Additional file 2: of Chromosome-scale comparative sequence analysis unravels molecular mechanisms of genome dynamics between two wheat cultivars
AuthorsThind, Anupriya Kaur
International Wheat Genome Sequencing Consortium
Ackermann, Patrick M.
Wulff, Brande B. H.
Twardziok, Sven O.
Mayer, Klaus F. X.
Krattinger, Simon G.
Permanent link to this recordhttp://hdl.handle.net/10754/664151
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AbstractTable S1. List of 678 CH Campala Lr22a genes that were found in haploblock c. (XLSX 32 kb)
CitationAnupriya 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
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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
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Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq dataWang, 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.
Supplementary Material for: Global expression differences and tissue specific expression differences in rice evolution result in two contrasting types of differentially expressed genesHoriuchi, Youko; Harushima, Yoshiaki; Fujisawa, Hironori; Mochizuki, Takako; Fujita, Masahiro; Ohyanagi, Hajime; Kurata, Nori (figshare, 2015) [Dataset]Abstract Background Since the development of transcriptome analysis systems, many expression evolution studies characterized evolutionary forces acting on gene expression, without explicit discrimination between global expression differences and tissue specific expression differences. However, different types of gene expression alteration should have different effects on an organism, the evolutionary forces that act on them might be different, and different types of genes might show different types of differential expression between species. To confirm this, we studied differentially expressed (DE) genes among closely related groups that have extensive gene expression atlases, and clarified characteristics of different types of DE genes including the identification of regulating loci for differential expression using expression quantitative loci (eQTL) analysis data. Results We detected differentially expressed (DE) genes between rice subspecies in five homologous tissues that were verified using japonica and indica transcriptome atlases in public databases. Using the transcriptome atlases, we classified DE genes into two types, global DE genes and changed-tissues DE genes. Global type DE genes were not expressed in any tissues in the atlas of one subspecies, however changed-tissues type DE genes were expressed in both subspecies with different tissue specificity. For the five tissues in the two japonica-indica combinations, 4.6 ± 0.8 and 5.9 ± 1.5 % of highly expressed genes were global and changed-tissues DE genes, respectively. Changed-tissues DE genes varied in number between tissues, increasing linearly with the abundance of tissue specifically expressed genes in the tissue. Molecular evolution of global DE genes was rapid, unlike that of changed-tissues DE genes. Based on gene ontology, global and changed-tissues DE genes were different, having no common GO terms. Expression differences of most global DE genes were regulated by cis-eQTLs. Expression evolution of changed-tissues DE genes was rapid in tissue specifically expressed genes and those rapidly evolved changed-tissues DE genes were regulated not by cis-eQTLs, but by complicated trans-eQTLs. Conclusions Global DE genes and changed-tissues DE genes had contrasting characteristics. The two contrasting types of DE genes provide possible explanations for the previous controversial conclusions about the relationships between molecular evolution and expression evolution of genes in different species, and the relationship between expression breadth and expression conservation in evolution.