May 8, 2017 at 19:52
Mareschal et al, Bioinformatics 2017
Related entries:
- 07/12/2015 - Mareschal et al, ISMB/ECCB 2015
- 02/24/2017 - cghRA
- 05/08/2017 - Mareschal et al, Bioinformatics 2017
Bioinformatics. 2017 May 8.
doi: 10.1093/bioinformatics/btx309.
[Epub ahead of print]
Application of the cghRA framework to the genomic characterization of Diffuse Large B-Cell Lymphoma.
Mareschal S, Ruminy P, Alcantara M, Villenet C, Figeac M, Dubois S, Bertrand P, Bouzelfen A, Viailly PJ, Penther D, Tilly H, Bastard C, Jardin F.
Motivation: Although sequencing-based technologies are becoming the new reference in genome analysis, comparative genomic hybridization arrays (aCGH) still constitute a simple and reliable approach for copy number analysis. The most powerful algorithms to analyse such data have been freely provided by the scientific community for many years, but combining them is a complex scripting task.
Results: The cghRA framework combines a user-friendly graphical interface and a powerful object-oriented command-line interface to handle a full aCGH analysis, as is illustrated in an original series of 107 Diffuse Large B-Cell Lymphomas. New algorithms for copy-number calling, polymorphism detection and minimal common region (MCR) prioritization were also developed and validated. While their performances will only be demonstrated with aCGH, these algorithms could actually prove useful to any copy-number analysis, whatever the technique used.
Availability and Implementation: R package and source for Linux, MS Windows and MacOS are freely available at http://bioinformatics.ovsa.fr/cghRA.
Contact: mareschal@ovsa.fr.
Supplementary information: Supplementary data are available at Bioinformatics online.
doi: 10.1093/bioinformatics/btx309.
[Epub ahead of print]
Application of the cghRA framework to the genomic characterization of Diffuse Large B-Cell Lymphoma.
Mareschal S, Ruminy P, Alcantara M, Villenet C, Figeac M, Dubois S, Bertrand P, Bouzelfen A, Viailly PJ, Penther D, Tilly H, Bastard C, Jardin F.
Motivation: Although sequencing-based technologies are becoming the new reference in genome analysis, comparative genomic hybridization arrays (aCGH) still constitute a simple and reliable approach for copy number analysis. The most powerful algorithms to analyse such data have been freely provided by the scientific community for many years, but combining them is a complex scripting task.
Results: The cghRA framework combines a user-friendly graphical interface and a powerful object-oriented command-line interface to handle a full aCGH analysis, as is illustrated in an original series of 107 Diffuse Large B-Cell Lymphomas. New algorithms for copy-number calling, polymorphism detection and minimal common region (MCR) prioritization were also developed and validated. While their performances will only be demonstrated with aCGH, these algorithms could actually prove useful to any copy-number analysis, whatever the technique used.
Availability and Implementation: R package and source for Linux, MS Windows and MacOS are freely available at http://bioinformatics.ovsa.fr/cghRA.
Contact: mareschal@ovsa.fr.
Supplementary information: Supplementary data are available at Bioinformatics online.
Pubmed, PMID: 28481978