Project description:This SuperSeries is composed of the following subset Series: GSE28948: TMPRSS2-ERG, HDACs and EZH2 are involved in an AR-centric transcriptional circuitry that calibrates androgenic response for prostate cancer progression (gene expression data) GSE28950: TMPRSS2-ERG, HDACs and EZH2 are involved in an AR-centric transcriptional circuitry that calibrates androgenic response for prostate cancer progression (ChIP-Seq data) GSE35540: TMPRSS2-ERG, HDACs and EZH2 are involved in an AR centric transcriptional circuitry that calibrates androgenic response for prostate cancer progression (gene expression after ERG KD) Refer to individual Series
Project description:Identify the best combination of predictive variables that influence ionizing radiation dose and improved image quality through analysis and quantification of PET-CT images in simulators and patients.
Project description:With an ability to compromise genome integrity, transposable elements (TEs) have significant associations with human diseases. Short-read sequencing has been used to study the expression of TEs; however, the highly repetitive nature of these elements makes multimapping a critical issue. Here we implement LocusMasterTE, an improved quantification method by integrating long-read sequencing. Introducing computed transcript per million(TPM) counts from long-read sequencing as prior distribution during Expectation-Maximization(EM) model in short-read TE quantification, multi-mapped reads are re-assigned to correct expression values. Based on simulated short reads, LocusMasterTE outperforms current quantitative approaches and is significantly favorable in capturing newly inserted TEs. We also verified that TEs quantified by LocusMasterTE clearly related to euchromatins and heterochromatins in cell line samples. With LocusMasterTE we anticipate that more accurate quantification can be performed, allowing novel functions of TEs to be uncovered.
Project description:With an ability to compromise genome integrity, transposable elements (TEs) have significant associations with human diseases. Short-read sequencing has been used to study the expression of TEs; however, the highly repetitive nature of these elements makes multimapping a critical issue. Here we implement LocusMasterTE, an improved quantification method by integrating long-read sequencing. Introducing computed transcript per million(TPM) counts from long-read sequencing as prior distribution during Expectation-Maximization(EM) model in short-read TE quantification, multi-mapped reads are re-assigned to correct expression values. Based on simulated short reads, LocusMasterTE outperforms current quantitative approaches and is significantly favorable in capturing newly inserted TEs. We also verified that TEs quantified by LocusMasterTE clearly related to euchromatins and heterochromatins in cell line samples. With LocusMasterTE we anticipate that more accurate quantification can be performed, allowing novel functions of TEs to be uncovered.
Project description:In this study, we used a surrogate lentivirus library to capture CRISPR editing outcome in HEK cells. The dataset include quantification of indel frequencies for SpCas9 gRNAs in 12,000 surrogate sites. After filtering low quality sites, the high quality SpCas9 gRNA activities from a total of 10592 sites have been used to develop an improved deep learning-based prediction model CRISPRon (https://rth.dk/resources/crispr/.).
Project description:Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally-efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package. Benchmarking samples to compare MBD- and MeDIP-seq [GSE38679, GSE24546; PMID 21045081] datasets against 450k measurements
Project description:Identifying cellular phosphorylation pathways based on kinase-substrate relationships is a critical step to understanding the regulation of physiological functions in cells. Mass spectrometry-based phosphoproteomics workflows have made it possible to comprehensively collect information on individual phosphorylation sites in a variety of samples. However, there is still no generic approach to uncover phosphorylation networks based on kinase-substrate relationships in rare cell populations. Here, we describe a motif-centric phosphoproteomics approach combined with multiplexed isobaric labeling, in which in vitro kinase reaction is used to generate the targeted phosphopeptides, which are spiked into one of the isobaric channels to increase detectability. Proof-of-concept experiments demonstrate selective and comprehensive quantification of targeted phosphopeptides by using multiple kinases for motif-centric channels. Over 7,000 tyrosine phosphorylation sites were quantified from several tens of µg of starting materials. This approach enables the quantification of multiple phosphorylation pathways under physiological or pathological regulation in a motif-centric manner.
Project description:Increasingly, biochemical co-fractionation-based approaches are used to study interactomes and protein complexes at high throughput. The devised methods facilitate the qualitative assignment and prediction of hundreds of putative cellular assemblies in one experiment and without dependency on genetic engineering to introduce affinity tags. The present dataset consists of a native proteome extracted by mild lysis from the HEK293 cell line and fractionated into 80 fractions along high resolution size exclusion chromatography, and each fraction analyzed via bottom-up SWATH mass spectrometry. In multi-tiered targeted analysis, from this fragment ion level chromatographic data, quantitative complex assembly information of the proteome is reconstructed in three steps, (i) peptide-centric detection of peptide analytes within each SEC fraction based on fragment ion co-elution groups; (ii) protein-centric detection of protein elution in SEC based on fragment peptide co-elution groups in SEC; (iii) complex-centric detection and quantification of protein complexes and -variants based on component subunit protein co-elution groups in SEC. The data delineate a global picture of quantitative complex formation within a human proteome, including deconvolution of novel subversions and assembly intermediates of critical cellular complexes with essential functions.
Project description:Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally-efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package.