eGADA: enhanced Genomic Alteration Detection Algorithm, a fast genomic segmentation algorithm based on Sparse Bayesian Learning

eGADA is an enhanced version of GADA, which is a fast segmentation algorithm utilizing the Sparse Bayesian Learning (or Relevance Vector Machine) technique from Tipping 2001. It can be applied to array intensity data, NGS sequencing coverage data, or any sequential data that displays characteristics of stepwise functions. Improvements by eGADA over GADA include: a) a customized Red-Black tree to expedite the final backward elimination step of GADA; b) code in C++, which is safer and better structured than C; c) use Boost libraries extensively to provide user-friendly help and commandline argument processing; d) user-friendly input and output formats; e) export a dynamic library eGADA.so (packaged via Boost.Python) that offers API to Python; f) other bug fixes/optimization. The code is published at https://github.com/polyactis/eGADA.