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A directory of tools, AI models, datasets, and research resources for biotech, bioinformatics, and other scientific fields. Aggregated from curated GitHub awesome-lists, HuggingFace, bio.tools, Bioconductor, and more.
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A versatile tool for in silico protease evaluation and selection in a proteomic and proteogenomic context.
A web-based portal to interactively explore clear cell renal cell carcinoma proteogenomics networks. an interactive WebGL visualizer of 3D Networks. NCI supported Clinical Proteomic Tumor Analysis Consortium (CPTAC), is a comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome (proteins) and genome (DNA and RNA) analysis technologies to different cancer types. CPTAC is composed of expertise in proteomics, genomics, cancer biology, oncology, and clinical chemistry, while creating open community resources that are widely used by the cancer community. This page provides visualization of the networks estimated based on proteogenomic data from CPTAC-CCRCC discovery project. For more information, visit the CPTAC-CCRCC data portal. Co-expression network was constructed based on phospho-peptide data of 103 Clear Cell Renal Cell Carcinoma (ccRCC) samples.
An Interactive Multi-Omics Data Browser for Proteogenomic Studies. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data and analysis results from various CPTAC projects, we built a prototype user-friendly web application called “ProTrack” with the CPTAC clear cell renal cell carcinoma (ccRCC) data set ( http: ccrcc.cptac-data-view.org ). Here we describe the salient features of this application which provides a dynamic, comprehensive, and granular visualization of the rich proteogenomic data. Statement of Significance The CPTAC initiative ( https: proteomics.cancer.gov ) has generated multi-omics data for multiple cancer types to understand the proteogenomic aberrations of these malignancies
PANOPLY is a platform for applying state-of-the-art statistical and machine learning algorithms to transform multi-omic data from cancer samples into biologically meaningful and interpretable results.
pypgatk is a Python library part of the ProteoGenomics Analysis Toolkit. It provides different bioinformatics tools for proteogenomics data analysis