from the normal background variability. Another new class of indel-detection software is those based on machine learning methods in which insertions and deletions identified by various methods are filtered against empirically derived training set data to reduce the false-positive rate (63). These newer methods have yet to be rigorously tested but promise to reduce the inherent false-positive rate of indel detection, especially in homopolymer tracts and areas of low sequence complexity.
