Abstract
Microsatellites (MSs) are DNA regions consisting of repeated short motif(s). MSs are linked to several diseases and have important biomedical applications. Thus, researchers have developed several computational tools to detect MSs. However, the currently available tools require adjusting many parameters, or depend on a list of motifs or on a library of known MSs. Therefore, two laboratories analyzing the same sequence with the same computational tool may obtain different results due to the user-adjustable parameters. Recent studies have indicated the need for a standard computational tool for detecting MSs. To this end, we applied machine-learning algorithms to develop a tool called MsDetector. The system is based on a hidden Markov model and a general linear model. The user is not obligated to optimize the parameters of MsDetector. Neither a list of motifs nor a library of known MSs is required. MsDetector is memory- and time-efficient. We applied MsDetector to several species. MsDetector located the majority of MSs found by other widely used tools. In addition, MsDetector identified novel MSs. Furthermore, the system has a very low false-positive rate resulting in a precision of up to 99%. MsDetector is expected to produce consistent results across studies analyzing the same sequence.
