| Autor: |
Rachid Zaim, Samir, Pebworth, Mark-Phillip, McGrath, Imran, Okada, Lauren, Weiss, Morgan, Reading, Julian, Czartoski, Julie L., Torgerson, Troy R., McElrath, M. Juliana, Bumol, Thomas F., Skene, Peter J., Li, Xiao-jun |
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| Zdroj: |
Nature Communications; 8/9/2024, Vol. 15 Issue 1, p1-24, 24p |
| Abstrakt: |
Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data. Analytical gaps limit the utility of scATAC-seq for studying gene regulatory programs in human disease. Here, authors describe MOCHA, a robust analytical tool with advanced statistical modelling that enables functional genomic inference in large cross-sectional and longitudinal human studies. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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