CoBRA: Containerized Bioinformatics Workflow for Reproducible Ch IP/ATAC-seq Analysis

Chromatin immunoprecipitation sequencing(Ch IP-seq)and the Assay for Transposase-Accessible Chromatin with high-throughput sequencing(ATAC-seq)have become essential technologies to effectively measure protein–DNA interactions and chromatin accessibility.However,there is a need for a scalable and reproducible pipeline that incorporates proper normalization between samples,correction of copy number variations,and integration of new downstream analysis tools.Here we present Containerized Bioinformatics workflow for Reproducible Ch IP/ATAC-seq Analysis(Co BRA),a modularized computational workflow which quantifies Ch IP-seq and ATAC-seq peak regions and performs unsupervised and supervised analyses.Co BRA provides a comprehensive state-of-the-art Ch IP-seq and ATAC-seq analysis pipeline that can be used by scientists with limited computational experience.This enables researchers to gain rapid insight into protein–DNA interactions and chromatin accessibility through sample clustering,differential peak calling,motif enrichment,comparison of sites to a reference database,and pathway analysis.Co BRA is publicly available online at https://bitbucket.org/cfce/cobra.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Targeted protein degradation(TPD)has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation machinery.However,the susceptibility of proteins for targeting by TPD approaches,termed“degradability”,is largely unknown.Here,we developed a machine learning model,model-free analysis of protein degradability(MAPD),to predict degradability from features intrinsic to protein targets.MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds[with an area under the precision–recall curve(AUPRC)of 0.759 and an area under the receiver operating characteristic curve(AUROC)of 0.775]and is likely generalizable to independent non-kinase proteins.We found five features with statistical significance to achieve optimal prediction,with ubiquitination potential being the most predictive.By structural modeling,we found that E2-accessible ubiquitination sites,but not lysine residues in general,are particularly associated with kinase degradability.Finally,we extended MAPD predictions to the entire proteome to find964 disease-causing proteins(including proteins encoded by 278 cancer genes)that may be tractable to TPD drug development.

CRISPR Screens Identify Essential Cell Growth Mediators in BRAF Inhibitor-resistant Melanoma

BRAF is a serine/threonine kinase that harbors activating mutations in^7%of human malignancies and^60%of melanomas.Despite initial clinical responses to BRAF inhibitors,patients frequently develop drug resistance.To identify candidate therapeutic targets for BRAF inhibitor resistant melanoma,we conduct CRISPR screens in melanoma cells harboring an activating BRAF mutation that had also acquired resistance to BRAF inhibitors.To investigate the mechanisms and pathways enabling resistance to BRAF inhibitors in melanomas,we integrate expression,ATAC-seq,and CRISPR screen data.We identify the JUN family transcription factors and the ETS family transcription factor ETV5 as key regulators of CDK6,which together enable resistance to BRAF inhibitors in melanoma cells.Our findings reveal genes contributing to resistance to a selective BRAF inhibitor PLX4720,providing new insights into gene regulation in BRAF inhibitor resistant melanoma cells.