BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data.
Ontology highlight
ABSTRACT:
SUBMITTER: Saal LH
PROVIDER: S-EPMC139402 | biostudies-literature | 2002 Jul
REPOSITORIES: biostudies-literature
Publications
BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data.
Saal Lao H LH Troein Carl C Vallon-Christersson Johan J Gruvberger Sofia S Borg Ake A Peterson Carsten C
Genome biology 20020715 8
The microarray technique requires the organization and analysis of vast amounts of data. These data include information about the samples hybridized, the hybridization images and their extracted data matrices, and information about the physical array, the features and reporter molecules. We present a web-based customizable bioinformatics solution called BioArray Software Environment (BASE) for the management and analysis of all areas of microarray experimentation. All software necessary to run a ...[more]
Similar Datasets
Project description:BACKGROUND: Advances in automated DNA sequencing technology have accelerated the generation of metagenomic DNA sequences, especially environmental ribosomal RNA gene (rDNA) sequences. As the scale of rDNA-based studies of microbial ecology has expanded, need has arisen for software that is capable of managing, annotating, and analyzing the plethora of diverse data accumulated in these projects. RESULTS: XplorSeq is a software package that facilitates the compilation, management and phylogenetic analysis of DNA sequences. XplorSeq was developed for, but is not limited to, high-throughput analysis of environmental rRNA gene sequences. XplorSeq integrates and extends several commonly used UNIX-based analysis tools by use of a Macintosh OS-X-based graphical user interface (GUI). Through this GUI, users may perform basic sequence import and assembly steps (base-calling, vector/primer trimming, contig assembly), perform BLAST (Basic Local Alignment and Search Tool; 123) searches of NCBI and local databases, create multiple sequence alignments, build phylogenetic trees, assemble Operational Taxonomic Units, estimate biodiversity indices, and summarize data in a variety of formats. Furthermore, sequences may be annotated with user-specified meta-data, which then can be used to sort data and organize analyses and reports. A document-based architecture permits parallel analysis of sequence data from multiple clones or amplicons, with sequences and other data stored in a single file. CONCLUSION: XplorSeq should benefit researchers who are engaged in analyses of environmental sequence data, especially those with little experience using bioinformatics software. Although XplorSeq was developed for management of rDNA sequence data, it can be applied to most any sequencing project. The application is available free of charge for non-commercial use at http://vent.colorado.edu/phyloware.
| S-EPMC2577119 | biostudies-literature
Project description:BackgroundMicroarray core facilities are commonplace in biological research organizations, and need systems for accurately tracking various logistical aspects of their operation. Although these different needs could be handled separately, an integrated management system provides benefits in organization, automation and reduction in errors.ResultsWe present SLIMarray (System for Lab Information Management of Microarrays), an open source, modular database web application capable of managing microarray inventories, sample processing and usage charges. The software allows modular configuration and is well suited for further development, providing users the flexibility to adapt it to their needs. SLIMarray Lite, a version of the software that is especially easy to install and run, is also available.ConclusionSLIMarray addresses the previously unmet need for free and open source software for managing the logistics of a microarray core facility.
| S-EPMC1636632 | biostudies-literature
Project description:Exploration of the underlying biological mechanisms of disease is useful for many purposes such as the development of novel treatment modalities in addition to informing on-going risk factor research. DNA-microarray technology is a relatively recent and novel approach to conducting genome-wide gene expression studies to identify previously unknown biological pathways associated with disease. The copious data arising from microarray experiments is not conducive to traditional analytical approaches. Beyond the analytical challenges, there are equally important issues related to the interpretation and presentation of results. This chapter outlines appropriate techniques for analyzing microarray data in a fashion that also yields a list of top genes with differential expression in a given experiment. Derivatives of the top genes list can be used as a starting point for the presentation of study results. The list also serves as the basis for additional techniques related to enhanced interpretation and presentation of results. All analyses described in this chapter can be performed using relatively limited computational resources such as a lap top PC with at least 2.0 GB of memory and 2.0 GHz of processing speed.
| S-EPMC6824719 | biostudies-literature
Project description:SummaryProtein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments.Availability and implementationMAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT.Contactjan.tavernier@vib-ugent.be or lennart.martens@vib-ugent.be.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5408788 | biostudies-literature
Project description:BACKGROUND: The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. RESULTS: Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. CONCLUSIONS: Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.
| S-EPMC3215701 | biostudies-literature
Project description:DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental designs used to collect data become more complex. An important example is multigroup data collected over different experimental groups, such as data collected from distinct stages of a disease process. We have developed a method specifically addressing these issues termed Bayesian ANOVA for microarrays (BAM). The BAM approach uses a special inferential regularization known as spike-and-slab shrinkage that provides an optimal balance between total false detections and total false non-detections. This translates into more reproducible differential calls. Spike and slab shrinkage is a form of regularization achieved by using information across all genes and groups simultaneously.BAMarray is a graphically oriented Java-based software package that implements the BAM method for detecting differentially expressing genes in multigroup microarray experiments (up to 256 experimental groups can be analyzed). Drop-down menus allow the user to easily select between different models and to choose various run options. BAMarraycan also be operated in a fully automated mode with preselected run options. Tuning parameters have been preset at theoretically optimal values freeing the user from such specifications. BAMarray provides estimates for gene differential effects and automatically estimates data adaptive, optimal cutoff values for classifying genes into biological patterns of differential activity across experimental groups. A graphical suite is a core feature of the product and includes diagnostic plots for assessing model assumptions and interactive plots that enable tracking of prespecified gene lists to study such things as biological pathway perturbations. The user can zoom in and lasso genes of interest that can then be saved for downstream analyses.BAMarray is user friendly platform independent software that effectively and efficiently implements the BAM methodology. Classifying patterns of differential activity is greatly facilitated by a data adaptive cutoff rule and a graphical suite. BAMarray is licensed software freely available to academic institutions. More information can be found at http://www.bamarray.com.
