35,935 research outputs found

    A novel R-package graphic user interface for the analysis of metabonomic profiles

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    Background Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries. /Results The package offers the following options: Raw 1-dimensional spectra processing: phase, baseline correction and normalization. Importing processed spectra. Including/excluding spectral ranges, optional binning and bucketing, detection and alignment of peaks. Sorting of metabolites based on their ability to discriminate, metabolite selection, and outlier identification. Multivariate unsupervised analysis: principal components analysis (PCA). Multivariate supervised analysis: partial least squares (PLS), linear discriminant analysis (LDA), k-nearest neighbor classification. Neural networks. Visualization and overlapping of spectra. Plot values of the chemical shift position for different samples. Furthermore, the "Metabonomic" GUI includes a console to enable other kinds of analyses and to take advantage of all R statistical tools. /Conclusion We made complex multivariate analysis user-friendly for both experienced and novice users, which could help to expand the use of NMR-based metabonomics

    DEVELOPMENT AND IMPLEMENTATION OF A BIOINFORMATICS ONLINE DISTANCE EDUCATION LEARNING TOOL FOR AFRICA

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    Bioinformatics refers to the creation and advancement of algorithms, computational and statistical techniques and theories for solving formal and practical problems arising from the management and analysis of biological data. However, some parts of the African continent have not been properly sensitized to bio-scientific and computing field. Thus, there is the need for appropriate strategies of introducing the basic components of this emerging scientific field to part of the African populace through the development of an online distance education learning tool. This study involved the design of a bioinformatics online distance educative tool an implementation of the bioinformatics online distance educative tool by a programming approach. Design and implementation were done using the Borland Delphi 7 Enterprise edition within its Integrated Development Environment. The advantage of using Delphi programming language in implementing this useful bioinformatics web tool is that Delphi programming language is an object oriented programming language that has a lot of extra facilities for the enhancement of further technical functions, which ordinary HTML cannot handle. The development and use of a bioinformatics distance education software, as a teaching tool, in some African countries holds great promise for accommodating the needs of the populace, who live in cities, small towns and remote areas

    Pranab Kumar Sen: Life and works

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    In this article, we describe briefly the highlights and various accomplishments in the personal as well as the academic life of Professor Pranab Kumar Sen.Comment: Published in at http://dx.doi.org/10.1214/193940307000000013 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

    Statistical methods of SNP data analysis with applications

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    Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stochastic gradient boosting. These methods and their new modifications, e.g., the MDR method with "independent rule", are used to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and external risk factors are examined. To perform the data analysis concerning the ischemic heart disease and myocardial infarction the supercomputer SKIF "Chebyshev" of the Lomonosov Moscow State University was employed

    Methods to study splicing from high-throughput RNA Sequencing data

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    The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data. We group the methods according to the different questions they address: 1) Assignment of the sequencing reads to their likely gene of origin. This is addressed by methods that map reads to the genome and/or to the available gene annotations. 2) Recovering the sequence of splicing events and isoforms. This is addressed by transcript reconstruction and de novo assembly methods. 3) Quantification of events and isoforms. Either after reconstructing transcripts or using an annotation, many methods estimate the expression level or the relative usage of isoforms and/or events. 4) Providing an isoform or event view of differential splicing or expression. These include methods that compare relative event/isoform abundance or isoform expression across two or more conditions. 5) Visualizing splicing regulation. Various tools facilitate the visualization of the RNA-Seq data in the context of alternative splicing. In this review, we do not describe the specific mathematical models behind each method. Our aim is rather to provide an overview that could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde

    R Programming for Bioinformatics

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