Project description:To verify the Ribo-seq data of mouse oocyte, we performed MS/MS on mouse fully-grown oocytes. And the results show that our Ribo-seq data well reflect the proteomic dynamics in the fully grown oocytes.
Project description:The effect of Myc activation on the proteome was investigated in U2OS cells and proteome changes were combined with Ribo-seq, RNA-seq and GRO-seq analyses.
Project description:We performed RNA-seq and Ribo-seq analyses to elucidate the translation in seeds at 85 and 115 DAF. We also completed a data-independent acquisition (DIA)-based proteomic analysis, while also examining relevant lipid metabolites.
Project description:Identifying smORFs and SEPs is technically and computationally challenging. Experimentally, techniques as ribosome profiling (Ribo-Seq and mass spectroscopy (MS) are used. Ribo-Seq sequences the mRNA and does not provide the translated frame, thus identifying proteins encoded by overlapping ORFs is not feasible. Herein we have used MS to characterize smORFomes of different Mycoplasma species. This data is used to corroborate the predictions of a random forest classifier that in silico predicts all the putative SEPs encoded by different bacterial genomes.
Project description:Identifying smORFs and SEPs is technically and computationally challenging. Experimentally, techniques as ribosome profiling (Ribo-Seq and mass spectroscopy (MS) are used. Ribo-Seq sequences the mRNA and does not provide the translated frame, thus identifying proteins encoded by overlapping ORFs is not feasible. Herein we have used MS to characterize smORFomes of different Mycoplasma species and Escherichia coli. This data is used to corroborate the predictions of a random forest classifier that in silico predicts all the putative SEPs encoded by different bacterial genomes.
Project description:Identifying smORFs and SEPs is technically and computationally challenging. Experimentally, techniques as ribosome profiling (Ribo-Seq and mass spectroscopy (MS) are used. Ribo-Seq sequences the mRNA and does not provide the translated frame, thus identifying proteins encoded by overlapping ORFs is not feasible. Herein we have used MS to characterize smORFomes of different Mycoplasma species, Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa. This data is used to corroborate the predictions of a random forest classifier that in silico predicts all the putative SEPs encoded by different bacterial genomes.
