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Bonin, A., Bellemain, E., Bronken Eidesen, P., Pompanon, F., Brochmann, C., Taberlet, P. 2004. How to track and assess genotyping errors in population genenics studies Molecular Ecology (Invited Review) 13, 3261-3273.
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Bellemain, E., Swenson, J.E., Tallmom, D.A., Brunberg, S., Taberlet, P. 2005. Estimating population size of elusive animals using DNA from hunter-collected feces: comparing four methods for brown bears. Conservation Biology, 19(1), 150-161
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Bellemain, E., Zedrosser, A., Manel, S., Taberlet, P., Waits, L.P., Swenson, J.E. 2006. The dilemma of female mate selection in the brown bear, a species with sexually selected infanticide. Proceedings of the Royal Society of London, Series B 273, 283-291
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Solberg, H., Bellemain, E., Drageset, O.M., Taberlet, P., Swenson, J.E. 2006. An evaluation of field and genetic methods to estimate brown bear (Ursus arctos) population size. Biological conservation 128, 158-168.
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Bellemain, E., Nawaz, A., Valentini, A., Swenson, J.E, Taberlet, P. 2007. Genetic tracking of the brown bear in northern Pakistan and implications for conservation. Biological Conservation 134, 537-547.
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Manel, S., Berthoud, F., Bellemain, E. Gaudeul, M., Swenson, J.E., Luikart, G., Waits, L.P. Intrabiodiv consortium, Taberlet, P. 2007. A new individual-based geographic approach for identifying genetic discontinuities. Molecular Ecology 16(10): 2031-2043.
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Valentini, A., Miquel, C., Nawaz, M.A., Bellemain, E., Coissac, E., Pompanon, F., Nascetti, G. et al. 2008. New perspectives in diet analysis based on DNA barcoding and parallel pyrosequencing: the trnL approach. Molecular Ecology, 2009, 9, 51-60
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Pages, M., Maudet, C., Bellemain, E., Taberlet, P., Hugues, S., Hänni, C. 2009. Sex your Ursid free. Conservation Genetics. DOI 10.1007/s10592-008-9650-x
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Bellemain, E., Carlsen, T., Coissac, E., Taberlet, P., Brochmann, C., Kauserud, H. 2010. ITS as a DNA barcode in fungi: An in silico approach reveals potential PCR biases. BMC Microbiology. 10:189
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Bonin, A., Bellemain, E., Bronken Eidesen, P., Pompanon, F., Brochmann, C., Taberlet, P. 2004. How to track and assess genotyping errors in population genenics studies Molecular Ecology (Invited Review) 13, 3261-3273.
Genotyping errors occur when the genotype determined after molecular analysis does not correspond to the real genotype of the individual under consideration. Virtually every genetic data set includes some erroneous genotypes, but genotyping errors remain a taboo subject in population genetics, even though they might greatly bias the final conclusions,
especially for studies based on individual identification. Here, we consider four case studies representing a large variety of population genetics investigations differing in their sampling strategies (noninvasive or traditional), in the type of organism studied (plant or animal) and the molecular markers used [microsatellites or amplified fragment length
polymorphisms (AFLPs)]. In these data sets, the estimated genotyping error rate ranges from 0.8% for microsatellite loci from bear tissues to 2.6% for AFLP loci from dwarf birch leaves. Main sources of errors were allelic dropouts for microsatellites and differences in peak intensities for AFLPs, but in both cases human factors were non-negligible error generators. Therefore, tracking genotyping errors and identifying their causes are necessary
to clean up the data sets and validate the final results according to the precision required. In addition, we propose the outline of a protocol designed to limit and quantify genotyping errors at each step of the genotyping process. In particular, we recommend (i) several efficient precautions to prevent contaminations and technical artefacts; (ii) systematic use of blind samples and automation; (iii) experience and rigor for laboratory work and scoring; and (iv) systematic reporting of the error rate in population genetics studies.
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