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  • 1
    Online Resource
    Online Resource
    Estimating Allele Age and Selection Coefficient from Time-Serial Data
    Oxford University Press (OUP) ; 2012
    In:  Genetics Vol. 192, No. 2 ( 2012-10-01), p. 599-607
    Details
    In: Genetics, Oxford University Press (OUP), Vol. 192, No. 2 ( 2012-10-01), p. 599-607
    Abstract: Recent advances in sequencing technologies have made available an ever-increasing amount of ancient genomic data. In particular, it is now possible to target specific single nucleotide polymorphisms in several samples at different time points. Such time-series data are also available in the context of experimental or viral evolution. Time-series data should allow for a more precise inference of population genetic parameters and to test hypotheses about the recent action of natural selection. In this manuscript, we develop a likelihood method to jointly estimate the selection coefficient and the age of an allele from time-serial data. Our method can be used for allele frequencies sampled from a single diallelic locus. The transition probabilities are calculated by approximating the standard diffusion equation of the Wright–Fisher model with a one-step process. We show that our method produces unbiased estimates. The accuracy of the method is tested via simulations. Finally, the utility of the method is illustrated with an application to several loci encoding coat color in horses, a pattern that has previously been linked with domestication. Importantly, given our ability to estimate the age of the allele, it is possible to gain traction on the important problem of distinguishing selection on new mutations from selection on standing variation. In this coat color example for instance, we estimate the age of this allele, which is found to predate domestication.
    Type of Medium: Online Resource
    ISSN: 1943-2631
    URL: Article
    DOI: 10.1534/genetics.112.140939
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2012
    detail.hit.zdb_id: 1477228-0
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Mapache: a flexible pipeline to map ancient DNA
    Oxford University Press (OUP) ; 2023
    In:  Bioinformatics Vol. 39, No. 2 ( 2023-02-03)
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 39, No. 2 ( 2023-02-03)
    Abstract: We introduce mapache, a flexible, robust and scalable pipeline to map, quantify and impute ancient and present-day DNA in a reproducible way. Mapache is implemented in the workflow manager Snakemake and is optimized for low-space consumption, allowing to efficiently (re)map large datasets—such as reference panels and multiple extracts and libraries per sample — to one or several genomes. Mapache can easily be customized or combined with other Snakemake tools. Availability and implementation Mapache is freely available on GitHub (https://github.com/sneuensc/mapache). An extensive manual is provided at https://github.com/sneuensc/mapache/wiki. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btad028
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1468345-3
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  • 3
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    Online Resource
    Inference of natural selection from ancient DNA
    Oxford University Press (OUP) ; 2020
    In:  Evolution Letters Vol. 4, No. 2 ( 2020-04-01), p. 94-108
    Details
    In: Evolution Letters, Oxford University Press (OUP), Vol. 4, No. 2 ( 2020-04-01), p. 94-108
    Abstract: Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods.
    Type of Medium: Online Resource
    ISSN: 2056-3744
    URL: Article
    DOI: 10.1002/evl3.165
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2894293-0
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  • 4
    Online Resource
    Online Resource
    bammds: a tool for assessing the ancestry of low-depth whole-genome data using multidimensional scaling (MDS)
    Oxford University Press (OUP) ; 2014
    In:  Bioinformatics Vol. 30, No. 20 ( 2014-10-15), p. 2962-2964
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 30, No. 20 ( 2014-10-15), p. 2962-2964
    Abstract: Summary: We present bammds , a practical tool that allows visualization of samples sequenced by second-generation sequencing when compared with a reference panel of individuals (usually genotypes) using a multidimensional scaling algorithm. Our tool is aimed at determining the ancestry of unknown samples—typical of ancient DNA data—particularly when only low amounts of data are available for those samples. Availability and implementation: The software package is available under GNU General Public License v3 and is freely available together with test datasets https://savannah.nongnu.org/projects/bammds/ . It is using R ( http://www.r-project.org/ ), parallel ( http://www.gnu.org/software/parallel/ ), samtools ( https://github.com/samtools/samtools ). Contact:  bammds-users@nongnu.org Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btu410
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2014
    detail.hit.zdb_id: 1468345-3
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  • 5
    Online Resource
    Online Resource
    A likelihood method for estimating present-day human contamination in ancient male samples using low-depth X-chromosome data
    Oxford University Press (OUP) ; 2020
    In:  Bioinformatics Vol. 36, No. 3 ( 2020-02-01), p. 828-841
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 36, No. 3 ( 2020-02-01), p. 828-841
    Abstract: The presence of present-day human contaminating DNA fragments is one of the challenges defining ancient DNA (aDNA) research. This is especially relevant to the ancient human DNA field where it is difficult to distinguish endogenous molecules from human contaminants due to their genetic similarity. Recently, with the advent of high-throughput sequencing and new aDNA protocols, hundreds of ancient human genomes have become available. Contamination in those genomes has been measured with computational methods often developed specifically for these empirical studies. Consequently, some of these methods have not been implemented and tested for general use while few are aimed at low-depth nuclear data, a common feature in aDNA datasets. Results We develop a new X-chromosome-based maximum likelihood method for estimating present-day human contamination in low-depth sequencing data from male individuals. We implement our method for general use, assess its performance under conditions typical of ancient human DNA research, and compare it to previous nuclear data-based methods through extensive simulations. For low-depth data, we show that existing methods can produce unusable estimates or substantially underestimate contamination. In contrast, our method provides accurate estimates for a depth of coverage as low as 0.5× on the X-chromosome when contamination is below 25%. Moreover, our method still yields meaningful estimates in very challenging situations, i.e. when the contaminant and the target come from closely related populations or with increased error rates. With a running time below 5 min, our method is applicable to large scale aDNA genomic studies. Availability and implementation The method is implemented in C++ and R and is available in github.com/sapfo/contaminationX and popgen.dk/angsd.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btz660
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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