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Genome scale analysis of DNA methylation patterns in prostate cancer patient derived xenograft models [EPIC Set 2]

ABSTRACT: Genome scale analysis of DNA methylation patterns in prostate cancer patient derived xenograft models [EPIC Set 2]

PROVIDER: PRJNA946668 | ENA |

REPOSITORIES: ENA

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Genome scale analysis of DNA methylation patterns in prostate cancer patient derived xenograft models [EPIC Set 1]

Project description:DNA methylation alterations are a universal feature of cancer. In prostate cancer, site specific DNA methylation changes have been suggested as driver in disease initial and progression. Here we provide a comprehensive assessment of DNA methylation changes in prostate cancer patient derived xenograft (PDX) models. We delineate patterns of both site specific and global methylation changes and nominate novel candidates for biomarker development. Genome wide DNA methylation profiling of prostate cancer patient derived xenograft and cell line models using Infinium EPIC arrays

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Genome scale analysis of DNA methylation patterns in prostate cancer patient derived xenograft models [EPIC Set 2]

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Project description:Genome wide DNA methylation profiling of GBM xenografts. The Illumina EPIC 850k Human DNA methylation array used to obtain DNA methylation profiles across approximately 800,000 CpGs in GBM xenograft samples. Samples included xx GBM xenografts.

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EPIC: Software Toolkit for Elution Profile-Based Inference of Protein Complexes

Project description:Heteromeric protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (CF/MS), but the resulting data can be challenging to process and interpret. Here, we describe EPIC (Elution Profile-based Inference of Complexes), a software toolkit for automated scoring of CF/MS data for large-scale determination of high-confidence physical interaction networks and macromolecular assemblies from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 590 putative worm protein complexes linked to diverse biological processes, including assemblies unique to nematodes. The EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/), and the open source code is available via GitHub (https://github.com/BaderLab/EPIC).

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