TY - JOUR
T1 - LOSITAN
T2 - A workbench to detect molecular adaptation based on a Fst-outlier method
AU - Antao, Tiago
AU - Lopes, Ana
AU - Lopes, Ricardo J.
AU - Beja-Pereira, Albano
AU - Luikart, Gordon
N1 - Funding Information:
TA was supported by research grant SFRH/BD/30834/2006, RJL by SFRH/ BPD/14953/2004 and AB-P by SFRH/BPD/17822/2004 and this work was supported by POCI/CVT/567558/2004 all from Fundacao para a Ciencia e Tecnologia (FCT), Portugal. GL was supported by the Luso-American Foundation, UP, CIBIO and research grant PTDC/BIA-BDE/65625/2006 from FCT.
Funding Information:
This work was partially supported by the Bill & Melinda Gates Foundation (grant #39777).
PY - 2008/7/28
Y1 - 2008/7/28
N2 - Background: 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. Results: Here we present LOSITAN, a selection detection workbench based on a well evaluated Fst-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral Fst), 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. Conclusion: 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.
AB - Background: 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. Results: Here we present LOSITAN, a selection detection workbench based on a well evaluated Fst-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral Fst), 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. Conclusion: 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.
UR - http://www.scopus.com/inward/record.url?scp=49649129741&partnerID=8YFLogxK
U2 - 10.1186/1471-2105-9-323
DO - 10.1186/1471-2105-9-323
M3 - Article
C2 - 18662398
AN - SCOPUS:49649129741
SN - 1471-2105
VL - 9
JO - BMC Bioinformatics
JF - BMC Bioinformatics
M1 - 323
ER -