INSERM U1245 Team "Genomics and Biomarkers in Lymphoma and Solid Tumors," Centre Henri Becquerel, 76000 Rouen, France.
Normandie Université, 14000 Caen, France.
Bioinformatics. 2017 Oct 1;33(19):2977-2985. doi: 10.1093/bioinformatics/btx309.
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 analyze such data have been freely provided by the scientific community for many years, but combining them is a complex scripting task.
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 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.
R package and source for Linux, MS Windows and MacOS are freely available at http://bioinformatics.ovsa.fr/cghRA.
mareschal@ovsa.fr or fabrice.jardin@chb.unicancer.fr.
Supplementary data are available at Bioinformatics online.
虽然基于测序的技术正在成为基因组分析的新参考,但比较基因组杂交阵列 (aCGH) 仍然是一种用于拷贝数分析的简单可靠的方法。多年来,科学界一直免费提供用于分析此类数据的最强大算法,但将它们组合在一起是一项复杂的脚本任务。
cghRA 框架结合了用户友好的图形界面和功能强大的面向对象的命令行界面,以处理完整的 aCGH 分析,如图 107 例弥漫性大 B 细胞淋巴瘤的原始系列所示。还开发和验证了用于拷贝数调用、多态性检测和最小常见区域优先级排序的新算法。虽然它们的性能仅在 aCGH 上进行了演示,但这些算法实际上可能对任何拷贝数分析都有用,无论使用何种技术。
适用于 Linux、MS Windows 和 MacOS 的 R 包和源代码可在 http://bioinformatics.ovsa.fr/cghRA 上免费获得。
mareschal@ovsa.fr 或 fabrice.jardin@chb.unicancer.fr。
补充数据可在 Bioinformatics 在线获得。