Project description:Human prostate CWR22 OT-tumor cells were prospectively purified for expression of various stem cell markers (TRA-1-60/CD151/CD166/EpCAM/CD44/α2-Integrin). Unsorted total tumor cells or the additional marker positive cells that do not manifest stem-like characteristics were used as control. All these cells were subjected to molecular profiling of total RNA expression and the fold change data are tabulated according to S/TFE of the purified cells in relation to their control. Notes: Low S/TFE = Low sphere and tumor forming efficiency; Moderate S/TFE = Moderate sphere and tumor forming efficiency; High S/TFE = High sphere and tumor forming efficiency; FDR* = False Discovery Rate; FC = Fold Change; Signal = Average Expression Signal Level.
Project description:Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.
2019-12-16 | GSE131267 | GEO
Project description:High-throughput Sequencing Data
Project description:Human prostate CWR22 OT-tumor cells were prospectively purified for expression of various stem cell markers (TRA-1-60/CD151/CD166/EpCAM/CD44/α2-Integrin). Unsorted total tumor cells or the additional marker positive cells that do not manifest stem-like characteristics were used as control. All these cells were subjected to molecular profiling of total RNA expression and the fold change data are tabulated according to S/TFE of the purified cells in relation to their control. Notes: Low S/TFE = Low sphere and tumor forming efficiency; Moderate S/TFE = Moderate sphere and tumor forming efficiency; High S/TFE = High sphere and tumor forming efficiency; FDR* = False Discovery Rate; FC = Fold Change; Signal = Average Expression Signal Level. Low-S/TFE, Moderate S/TFE, High S/TFE data sets were compared to No S/TFE control sets
Project description:Human prostate CWR22 OT-tumor cells were prospectively purified for expression of various stem cell markers (TRA-1-60/CD151/CD166/EpCAM/CD44/α2-Integrin). Unsorted total tumor cells or the additional marker positive cells that do not manifest stem-like characteristics were used as control. All these cells were subjected to molecular profiling of total RNA expression and the fold change data are tabulated according to S/TFE of the purified cells in relation to their control. Notes: Low S/TFE = Low sphere and tumor forming efficiency; Moderate S/TFE = Moderate sphere and tumor forming efficiency; High S/TFE = High sphere and tumor forming efficiency; FDR* = False Discovery Rate; FC = Fold Change; Signal = Average Expression Signal Level. Low-S/TFE, Moderate S/TFE, High S/TFE data sets were compared to No S/TFE control sets
Project description:Enhancers provide critical information directing cell-type specific transcriptional programs, regulated by binding of signal-dependent transcription factors and their associated cofactors. Here we report that the most strongly activated estrogen (E2)-responsive enhancers are characterized by trans-recruitment and in situ assembly of a large 1-2MDa complex of diverse DNA-binding transcription factors by ERα at ERE-containing enhancers. We refer to enhancers recruiting these factors as mega transcription factor-bound in trans (MegaTrans) enhancers. The MegaTrans complex is a signature of the most potent functional enhancers and is required for activation of enhancer RNA transcription and recruitment of coactivators, including p300 and Med1. The MegaTrans complex functions, in part, by recruiting specific enzymatic machinery, exemplified by DNA-dependent protein kinase. Thus, MegaTrans-containing enhancers represent a cohort of functional enhancers that mediate a broad and important transcriptional program and provide a molecular explanation for transcription factor clustering and hotspots noted in the genome. Chromatin Immunoprecipitation (ChIP) assay followed by high throughput sequencing (ChIP-seq)
Project description:To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host’s cell machinery. Promoter libraries of E. coli sigma factor 70 and B. subtilis B-, F- and W-dependent promoters are exploited to construct prediction models, capable of both predicting promoter TIF and orthogonality of the specific promoters. This is achieved by the creation of high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries.
Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.