High performance computation of landscape genomic models integrating local indices of spatial association

Motivation: The increasing availability of high-throughput datasets requires powerful methods to support the detection of signatures of selection in landscape genomics. Results: We present an integrated approach to study signatures of local adaptation, providing rapid processing of whole genome data and enabling assessment of spatial association using molecular markers. Availabilty: Sam{\ss}ada is an open source software written in C++ available at http:lasig.epfl.ch/sambada (under the license GNU GPL 3). Compiled versions are provided for Windows, Linux and MacOS X.


Published in:
arXiv Quantitative Biology, [q-bio.PE], arXiv:1405.7658v1
Year:
2014
Laboratories:




 Record created 2014-05-30, last modified 2018-09-13

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