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Sökning: WFRF:(Leeb Tosso) > (2015-2019) > (2017)

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1.
  • Wallner, Barbara, et al. (författare)
  • Y Chromosome Uncovers the Recent Oriental Origin of Modern Stallions
  • 2017
  • Ingår i: Current Biology. - : CELL PRESS. - 0960-9822 .- 1879-0445. ; 27:13, s. 2029-2035
  • Tidskriftsartikel (refereegranskat)abstract
    • The Y chromosome directly reflects male genealogies, but the extremely low Y chromosome sequence diversity in horses has prevented the reconstruction of stallion genealogies [1, 2]. Here, weresolve the first Y chromosomegenealogy of modern horses by screening 1.46 Mb of the male-specific region of the Y chromosome (MSY) in 52 horses from 21 breeds. Based on highly accurate pedigree data, we estimated the de novo mutation rate of the horse MSY and showed that various modern horse Y chromosome lineages split much later than the domestication of the species. Apart from few private northern European haplotypes, all modern horse breeds clustered together in a roughly 700-year-old haplogroup that was transmitted to Europe by the import of Oriental stallions. The Oriental horse group consisted of two major subclades: the Original Arabian lineage and the Turkoman horse lineage. We show that the English Thoroughbred MSY was derived from the Turkoman lineage and that English Thoroughbred sires are largely responsible for the predominance of this haplotype in modern horses.
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2.
  • Wucher, Valentin, et al. (författare)
  • FEELnc : a tool for long non-coding RNA annotation and its application to the dog transcriptome
  • 2017
  • Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 45:8
  • Tidskriftsartikel (refereegranskat)abstract
    • Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. One of the main bottlenecks, however, is to correctly identify the different classes of RNAs among the plethora of reconstructed transcripts, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program that accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-the-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE data sets. The program also provides specific modules that enable the user to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to identify lncRNAs even in the absence of a training set of non-coding RNAs. We used FEELnc on a real data set comprising 20 canine RNA-seq samples produced by the European LUPA consortium to substantially expand the canine genome annotation to include 10 374 novel lncRNAs and 58 640 mRNA transcripts. FEELnc moves beyond conventional coding potential classifiers by providing a standardized and complete solution for annotating lncRNAs and is freely available at https://github.com/tderrien/FEELnc.
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