0d441e8a-7142-4429-8a71-8b8a8a06909d - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001006896 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Initial whole genome sequencing of plasma cell neoplasms in First Responders exposed to the World Trade Center attack of September 11, 2001
| EGAS00001004467 | EGA
Project description:We performed shallow whole genome sequencing (WGS) on circulating free (cf)DNA extracted from plasma or cerebrospinal fluid (CSF), and shallow WGS on the tissue DNA extracted from the biopsy in order to evaluate the correlation between the two biomaterials. After library construction and sequencing (Hiseq3000 or Ion Proton), copy number variations were called with WisecondorX.
2021-11-17 | E-MTAB-10548 | biostudies-arrayexpress
Project description:Chromosomal copy number variations (CNV) have been associated with various neurological and developmental disorders and chromosomal microarray (CMA) is a method of choice to diagnose Copy Number Gain/Loss syndromes. Recently, next-generation sequencing (NGS)-based low-coverage whole genome sequencing (LC-WGS) has been applied to detect Copy Number Gain/Loss syndromes. This dataset is intended to be used as a “Golden standard data set” for development of LC-WGS analysis method. It consists of patients (n=63) who have a mental delay and/or physical disability phenotype and normal (n=20) phenotype.
2018-09-29 | GSE120624 | GEO
Project description:Whole genome sequencing (WGS) of tongue cancer samples and cell line was performed to identify the fusion gene translocation breakpoint. WGS raw data was aligned to human reference genome (GRCh38.p12) using BWA-MEM (v0.7.17). The BAM files generated were further analysed using SvABA (v1.1.3) tool to identify translocation breakpoints. The translocation breakpoints were annotated using custom scripts, using the reference GENCODE GTF (v30). The fusion breakpoints identified in the SvABA analysis were additionally confirmed using MANTA tool (v1.6.0).
2024-02-17 | E-MTAB-12534 | biostudies-arrayexpress
Project description:Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers have been identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and has already been studied using RNA-seq. In this study a massive (325,025 cells and 49 patients) single cell multiomic dataset was generated with jointly quantified ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identified an association between one plasma cell subtype with myeloma progression that we have called relapsed/refractory plasma cells (RRPCs). These cells are associated with 1q alterations, TP53 mutations, and higher expression of PHF19. We also identified downstream regulation of cell cycle inhibitors in these cells, possible regulation of the transcription factor (TF) PBX1 on 1q, and determined that PHF19 may be acting primarily through this subset of cells.
2024-01-31 | GSE254307 | GEO
Project description:Lack of a standard method for stratifying advanced-stage NSCLC patients receiving platinum combination therapy often results in a number of patients that do not derive benefit yet are still exposed to treatment toxicity. We hypothesized that miRNAs in pre-treatment serum and/or plasma could be used to differentiate non-small cell lung cancer (NSCLC) patients who would have disease progression to first-line carboplatin and gemcitabine chemotherapy at first response assessment. miRNA profiling of mature and precursor miRNAs was performed on total RNA isolated from the pre-treatment serum and plasma of 24 NSCLC patients. Single validated candidates or combinations thereof were selected based on specificity and sensitivity to segregate patients with disease progression at first radiologic response (PD) vs. those without progressed disease (nonPD). Two precursor miRNA were significantly over-expressed in serum (but not plasma) of PD patients: pre-miR-518b and pre-miR-598. Serum miRNAs may serve as a screening tool in predicting chemoresistance to platinum-based combination chemotherapy.
