Bioinformatics
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A Web-based visualization tool, the Microbial Genome Viewer, is presented that allows the user to combine complex genomic data in a highly interactive way. This Web tool enables the interactive generation of chromosome wheels and linear genome maps from genome annotation data stored in a MySQL database. The generated images are in scalable vector graphics (SVG) format, which is suitable for creating high-quality scalable images and dynamic Web representations. Gene-related data such as transcriptome and time-course microarray experiments can be superimposed on the maps for visual inspection. ⋯ The Microbial Genome Viewer 1.0 is freely available at http://www.cmbi.kun.nl/MGV
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Comparative Study
Venn Mapping: clustering of heterologous microarray data based on the number of co-occurring differentially expressed genes.
To evaluate microarray data, clustering is widely used to group biological samples or genes. However, problems arise when comparing heterologous databases. As the clustering algorithm searches for similarities between experiments, it will most likely first separate the data sets, masking relationships that exist between samples from different databases. ⋯ http://www.erasmusmc.nl/gatcplatform/vennmapper.html.
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BioQuery is an application that helps scientists automate database searches. Users can build and store queries to public biomedical databases, and receive periodic updates on the results of those queries when new data is available. The application is implemented on a portable object framework that can provide database-searching capability to other applications. This framework is easily extensible, allowing users to develop plug-ins that provide access to new databases. BioQuery thus provides end-users with a complete database searching interface and updating service, and gives developers a toolkit to provide database-searching capability to their applications. ⋯ Free to all users: http://www.bioquery.org.
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Comparative Study
Rank order metrics for quantifying the association of sequence features with gene regulation.
Genome sequences and transcriptome analyses allow the correlation between gene regulation and DNA sequence features to be studied at the whole-genome level. To quantify these correlations, metrics are needed that can be applied to any sequence feature, regardless of its statistical distribution. It is also desirable for the metric values to be determined objectively, that is, without the use of subjective threshold values. ⋯ A Python program for calculating the ROC AUC and MNCP metric values given input rank orders is available from ftp://ftp.bs.jhmi.edu/users/nclarke/MNCP/
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"Database Referencing of Array Genes ONline" or "DRAGON" is a web-accessible database that aids in the analysis of differential gene expression data as a biological annotation tool. Users of DRAGON can submit data sets containing large lists of genes and then choose particular characteristics that DRAGON supplies for all genes on the list rapidly and simultaneously. ⋯ pevsner@kennedykrieger.org or cbouton@jhmi.edu