@misc{PaijmansBarlowHennebergeretal.2020, author = {Paijmans, Johanna L. A. and Barlow, Axel and Henneberger, Kirstin and Fickel, J{\"o}rns and Hofreiter, Michael and Foerste, Daniel W. G.}, title = {Ancestral mitogenome capture of the Southeast Asian banded linsang}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {972}, issn = {1866-8372}, doi = {10.25932/publishup-47444}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-474441}, pages = {14}, year = {2020}, abstract = {Utilising a reconstructed ancestral mitochondrial genome of a clade to design hybridisation capture baits can provide the opportunity for recovering mitochondrial sequences from all its descendent and even sister lineages. This approach is useful for taxa with no extant close relatives, as is often the case for rare or extinct species, and is a viable approach for the analysis of historical museum specimens. Asiatic linsangs (genus Prionodon) exemplify this situation, being rare Southeast Asian carnivores for which little molecular data is available. Using ancestral capture we recover partial mitochondrial genome sequences for seven banded linsangs (P. linsang) from historical specimens, representing the first intraspecific genetic dataset for this species. We additionally assemble a high quality mitogenome for the banded linsang using shotgun sequencing for time-calibrated phylogenetic analysis. This reveals a deep divergence between the two Asiatic linsang species (P. linsang, P. pardicolor), with an estimated divergence of ~12 million years (Ma). Although our sample size precludes any robust interpretation of the population structure of the banded linsang, we recover two distinct matrilines with an estimated tMRCA of ~1 Ma. Our results can be used as a basis for further investigation of the Asiatic linsangs, and further demonstrate the utility of ancestral capture for studying divergent taxa without close relatives.}, language = {en} } @misc{BarlowHartmannGonzalezetal.2020, author = {Barlow, Axel and Hartmann, Stefanie and Gonzalez, Javier and Hofreiter, Michael and Paijmans, Johanna L. A.}, title = {Consensify}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1033}, issn = {1866-8372}, doi = {10.25932/publishup-47252}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472521}, pages = {24}, year = {2020}, abstract = {A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes.}, language = {en} } @misc{TaronLellBarlowetal.2018, author = {Taron, Ulrike H. and Lell, Moritz and Barlow, Axel and Paijmans, Johanna L. A.}, title = {Testing of Alignment Parameters for Ancient Samples}, series = {Genes}, journal = {Genes}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-409683}, pages = {12}, year = {2018}, abstract = {High-throughput sequence data retrieved from ancient or other degraded samples has led to unprecedented insights into the evolutionary history of many species, but the analysis of such sequences also poses specific computational challenges. The most commonly used approach involves mapping sequence reads to a reference genome. However, this process becomes increasingly challenging with an elevated genetic distance between target and reference or with the presence of contaminant sequences with high sequence similarity to the target species. The evaluation and testing of mapping efficiency and stringency are thus paramount for the reliable identification and analysis of ancient sequences. In this paper, we present 'TAPAS', (Testing of Alignment Parameters for Ancient Samples), a computational tool that enables the systematic testing of mapping tools for ancient data by simulating sequence data reflecting the properties of an ancient dataset and performing test runs using the mapping software and parameter settings of interest. We showcase TAPAS by using it to assess and improve mapping strategy for a degraded sample from a banded linsang (Prionodon linsang), for which no closely related reference is currently available. This enables a 1.8-fold increase of the number of mapped reads without sacrificing mapping specificity. The increase of mapped reads effectively reduces the need for additional sequencing, thus making more economical use of time, resources, and sample material.}, language = {en} }