LOSITAN is a software tool designed to detect molecular adaptation using an Fst-outlier method. It is a user-friendly workbench that simplifies the process of analyzing multilocus population genetics data. LOSITAN provides an intuitive graphical user interface, enabling users to import and export data, generate graphics in various formats, and perform iterative contour smoothing. It also allows for the use of multi-core processors to speed up computations, reducing processing time significantly. The software is based on the fdist program, which is used to calculate Fst values and identify outlier loci. LOSITAN improves upon fdist by automating parameter tuning, approximating neutral Fst values, and providing confidence intervals. It also includes a method to adjust the average Fst value to match real data, even when theoretical assumptions do not hold. Additionally, LOSITAN offers iterative smoothing of confidence interval contours and supports multiple CPU cores for parallel processing. LOSITAN is designed to be accessible to a wide range of users, including those with limited computational experience. It addresses common challenges in Fst-outlier analysis, such as determining neutral Fst values and avoiding human errors in data processing. Despite its advantages, LOSITAN does not resolve fundamental issues with Fst-outlier methods, such as the nonlinear behavior of Fst values. The software is available as a Java Web Start application, allowing direct execution from the web. It is platform-independent and requires a browser with JavaWebStart support. LOSITAN is open-source and licensed under the GNU GPL, with code available for GenePop and fdist file formats. LOSITAN represents an example of an "embarrassingly simple parallel" computation approach, leveraging multi-core hardware to enhance performance. It is part of a growing trend in bioinformatics to utilize multi-core computing for more efficient data analysis. Future developments include adding other F-outlier methods and simulation facilities to explore demographic scenarios. The software is supported by various funding sources and is widely used in population genetics research.LOSITAN is a software tool designed to detect molecular adaptation using an Fst-outlier method. It is a user-friendly workbench that simplifies the process of analyzing multilocus population genetics data. LOSITAN provides an intuitive graphical user interface, enabling users to import and export data, generate graphics in various formats, and perform iterative contour smoothing. It also allows for the use of multi-core processors to speed up computations, reducing processing time significantly. The software is based on the fdist program, which is used to calculate Fst values and identify outlier loci. LOSITAN improves upon fdist by automating parameter tuning, approximating neutral Fst values, and providing confidence intervals. It also includes a method to adjust the average Fst value to match real data, even when theoretical assumptions do not hold. Additionally, LOSITAN offers iterative smoothing of confidence interval contours and supports multiple CPU cores for parallel processing. LOSITAN is designed to be accessible to a wide range of users, including those with limited computational experience. It addresses common challenges in Fst-outlier analysis, such as determining neutral Fst values and avoiding human errors in data processing. Despite its advantages, LOSITAN does not resolve fundamental issues with Fst-outlier methods, such as the nonlinear behavior of Fst values. The software is available as a Java Web Start application, allowing direct execution from the web. It is platform-independent and requires a browser with JavaWebStart support. LOSITAN is open-source and licensed under the GNU GPL, with code available for GenePop and fdist file formats. LOSITAN represents an example of an "embarrassingly simple parallel" computation approach, leveraging multi-core hardware to enhance performance. It is part of a growing trend in bioinformatics to utilize multi-core computing for more efficient data analysis. Future developments include adding other F-outlier methods and simulation facilities to explore demographic scenarios. The software is supported by various funding sources and is widely used in population genetics research.