Project description:ngs2018_04_half_edit-half_edit - Is there some transcriptomic defects in these different PPR KO mutants? - Identification of RNA editing defects in 3 differents KO mutants for E+ PPR. Results will be compared to different predictive methods in order to find out which one is the more accurate. Also looking for other transcriptomic defects in a pure-PPR.
Project description:Pentatricopeptide repeat (PPR) proteins, which are characterized by tandem 30-40 amino acid sequence motifs, constitute a large gene family in plants. These known PPR proteins have been identified to play important roles in organellar RNA metabolism and plant development in Arabidopsis and rice. However, functions of PPR genes in woody species remain still largely unknown. Here, we identified and characterized a total of 626 PPR genes containing PPR motifs in the poplar genome. A comprehensive genome-wide analysis of the poplar PPR gene family was performed, including chromosomal location, phylogenetic relationships, gene duplication. Transcriptomic analyses identified that 154 of the PtrPPR genes were induced by biotic and abiotic treatments, including Marssonina brunnea, salicylic acid (SA), methyl jasmonate (MeJA), wounding, cold and salinity. Quantitative RT-PCR analysis further confirmed the expression profiles of 11 PtrPPR genes under different stresses. Our results contribute to a more comprehensive understanding the roles of PPR proteins and provided an insight for improving the stress tolerance in poplar.
Project description:ngs2019_18_eplus-eplus-search for mitochondrial editing defect in an arabidopsis PPR mutant Annotation, RNA/Small-RNA quantification: editing quantification. The Mito samples were first enriched with mitochondria by a series of multi-speed centrifugations after grinding with mortar at 4°C.
Project description:We provide sequences of maize chlroplast RNAs associated with non-PPR editing factors through immunoprecipitation. The accumulated sequences indicate expansive role in RNA processing and potential associations of OZ1-ORRM1 complexes with the ribosome and less efficiently translated mRNAs.
Project description:In a recent study, we showed that a T-DNA insertional mutation in a mitochondrial PPR protein, POCO1, led to the earlier floral transition (Emami and Kempken 2019). We used RNA-seq analysis to provide an overview of the global transcriptome changes in poco1 mutant during different developmental stages.
Project description:The biomarker development field within molecular medicine remains limited by the methods that are available for building predictive models. We developed an efficient method for conservatively estimating confidence intervals for the cross validation derived prediction errors of biomarker models. This new method was investigated for its ability to improve the capacity of our previously developed method, StaVarSel, for selecting stable biomarkers. Compared with the standard cross validation method StaVarSel markedly improved the estimated generalisable predictive capacity of serum miRNA biomarkers for the detection of disease states that are at increased risk of progressing to oesophageal adenocarcinoma. The incorporation of our new method for conservatively estimating confidence intervals into StaVarSel resulted in the selection of less complex models with increased stability and improved or similar predictive capacities. The methods developed in this study have the potential to improve progress from biomarker discovery to biomarker driven translational research.
Project description:Metazoan genomes encode hundreds of RNA binding proteins (RBPs) but relatively few have well-defined RNA-binding preferences. Current techniques for determining RNA targets, including those involving in vitro selection and RNA co-immunoprecipitation, require significant time and labour investment. Here we introduce RNAcompete, a new method for the systematic analysis of RNA-binding specificities that employs a single binding reaction to determine the relative preferences of RBPs for short RNAs that containing a complete range of k-mers in structured and unstructured RNA contexts. We tested RNAcompete by analyzing nine diverse RBPs (HuR, Vts1, FUSIP1, PTB, U1A, SF2/ASF, SLM2, RBM4, and YB1). RNAcompete identified both expected and previously unknown RNA binding preferences. Using in vitro and in vivo binding data, we demonstrate that preferences for individual 7-mers identified by RNAcompete are a more accurate representation of binding activity than conventional motif models. We anticipate that RNAcompete will be a valuable tool for the large-scale study of RNA-protein interactions. The bound RNA from each RNA binding protein pulldown assay is analyzed on a custom Agilent microarray using a pool RNA control as a reference.
Project description:Metazoan genomes encode hundreds of RNA binding proteins (RBPs) but relatively few have well-defined RNA-binding preferences. Current techniques for determining RNA targets, including those involving in vitro selection and RNA co-immunoprecipitation, require significant time and labour investment. Here we introduce RNAcompete, a new method for the systematic analysis of RNA-binding specificities that employs a single binding reaction to determine the relative preferences of RBPs for short RNAs that containing a complete range of k-mers in structured and unstructured RNA contexts. We tested RNAcompete by analyzing nine diverse RBPs (HuR, Vts1, FUSIP1, PTB, U1A, SF2/ASF, SLM2, RBM4, and YB1). RNAcompete identified both expected and previously unknown RNA binding preferences. Using in vitro and in vivo binding data, we demonstrate that preferences for individual 7-mers identified by RNAcompete are a more accurate representation of binding activity than conventional motif models. We anticipate that RNAcompete will be a valuable tool for the large-scale study of RNA-protein interactions.
Project description:Here we present miR-eCLIP analysis of AGO2 in HEK293 cells to address the small RNA repertoire and uncover their physiological targets. We developed an optimized bioinformatics approach of chimeric read identification to detect chimeras of high confidence, which were useed as an biologically validated input for miRBind, a deep learning method and web-server that can be used to accurately predict the potential of miRNA:target site binding.