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rCGH

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A comprehensive array-based comparative genomic hybridization analysis workflow, integrating computational improvements and functionalities specifically designed for precision medicine. rCGH ensures the traceability of the entire process of individual samples and provides interactive visualization tools allowing to better interpret—and potentially reprocess—genomic profiles, individually. The web-server application can assist oncologists in reviewing copy-number alterations in genomic profiles and identifying matched therapeutic orientations. rCGH supports the major microarray platforms and facilitates profiles interpretation and decision-making through sharable interactive visualizations.

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rCGH classification

rCGH specifications

Software type:
Package/Module
Restrictions to use:
None
Output data:
rCGH stores all the original and computed data, as well as the workflow parameters, to ensure traceability. Segmentation tables are of the same format as standard circular binary segmentation (CBS) outputs, completed with the segment lengths and the within-segment log2 relative ratios (LRR) standard deviation.
Operating system:
Unix/Linux, Mac OS, Windows
License:
Artistic License version 2.0
Version:
1.0.2
Requirements:
methods
Interface:
Command line interface
Input data:
As input rCGH supports Agilent Human CGH data, from 44K to 400K, and Affymetrix, SNP6 and cytoScanHD. All are provided in text format by platform-specific software: standard Agilent text files are exported from Agilent Feature Extraction software (FE), while Affymetrix cychp.txt, cnchp.txt or probeset.txt files are obtained by processing Affymetrix CEL files through ChAS or Affymetrix Power Tools (APT) software. Custom arrays can also be supported, provided the data format complies with the requirements.
Biological technology:
Affymetrix, Agilent Technologies
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes

rCGH distribution

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rCGH support

Documentation

Credits

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Publications

Institution(s)

INSERM U981, Gustave Roussy, University Paris-Sud, Villejuif, France; Sage Bionetworks, Seattle, WA, USA; Department of Medical Oncology, Gustave Roussy, Villejuif, France

Funding source(s)

This work was supported by the Integrative Cancer Biology Program of the National Cancer Institute (U54CA149237), Unicancer, the ARC foundation, the Breast Cancer Research foundation and Odyssea.

Link to literature

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