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    Additional file 2: of Chromosome-scale comparative sequence analysis unravels molecular mechanisms of genome dynamics between two wheat cultivars

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    Type
    Data File
    Authors
    Thind, Anupriya Kaur
    International Wheat Genome Sequencing Consortium
    Wicker, Thomas
    Müller, Thomas
    Ackermann, Patrick M.
    Steuernagel, Burkhard
    Wulff, Brande B. H.
    Spannagl, Manuel
    Twardziok, Sven O.
    Felder, Marius
    Lux, Thomas
    Mayer, Klaus F. X.
    Keller, Beat
    Krattinger, Simon G. cc
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Plant Science
    Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/664151
    
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    Abstract
    Table S1. List of 678 CH Campala Lr22a genes that were found in haploblock c. (XLSX 32 kb)
    Citation
    Anupriya Thind, Wicker, T., MĂźller, T., Ackermann, P., Steuernagel, B., Brande Wulff, Spannagl, M., Twardziok, S., Felder, M., Lux, T., Mayer, K., Keller, B., & Krattinger, S. (2018). Additional file 2: of Chromosome-scale comparative sequence analysis unravels molecular mechanisms of genome dynamics between two wheat cultivars [Data set]. figshare. https://doi.org/10.6084/M9.FIGSHARE.6978275.V1
    Publisher
    figshare
    DOI
    10.6084/m9.figshare.6978275.v1
    Relations
    Is Supplement To:
    • [Article]
      Thind AK, Wicker T, Müller T, Ackermann PM, et al. (2018) Chromosome-scale comparative sequence analysis unravels molecular mechanisms of genome dynamics between two wheat cultivars. Genome Biology 19. Available: http://dx.doi.org/10.1186/s13059-018-1477-2.. DOI: 10.1186/s13059-018-1477-2 HANDLE: 10754/628478
    ae974a485f413a2113503eed53cd6c53
    10.6084/m9.figshare.6978275.v1
    Scopus Count
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    Biological and Environmental Science and Engineering (BESE) Division; Datasets

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      Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data

      Wang, Chunxiang; Gao, Xin; Liu, Juntao (figshare, 2020) [Dataset]
      Abstract Background Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data. Results We designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. Conclusion The SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types.
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