Automatic recognition of regions of intrinsically poor multiple alignment using machine learning

Phylogenetic analysis requires alignment of gene or protein sequences. Some regions of genes evolve fast and suffer numerous insertion and deletion events and cannot be aligned reliably with automatic alignment algorithms. Such regions of intrinsically uncertain alignment are currently detected and deleted manually before performing phylogenetic analysis. We present the results of a machine learning approach to detect regions of poor alignment automatically. We compare the results obtained from Naive Bayes (NB), C4.5 decision tree (C4.5) and support vector machine (SVM) approaches.