene networks with state space models http://bioinformaticsdirectory.com/5298/sign-ssm-open-source-parallel-software-for-estimating-gene-networks-with-state-space-models/ http://bioinformaticsdirectory.com/5298/sign-ssm-open-source-parallel-software-for-estimating-gene-networks-with-state-space-models/#comments Thu, 14 Apr 2011 14:14:59 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5298/sign-ssm-open-source-parallel-software-for-estimating-gene-networks-with-state-space-models/ Summary: SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles.

Availability: SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site.

Contact: tamada@ims.u-tokyo.ac.jp

Bioinformatics – recent issues

]]> http://bioinformaticsdirectory.com/5298/sign-ssm-open-source-parallel-software-for-estimating-gene-networks-with-state-space-models/feed/ 0 Algorithms in Bioinformatics: A Practical Introduction (Chapman & Hall/CRC Mathematical & Computational Biology) http://bioinformaticsdirectory.com/5297/algorithms-in-bioinformatics-a-practical-introduction-chapman-hallcrc-mathematical-computational-biology/ http://bioinformaticsdirectory.com/5297/algorithms-in-bioinformatics-a-practical-introduction-chapman-hallcrc-mathematical-computational-biology/#comments Thu, 14 Apr 2011 14:14:58 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5297/algorithms-in-bioinformatics-a-practical-introduction-chapman-hallcrc-mathematical-computational-biology/ Algorithms in Bioinformatics: A Practical Introduction (Chapman & Hall/CRC Mathematical & Computational Biology)

Thoroughly Describes Biological Applications, Computational Problems, and Various Algorithmic Solutions

Developed from the author’s own teaching material, Algorithms in Bioinformatics: A Practical Introduction provides an in-depth introduction to the algorithmic techniques applied in bioinformatics. For each topic, the author clearly details the biological motivation and precisely defines the corresponding computational problems. He also includes detailed examples to illustrate each algorithm and end-of-chapter exercises for students to familiarize themselves with the topics. Supplementary material is available at http://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/

This classroom-tested textbook begins with basic molecular biology concepts. It then describes ways to measure sequence similarity, presents simple applications of the suffix tree, and discusses the problem of searching sequence databases. After introducing methods for aligning multiple biological sequences and genomes, the text explores applications of the phylogenetic tree, methods for comparing phylogenetic trees, the problem of genome rearrangement, and the problem of motif finding. It also covers methods for predicting the secondary structure of RNA and for reconstructing the peptide sequence using mass spectrometry. The final chapter examines the computational problem related to population genetics.

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Price: $ 58.50

]]> http://bioinformaticsdirectory.com/5297/algorithms-in-bioinformatics-a-practical-introduction-chapman-hallcrc-mathematical-computational-biology/feed/ 0 STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB http://bioinformaticsdirectory.com/5295/stochsimgpu-parallel-stochastic-simulation-for-the-systems-biology-toolbox-2-for-matlab/ http://bioinformaticsdirectory.com/5295/stochsimgpu-parallel-stochastic-simulation-for-the-systems-biology-toolbox-2-for-matlab/#comments Thu, 14 Apr 2011 14:14:31 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5295/stochsimgpu-parallel-stochastic-simulation-for-the-systems-biology-toolbox-2-for-matlab/ Motivation: The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB.

Results: The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM) and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user’s models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2.

Availability: The software is open source under the GPL v3 and available at http://www.maths.ox.ac.uk/cmb/STOCHSIMGPU. The web site also contains supplementary information.

Contact: klingbeil@maths.ox.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics – recent issues

]]> http://bioinformaticsdirectory.com/5295/stochsimgpu-parallel-stochastic-simulation-for-the-systems-biology-toolbox-2-for-matlab/feed/ 0 SurvJamda: an R package to predict patients’ survival and risk assessment using joint analysis of microarray gene expression data http://bioinformaticsdirectory.com/5294/survjamda-an-r-package-to-predict-patients-survival-and-risk-assessment-using-joint-analysis-of-microarray-gene-expression-data/ http://bioinformaticsdirectory.com/5294/survjamda-an-r-package-to-predict-patients-survival-and-risk-assessment-using-joint-analysis-of-microarray-gene-expression-data/#comments Thu, 14 Apr 2011 14:14:29 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5294/survjamda-an-r-package-to-predict-patients-survival-and-risk-assessment-using-joint-analysis-of-microarray-gene-expression-data/ Summary: SurvJamda (Survival prediction by joint analysis of microarray data) is an R package that utilizes joint analysis of microarray gene expression data to predict patients’ survival and risk assessment. Joint analysis can be performed by merging datasets or meta-analysis to increase the sample size and to improve survival prognosis. The prognosis performance derived from the combined datasets can be assessed to determine which feature selection approach, joint analysis method and bias estimation provide the most robust prognosis for a given set of datasets.

Availability: The survJamda package is available at the Comprehensive R Archive Network, http://cran.r-project.org.

Contact: hyasrebi@yahoo.com

Bioinformatics – recent issues

]]> http://bioinformaticsdirectory.com/5294/survjamda-an-r-package-to-predict-patients-survival-and-risk-assessment-using-joint-analysis-of-microarray-gene-expression-data/feed/ 0 Bioinformatics: An Introduction (Computational Biology) http://bioinformaticsdirectory.com/5247/bioinformatics-an-introduction-computational-biology/ http://bioinformaticsdirectory.com/5247/bioinformatics-an-introduction-computational-biology/#comments Mon, 11 Apr 2011 23:14:43 +0000 Bioinformatics Computational Biology http://bioinformaticsdirectory.com/5247/bioinformatics-an-introduction-computational-biology/ Bioinformatics: An Introduction (Computational Biology)

Bioinformatics is interpreted as the application of information science to biology, in which it plays a fundamental and all-pervasive role. The field continues to develop intensively in both academia and commercially, and is highly interdisciplinary. This broad-ranging and thoroughly updated second edition covers new findings while retaining the successful formula of the original text.

Bioinformatics: An Introduction is structured into three parts devoted to Information, Biology, and Applications. Every section of the book has been completely revised for currency, and expanded where relevant, to take account of significant new discoveries and realizations of the importance of key concepts. Furthermore, two new chapters provide instruction about algorithms and knowledge representation. Emphases are placed on the underlying fundamentals and on acquisition of a broad and comprehensive grasp of the field as a whole.

Features:

• Provides a solid foundation in, and self-contained introduction to, the field of bioinformatics and its state-of-the-art as it relates to computational biology research

• Imparts a thorough grounding of core concepts, enabling the reader to understand contemporary work within an optimal context

• Includes examples, definitions, problems and a comprehensive and useful bibliography

• Offers additional chapters on algorithms and knowledge representation, including text mining [NEW]

• Presents new experimental methods, and serves as a springboard for new research [NEW]

• Contains a greatly expanded chapter on interactions and regulatory networks [NEW]

• Incorporates discussion of the method of drawing inferences from abstract sequence analysis based on frequency dictionaries

• Contains an extensively revised chapter on medical applications [NEW]

• Emphasizes the underlying fundamentals and acquisition of a broad and comprehensive grasp of the field as a whole

This significantly improved second edition of a successful textbook is intended to be a complete study companion for the advanced undergraduate or graduate student. It is self-contained, bringing together the multiple disciplines necessary for a profound grasp of the field into a coherent whole, thereby allowing the reader to gain much insight into the state-of-the-art of bioinformatics.

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Price: $ 47.99

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