LOSITAN: A workbench to detect molecular adaptation based on a F st -outlier method

TLDR

Testing for selection is becoming a key step in multilocus population genetics, yet existing applications are difficult to use and leave many error‑prone tasks to the user. The authors present LOSITAN, a selection‑detection workbench built on a well‑evaluated Fst‑outlier method. LOSITAN accurately approximates model parameters, offers data import/export, contour smoothing, and graphics via a GUI, and exploits multi‑core processors by locally parallelizing fdist, halving runtime on dual‑core machines and scaling linearly with more cores. LOSITAN expands selection detection to a broader user base, enabling large population genomic datasets to be analyzed efficiently through its user‑friendly interface and comprehensive functionality.

Abstract

Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user. Here we present LOSITAN, a selection detection workbench based on a well evaluated F st -outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral F st ), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores. LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.