| S-EPMC1382258 | biostudies-literature
Project description:Over the last several decades, fast-scan cyclic voltammetry (FSCV) has proved to be a valuable analytical tool for the real-time measurement of neurotransmitter dynamics in vitro and in vivo. Indeed, FSCV has found application in a wide variety of disciplines including electrochemistry, neurobiology, and behavioral psychology. The maturation of FSCV as an in vivo technique led users to pose increasingly complex questions that require a more sophisticated experimental design. To accommodate recent and future advances in FSCV application, our lab has developed High Definition Cyclic Voltammetry (HDCV). HDCV is an electrochemical software suite that includes data acquisition and analysis programs. The data collection program delivers greater experimental flexibility and better user feedback through live displays. It supports experiments involving multiple electrodes with customized waveforms. It is compatible with transistor-transistor logic-based systems that are used for monitoring animal behavior, and it enables simultaneous recording of electrochemical and electrophysiological data. HDCV analysis streamlines data processing with superior filtering options, seamlessly manages behavioral events, and integrates chemometric processing. Furthermore, analysis is capable of handling single files collected over extended periods of time, allowing the user to consider biological events on both subsecond and multiminute time scales. Here we describe and demonstrate the utility of HDCV for in vivo experiments.
| S-EPMC3838858 | biostudies-literature
Project description:BACKGROUND:Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data. RESULTS:A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package. CONCLUSION:The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies.
| S-EPMC2572073 | biostudies-literature
Project description:Research studies exploring the determinants of disease require sufficient statistical power to detect meaningful effects. Sample size is often increased through centralized pooling of disparately located datasets, though ethical, privacy and data ownership issues can often hamper this process. Methods that facilitate the sharing of research data that are sympathetic with these issues and which allow flexible and detailed statistical analyses are therefore in critical need. We have created a software platform for the Virtual Pooling and Analysis of Research data (ViPAR), which employs free and open source methods to provide researchers with a web-based platform to analyse datasets housed in disparate locations.Database federation permits controlled access to remotely located datasets from a central location. The Secure Shell protocol allows data to be securely exchanged between devices over an insecure network. ViPAR combines these free technologies into a solution that facilitates 'virtual pooling' where data can be temporarily pooled into computer memory and made available for analysis without the need for permanent central storage.Within the ViPAR infrastructure, remote sites manage their own harmonized research dataset in a database hosted at their site, while a central server hosts the data federation component and a secure analysis portal. When an analysis is initiated, requested data are retrieved from each remote site and virtually pooled at the central site. The data are then analysed by statistical software and, on completion, results of the analysis are returned to the user and the virtually pooled data are removed from memory.ViPAR is a secure, flexible and powerful analysis platform built on open source technology that is currently in use by large international consortia, and is made publicly available at [http://bioinformatics.childhealthresearch.org.au/software/vipar/].
| S-EPMC4864874 | biostudies-other
Project description:BACKGROUND: Identification of transcription factors (TFs) involved in a biological process is the first step towards a better understanding of the underlying regulatory mechanisms. However, due to the involvement of a large number of genes and complicated interactions in a gene regulatory network (GRN), identification of the TFs involved in a biology process remains to be very challenging. In reality, the recognition of TFs for a given a biological process can be further complicated by the fact that most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation except for small conserved domains. This poses a significant challenge for identification of the exact TFs involved or ranking the importance of a set of TFs to a process of interest. Therefore, new methods for recognizing novel TFs are desperately needed. Although a plethora of methods have been developed to infer regulatory genes using microarray data, it is still rare to find the methods that use existing knowledge base in particular the validated genes known to be involved in a process to bait/guide discovery of novel TFs. Such methods can replace the sometimes-arbitrary process of selection of candidate genes for experimental validation and significantly advance our knowledge and understanding of the regulation of a process. RESULTS: We developed an automated software package called TF-finder for recognizing TFs involved in a biological process using microarray data and existing knowledge base. TF-finder contains two components, adaptive sparse canonical correlation analysis (ASCCA) and enrichment test, for TF recognition. ASCCA uses positive target genes to bait TFS from gene expression data while enrichment test examines the presence of positive TFs in the outcomes from ASCCA. Using microarray data from salt and water stress experiments, we showed TF-finder is very efficient in recognizing many important TFs involved in salt and drought tolerance as evidenced by the rediscovery of those TFs that have been experimentally validated. The efficiency of TF-finder in recognizing novel TFs was further confirmed by a thorough comparison with a method called Intersection of Coexpression (ICE). CONCLUSIONS: TF-finder can be successfully used to infer novel TFs involved a biological process of interest using publicly available gene expression data and known positive genes from existing knowledge bases. The package for TF-finder includes an R script for ASCCA, a Perl controller, and several Perl scripts for parsing intermediate outputs. The package is available upon request (hairong@mtu.edu). The R code for standalone ASCCA is also available.
| S-EPMC2930629 | biostudies-literature