Dataset Information

Analysis of array-CGH data using the R and Bioconductor software suite.

ABSTRACT: BACKGROUND:Array-based comparative genomic hybridization (array-CGH) is an emerging high-resolution and high-throughput molecular genetic technique that allows genome-wide screening for chromosome alterations. DNA copy number alterations (CNAs) are a hallmark of somatic mutations in tumor genomes and congenital abnormalities that lead to diseases such as mental retardation. However, accurate identification of amplified or deleted regions requires a sequence of different computational analysis steps of the microarray data. RESULTS:We have developed a user-friendly and versatile tool for the normalization, visualization, breakpoint detection, and comparative analysis of array-CGH data which allows the accurate and sensitive detection of CNAs. CONCLUSION:The implemented option for the determination of minimal altered regions (MARs) from a series of tumor samples is a step forward in the identification of new tumor suppressor genes or oncogenes.

SUBMITTER: Hofmann WA

PROVIDER: S-EPMC2728899 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Analysis of array-CGH data using the R and Bioconductor software suite.

Hofmann Winfried A WA Weigmann Anja A Tauscher Marcel M Skawran Britta B Focken Tim T Buurman Reena R Wingen Luzie U LU Schlegelberger Brigitte B Steinemann Doris D

Comparative and functional genomics 20090819

<h4>Background</h4>Array-based comparative genomic hybridization (array-CGH) is an emerging high-resolution and high-throughput molecular genetic technique that allows genome-wide screening for chromosome alterations. DNA copy number alterations (CNAs) are a hallmark of somatic mutations in tumor genomes and congenital abnormalities that lead to diseases such as mental retardation. However, accurate identification of amplified or deleted regions requires a sequence of different computational ana ...[more]

PMID: 19696946

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Project description:Motivation. Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background. Results. A software suite named Splicing Express was created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills. Splicing Express performs automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool named Splooce. As a final result, Splicing Express creates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show that Splicing Express is able to perform all tasks in a straightforward way, identifying well-known specific events. Availability and Implementation. Splicing Express is written in Perl and is suitable to run only in UNIX-like systems. More details can be found at: http://www.bioinformatics-brazil.org/splicingexpress.

Dataset Information

Analysis of array-CGH data using the R and Bioconductor software suite.

Publications

Analysis of array-CGH data using the R and Bioconductor software suite.

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