NoiseMaker: simulated screens for statistical assessment.
High-throughput screening (HTS) is a common technique for both drug discovery and basic research, but researchers often struggle with how best to derive hits from HTS data. While a wide range of hit identification techniques exist, little information is available about their sensitivity and specificity, especially in comparison to each other. To address this, we have developed the open-source NoiseMaker software tool for generation of realistically noisy virtual screens. By applying potential hit identification methods to NoiseMaker-simulated data and determining how many of the pre-defined true hits are recovered (as well as how many known non-hits are misidentified as hits), one can draw conclusions about the likely performance of these techniques on real data containing unknown true hits. Such simulations apply to a range of screens, such as those using small molecules, siRNAs, shRNAs, miRNA mimics or inhibitors, or gene over-expression; we demonstrate this utility by using it to explain apparently conflicting reports about the performance of the B score hit identification method.


METHODS
NoiseMaker is written in C#, an ECMA and ISO standard language with compilers for multiple operating systems. Source code, a Windows installer and complete unit tests are available at http://sourceforge.net/projects/noisemaker. Full documentation and support are provided via an extensive help file and tool-tips, and the developers welcome user suggestions.
Kwan
Phoenix
P
Thermo Fisher Scientific, 2650 Crescent Drive, Lafayette, CO 80026, USA.
Birmingham
Amanda
A
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D035683:MicroRNAs / Q000737:chemistry; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000737:chemistry; D012984:Software; D014584:User-Computer Interface
D035683:MicroRNAs; D034741:RNA, Small Interfering