Project description:We investigated whether mouse serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. Submitter states "We have no processed data to submit. We have no gpr files to submit."
Project description:This experiment was conducted to generate targeted resequencing data covering a region associated with osteosarcoma in greyhounds. 8 greyhounds diagnosed with osteosarcoma and 7 greyhounds without tumors were sequenced. DNA from the 15 dogs was used to prepare libraries and hybrid capture performed to enrich the region of interest prior to paired-end sequencing using Illumina Genome Analyzer II. The reads were aligned to the dog-genome CanFam2.0 using bwa and pre-processed using Picard and GATK. Variant discovery was performed using GATK. The resulting list of variants were used in the study to finemap the associated region and look for causal variants. We submit the preprocessed BAM-files that still have all reads although some reads are flagged. We also submit the resulting vcf-file with called and filtered variants in all individuals.
Project description:We performed a systematic study of 141 mammalian RhoGAPs and GEFs including their interactome, specificity and localisation. Here we submit the data from the interactome screen that were processed using MaxQuant.
Project description:In addition to determining possible diagnostic and predictive peptides of lupus and CNS-lupus, we also used our microarray technology along with the Guitope computer program to determine possible natural protein match to five monoclonal autoantibodies that were created using one of the autoimmune MRL/lpr mouse. Submitter states "We have no processed data to submit. We have no gpr files to submit."
Project description:In this project we cloned 141 mammalian RhoGAPs/GEFs (112 human, 26 mouse, 2 rat, 1 chimpanzee) and performed a systematic study of their interactome, localisation and specificity. Here we submit the mass spectrometry data from the interactome screen.
Project description:DNA methylation has been shown to play a major role in determining cellular phenotype by regulating gene expression. Moreover, dysregulation of differentially methylated genes has been implicated in disease pathogenesis of various conditions including cancer development as well as autoimmune diseases such as systemic Lupus erythematosus and rheumatoid arthritis. Evidence is rapidly accumulating for a role of DNA methylation in regulating immune responses in health and disease. However, the exact mechanisms remain unknown. The overall aim of the project is to investigate the role of epigenetic mechanisms in regulating immunity and their impact on autoimmune disease pathogenesis.The aim of this pilot study is to perform whole genome methylation analysis in peripheral blood mononuclear cells (PBMCs) and cell subsets (CD4, CD8, CD14, CD19, CD16 and whole PBMCs) obtained from 6 healthy volunteers. Whole genome methylation analysis will be performed using two methodological approaches, the Infinium Methylation Bead Array K450 (Illumina) and MeDIP-seq. mRNA expression arrays will also be performed in order to correlate DNA methylation with gene expression as well as genotyping on the Illumina OmniExpress chip
Project description:Ovarian cancer is the leading cause of death in gynaecological malignancies in women. However, currently there are no clinical or pathologic parameters available that can reliably predict clinical outcome. In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict disease stage with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs). In the current study we prospectively evaluate models, built on the pilot data set, on a set of 49 new patients. Principal component analysis showed that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the prospective study did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study, only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours but this model was not able to predict resistance to platin-based chemotherapy. We discuss possible reasons for prospective failure of these models and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before this technology could be considered ready for clinical use.