Supplementary Material for: Global expression differences and tissue specific expression differences in rice evolution result in two contrasting types of differentially expressed genes
Permanent link to this recordhttp://hdl.handle.net/10754/624141
MetadataShow full item record
AbstractAbstract 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.
CitationHoriuchi, Y., Harushima, Y., Fujisawa, H., Mochizuki, T., Fujita, M., Ohyanagi, H., & Kurata, N. (2015). Global expression differences and tissue specific expression differences in rice evolution result in two contrasting types of differentially expressed genes. Figshare. https://doi.org/10.6084/m9.figshare.c.3643793
RelationsIs Supplement To:
Showing items related by title, author, creator and subject.
Additional file 4: of Silica diatom shells tailored with Au nanoparticles enable sensitive analysis of molecules for biological, safety and environment applicationsOnesto, V.; Villani, M.; Coluccio, M. L.; Majewska, R.; Alabastri, A.; Battista, E.; Schirato, A.; Calestani, D.; Coppedé, N.; Cesarelli, M.; Amato, F.; Di Fabrizio, Enzo M.; Gentile, F. (figshare, 2018) [Data File]Supporting figures to the Numerical Simulation Methods of the main text. (DOCX 608Â kb)
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.
CRISPR-Cas13d mediates robust RNA virus interference in plantsMahas, Ahmed; Aman, Rashid; Mahfouz, Magdy M. (figshare, 2019) [Dataset]Abstract Background CRISPR-Cas systems endow bacterial and archaeal species with adaptive immunity mechanisms to fend off invading phages and foreign genetic elements. CRISPR-Cas9 has been harnessed to confer virus interference against DNA viruses in eukaryotes, including plants. In addition, CRISPR-Cas13 systems have been used to target RNA viruses and the transcriptome in mammalian and plant cells. Recently, CRISPR-Cas13a has been shown to confer modest interference against RNA viruses. Here, we characterized a set of different Cas13 variants to identify those with the most efficient, robust, and specific interference activities against RNA viruses in planta using Nicotiana benthamiana. Results Our data show that LwaCas13a, PspCas13b, and CasRx variants mediate high interference activities against RNA viruses in transient assays. Moreover, CasRx mediated robust interference in both transient and stable overexpression assays when compared to the other variants tested. CasRx targets either one virus alone or two RNA viruses simultaneously, with robust interference efficiencies. In addition, CasRx exhibits strong specificity against the target virus and does not exhibit collateral activity in planta. Conclusions Our data establish CasRx as the most robust Cas13 variant for RNA virus interference applications in planta and demonstrate its suitability for studying key questions relating to virus biology.