Biopython: freely available Python tools for computational molecular biology and bioinformaticsAbstract Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Availability: Biopython is freely available, with documentation and source code at www.biopython.org under the Biopython license. Contact: All queries should be directed to the Biopython mailing lists, see www.biopython.org/wiki/_Mailing_listspeter.cock@scri.ac.uk.
LOSITAN: A workbench to detect molecular adaptation based on a F st -outlier methodTesting 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.
Genomic analyses inform on migration events during the peopling of EurasiaGenetic diversity of the African malaria vector Anopheles gambiaeEffects of Overlapping Generations on Linkage Disequilibrium Estimates of Effective Population SizeUse of single-sample genetic methods to estimate effective population size has skyrocketed in recent years. Although the underlying models assume discrete generations, they are widely applied to age-structured species. We simulated genetic data for 21 iteroparous animal and plant species to evaluate two untested hypotheses regarding performance of the single-sample method based on linkage disequilibrium (LD): (1) estimates based on single-cohort samples reflect the effective number of breeders in one reproductive cycle (Nb), and (2) mixed-age samples reflect the effective size per generation (Ne). We calculated true Ne and Nb, using the model species' vital rates, and verified these with individual-based simulations. We show that single-cohort samples should be equally influenced by Nb and Ne and confirm this with simulated results: [Formula: see text] was a linear (r(2) = 0.98) function of the harmonic mean of Ne and Nb. We provide a quantitative bias correction for raw [Formula: see text] based on the ratio Nb/Ne, which can be estimated from two or three simple life history traits. Bias-adjusted estimates were within 5% of true Nb for all 21 study species and proved robust when challenged with new data. Mixed-age adult samples produced downwardly biased estimates in all species, which we attribute to a two-locus Wahlund effect (mixture LD) caused by combining parents from different cohorts in a single sample. Results from this study will facilitate interpretation of rapidly accumulating genetic estimates in terms of both Ne (which influences long-term evolutionary processes) and Nb (which is more important for understanding eco-evolutionary dynamics and mating systems).