Project description:This SuperSeries is composed of the following subset Series: GSE17162: Structural and Functional Analysis of Viral siRNAs using Solexa sequencing GSE17164: Structural and Functional Analysis of Viral siRNAs using 454 sequencing Refer to individual Series
Project description:Supporting microarray data for manuscript entitled "OSTEOPONTIN AND PAI-1 EXPRESSION IN MALIGNANT HYPERTENSION: SUPPRESSION BY p38 MAPK INHIBITORS" submitted to the HYPERTENSION journal. Keywords: timecourse, diet
Project description:Quantitative proteomic analysis raw data for the manuscript entitled “A covalent peptide-based lysosome-targeting protein degradation platform for cancer immunotherapy”.
Project description:This PXD project contains two projects published on ProteomicsDB (https://www.proteomicsDB.org) as integral part of the publication. The first project entitled 'human body map' (https://www.proteomicsdb.org/#projects/42) involves the analysis of 36 different human tissues and body fluids. The second project entitled 'Cellzome adopted' includes a collection of raw files which comprises identifications of 'missing proteins'.
Project description:Whole-genome tiling arrays were used to validate deletions and tandem duplications that were inferred based on next-generation sequencing data. The arrays were generated for six samples of the Drosophila melanogaster Genetic Reference Panel (DGRP) as well as the Berkeley reference strain. Structural variations (SVs) were assessed by comparing probe intensities within the region of interest between the sample for which the SV was predicted and the reference strain.
Project description:Proteomics of HEPG2 cells following FTO overexpression and knockdown. Data accompany our paper entitled “Dynamic Regulation of N6,2′-O-dimethyladenosine (m6Am) in Obesity” scheduled for publication in Nature Communications, 2021
Project description:Proteomics of liver tissue from ob/ob and WT mice. Data accompany our paper entitled “Dynamic Regulation of N6,2′-O-dimethyladenosine (m6Am) in Obesity” scheduled for publication in Nature Communications, 2021
Project description:Background. In a previous publication we introduced a novel approach to identify genes that hold predictive information about treatment outcome. A linear regression model was fitted by using the least angle regression algorithm (LARS) with the expression profiles of a construction set of 18 glioma progenitor cells enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed predicting therapy-induced impairment of proliferation in vitro. Prediction performance was validated in leave one out cross validation. Methods. In this study, we used an additional validation set of 18 serum-free short-term treated in vitro cell cultures to test the predictive properties of the signature in an independent cohort. We assessed proliferation rates together with transcriptome-wide expression profiles after Sunitinib treatment of each individual cell culture, following the methods of the previous publication. Results. We confirmed treatment-induced expression changes in our validation set, but our signature failed to predict proliferation inhibition. Conclusion. Although the gene signature published from our construction set exhibited good prediction accuracy in cross validation, we were not able to validate the signature in an independent validation data set. Reasons could be regression to the mean, the moderate numbers of samples, or too low differences in the response to proliferation inhibition. At this stage and based on the presented results, we conclude that the signature does not warrant further developmental steps towards clinical application.