共查询到20条相似文献,搜索用时 15 毫秒
1.
Background
In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools. 相似文献2.
Background
As biology becomes an increasingly computational science, it is critical that we develop software tools that support not only bioinformaticians, but also bench biologists in their exploration of the vast and complex data-sets that continue to build from international genomic, proteomic, and systems-biology projects. The BioMoby interoperability system was created with the goal of facilitating the movement of data from one Web-based resource to another to fulfill the requirements of non-expert bioinformaticians. In parallel with the development of BioMoby, the European myGrid project was designing Taverna, a bioinformatics workflow design and enactment tool. Here we describe the marriage of these two projects in the form of a Taverna plug-in that provides access to many of BioMoby's features through the Taverna interface. 相似文献3.
Filippo Rusconi 《BMC bioinformatics》2006,7(1):226-16
Background
Nowadays, a variety of (bio-)polymers can be analyzed by mass spectrometry. The detailed interpretation of the spectra requires a huge number of "hypothesis cycles", comprising the following three actions 1) put forth a structural hypothesis, 2) test it, 3) (in)validate it. This time-consuming and painstaking data scrutiny is alleviated by using specialized software tools. However, all the software tools available to date are polymer chemistry-specific. This imposes a heavy overhead to researchers who do mass spectrometry on a variety of (bio-)polymers, as each polymer type will require a different software tool to perform data simulations and analyses. We developed a software to address the lack of an integrated software framework able to deal with different polymer chemistries. 相似文献4.
Riadh Hammami Abdelmajid Zouhir Karim Naghmouchi Jeannette Ben Hamida Ismail Fliss 《BMC bioinformatics》2008,9(1):121
Background
The exponential growth of research in molecular biology has brought concomitant proliferation of databases for stocking its findings. A variety of protein sequence databases exist. While all of these strive for completeness, the range of user interests is often beyond their scope. Large databases covering a broad range of domains tend to offer less detailed information than smaller, more specialized resources, often creating a need to combine data from many sources in order to obtain a complete picture. Scientific researchers are continually developing new specific databases to enhance their understanding of biological processes. 相似文献5.
Sohrab?P?Shah David?YM?He Jessica?N?Sawkins Jeffrey?C?Druce Gerald?Quon Drew?Lett Grace?XY?Zheng Tao?Xu BF?Francis?Ouellette
Background
We present Pegasys – a flexible, modular and customizable software system that facilitates the execution and data integration from heterogeneous biological sequence analysis tools. 相似文献6.
Rachel Spicer Reza M. Salek Pablo Moreno Daniel Cañueto Christoph Steinbeck 《Metabolomics : Official journal of the Metabolomic Society》2017,13(9):106
Introduction
The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.Objectives
To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.Methods
The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC–MS, GC–MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.Results
A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.Conclusion
This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools’ abilities to perform specific data analysis tasks e.g. peak picking.7.
Vidar Beisvag Frode KR Jünge Hallgeir Bergum Lars Jølsum Stian Lydersen Clara-Cecilie Günther Heri Ramampiaro Mette Langaas Arne K Sandvik Astrid Lægreid 《BMC bioinformatics》2006,7(1):470-13
Background
Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions. 相似文献8.
9.
Thierry Lombardot Renzo Kottmann Gregory Giuliani Andrea de Bono Nans Addor Frank Oliver Glöckner 《BMC bioinformatics》2007,8(1):406
Background
Marine ecological genomics can be defined as the application of genomic sciences to understand the structure and function of marine ecosystems. In this field of research, the analysis of genomes and metagenomes of environmental relevance must take into account the corresponding habitat (contextual) data, e.g. water depth, physical and chemical parameters. The creation of specialised software tools and databases is requisite to allow this new kind of integrated analysis. 相似文献10.
Background
Various software tools are available for the display of pairwise linkage disequilibrium across multiple single nucleotide polymorphisms. The HapMap project also presents these graphics within their website. However, these approaches are limited in their use of data from multiallelic markers and provide limited information in a graphical form. 相似文献11.
Background
Sequence data analyses such as gene identification, structure modeling or phylogenetic tree inference involve a variety of bioinformatics software tools. Due to the heterogeneity of bioinformatics tools in usage and data requirements, scientists spend much effort on technical issues including data format, storage and management of input and output, and memorization of numerous parameters and multi-step analysis procedures. 相似文献12.
Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations 总被引:1,自引:0,他引:1
Background
During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. 相似文献13.
Benjamin Misselwitz Gerhard Strittmatter Balamurugan Periaswamy Markus C Schlumberger Samuel Rout Peter Horvath Karol Kozak Wolf-Dietrich Hardt 《BMC bioinformatics》2010,11(1):30
Background
Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories. 相似文献14.
Maciej Paszkowski-Rogacz Mikolaj Slabicki M Teresa Pisabarro Frank Buchholz 《BMC bioinformatics》2010,11(1):254
Background
With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam. 相似文献15.
Claudia Angelini Luisa Cutillo Daniela De Canditiis Margherita Mutarelli Marianna Pensky 《BMC bioinformatics》2008,9(1):415
Background
Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient. 相似文献16.
Background
Two-dimensional data colourings are an effective medium by which to represent three-dimensional data in two dimensions. Such "color-grid" representations have found increasing use in the biological sciences (e.g. microarray 'heat maps' and bioactivity data) as they are particularly suited to complex data sets and offer an alternative to the graphical representations included in traditional statistical software packages. The effectiveness of color-grids lies in their graphical design, which introduces a standard for customizable data representation. Currently, software applications capable of generating limited color-grid representations can be found only in advanced statistical packages or custom programs (e.g. micro-array analysis tools), often associated with steep learning curves and requiring expert knowledge. 相似文献17.
Paschall JE Oleksiak MF VanWye JD Roach JL Whitehead JA Wyckoff GJ Kolell KJ Crawford DL 《BMC genomics》2004,5(1):96
Background
While studies of non-model organisms are critical for many research areas, such as evolution, development, and environmental biology, they present particular challenges for both experimental and computational genomic level research. Resources such as mass-produced microarrays and the computational tools linking these data to functional annotation at the system and pathway level are rarely available for non-model species. This type of "systems-level" analysis is critical to the understanding of patterns of gene expression that underlie biological processes. 相似文献18.
Background
Genome databases contain diverse kinds of information, including gene annotations and nucleotide and amino acid sequences. It is not easy to integrate such information for genomic study. There are few tools for integrated analyses of genomic data, therefore, we developed software that enables users to handle, manipulate, and analyze genome data with a variety of sequence analysis programs. 相似文献19.
Background
The development of software tools that analyze microarray data in the context of genetic knowledgebases is being pursued by multiple research groups using different methods. A common problem for many of these tools is how to correct for multiple statistical testing since simple corrections are overly conservative and more sophisticated corrections are currently impractical. A careful study of the nature of the distribution one would expect by chance, such as by a simulation study, may be able to guide the development of an appropriate correction that is not overly time consuming computationally. 相似文献20.
Philipp N Seibel Jan Krüger Sven Hartmeier Knut Schwarzer Kai L?wenthal Henning Mersch Thomas Dandekar Robert Giegerich 《BMC bioinformatics》2006,7(1):490