Here we developed a novel analysis framework named MACE (model-based analysis of ChIP-exo) dedicated to ChIP-exo data analysis. MACExo has the following four steps: 1) sequencing data normalization and bias correction; 2) signal consolidation and noise reduction; 3) single nucleotide resolution border detection using Chebyshev Inequality; and 4) border matching using Gale-Shapley’s stable matching algorithm. When applied to yeast Reb1 and human CTCF ChIP-exo data, MACE is able to define TFBSs with higher sensitivity, specificity and spatial resolution, as evidenced by multiple criteria, such as motif enrichment, sequence conservation, nucleosome positioning, and open chromatin states.

Project Activity

See All Activity >

Categories

Data Analytics

Follow chipexo

chipexo Web Site

Other Useful Business Software
Gemini 3 and 200+ AI Models on One Platform Icon
Gemini 3 and 200+ AI Models on One Platform

Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.

Build generative AI apps with Vertex AI. Switch between models without switching platforms.
Start Free
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of chipexo!

Additional Project Details

Registered

2013-06-20