2012-12-01 | GSE25019 | GEO
Project description:Background: Clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC) can usually be distinguished by histologic characteristics. Occasionally, diagnosis proves challenging and diagnostic difficulty will likely increase as needle biopsies of renal lesions become more common. Method: To identify markers that aid in differentiating ccRCC from chRCC, we used gene expression profiles to identify candidate markers that correlate with histology. 39 antisera and antibodies, including 35 for transcripts identified from gene expression profiling, were evaluated. Promising markers were tested on a tissue microarray (TMA) containing 428 renal neoplasms. Strength of staining of each core on the TMA was formally scored and the distribution of staining across different types of renal neoplasms was analyzed. Results: Based on results from initial immunohistochemical staining of multitissue titer arrays, 23 of the antisera and antibodies were selected for staining of the TMA. For 7 of these markers, strength of staining of each core on the TMA was formally scored. Vimentin (positive in ccRCC) and CD9 (positive in chRCC) best distinguished ccRCC from chRCC. The combination of vimentin negativity and CD9 positivity was found to distinguish chRCC from ccRCC with a sensitivity of 100.0% and a specificity of 95.2%. Conclusions: Based on gene expression analysis, we identify CD9 and vimentin as candidate markers for distinguishing between ccRCC and chRCC. In difficult cases and particularly when the amount of diagnostic tissue is limited, vimentin and CD9 staining could serve as a useful adjunct in the differential diagnosis of ccRCC and chRCC. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: Stage of Clear Cell renal cell carcinoma (I - V)) disease_state_design
2010-08-20 | E-GEOD-17746 | biostudies-arrayexpress
Project description:We evaluated the responses of attack phase Bdellvobrio bacteriovorus HD100 when they are exposed to S. aureus biofilm.
2018-08-01 | GSE109687 | GEO
Project description:Background: Clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC) can usually be distinguished by histologic characteristics. Occasionally, diagnosis proves challenging and diagnostic difficulty will likely increase as needle biopsies of renal lesions become more common. Method: To identify markers that aid in differentiating ccRCC from chRCC, we used gene expression profiles to identify candidate markers that correlate with histology. 39 antisera and antibodies, including 35 for transcripts identified from gene expression profiling, were evaluated. Promising markers were tested on a tissue microarray (TMA) containing 428 renal neoplasms. Strength of staining of each core on the TMA was formally scored and the distribution of staining across different types of renal neoplasms was analyzed. Results: Based on results from initial immunohistochemical staining of multitissue titer arrays, 23 of the antisera and antibodies were selected for staining of the TMA. For 7 of these markers, strength of staining of each core on the TMA was formally scored. Vimentin (positive in ccRCC) and CD9 (positive in chRCC) best distinguished ccRCC from chRCC. The combination of vimentin negativity and CD9 positivity was found to distinguish chRCC from ccRCC with a sensitivity of 100.0% and a specificity of 95.2%. Conclusions: Based on gene expression analysis, we identify CD9 and vimentin as candidate markers for distinguishing between ccRCC and chRCC. In difficult cases and particularly when the amount of diagnostic tissue is limited, vimentin and CD9 staining could serve as a useful adjunct in the differential diagnosis of ccRCC and chRCC. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: Stage of Clear Cell renal cell carcinoma (I - V))
2010-08-20 | GSE17746 | GEO
Project description:Previously, we published a dataset of human blood plasma and serum samples of 10 healthy males and 10 healthy females, fractionated on a set of sorbents (cation exchange Toyopearl CM-650M, CM Bio-Gel A, SP Sephadex C-25 and anion exchange QAE Sephadex A-25) and analyzed by LC-MS/MS individually and pooled in equal amounts (Supplementary Table S1, Sheet 1) [33]. The mass spectrometry peptidomics data was deposited to the ProteomeXchange Consortium via the PRIDE partner repository (dataset identifiers PXD008141 and 10.6019/PXD008141). Direct download link: http://www.ebi.ac.uk/pride/archive/projects/PXD008141. We analyzed this dataset again within this work. The detailed information about the dataset of blood plasma/serum samples of 20 healthy donors fractionated on a set of sorbents is available in the original paper [33], including the clinical parameters of the donors, sample collection, plasma/serum fractionation, peptide extraction and LC-MS/MS analysis. 33. Arapidi, G. et al. Peptidomics dataset: Blood plasma and serum samples of healthy donors fractionated on a set of chromatography sorbents. Data Brief 18, 1204–1211 (2018).
2024-06-14 | PXD027587 | Pride