fCCAC

fCCAC performs functional canonical correlation analysis on nucleic acid sequencing datasets to assess shared covariance and variability across ChIP-seq and other genomic data.

Key Features:

• Functional Canonical Correlation Analysis: fCCAC employs functional canonical correlation analysis to assess covariance and identify shared patterns of variation between nucleic acid sequencing datasets.
• ChIP-seq data support: fCCAC is tailored to handle chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) data for comparison of biological and technical replicates and reproducibility evaluation.
• Shared covariance detection: fCCAC uncovers shared covariance between genomic features such as histone modifications and DNA-binding proteins, including relationships involving the H3K4me3 mark and its epigenetic writers and readers.
• Reproducibility and variability assessment: fCCAC evaluates variability across DNA and RNA sequencing datasets to support reproducibility assessment.
• Comparative correlation analysis: fCCAC facilitates comparison of multiple datasets to identify and quantify correlations among complex genomic interactions.

Scientific Applications:

• Reproducibility assessment: fCCAC aids assessment of experimental reproducibility by quantifying covariance and variability across sequencing datasets.
• Correlation analysis: fCCAC enables detection and quantification of correlations between genomic datasets and features.
• Epigenetic research: fCCAC supports studies of chromatin marks and protein–DNA interactions, including analysis of histone modifications such as H3K4me3 and associated writers and readers.

Methodology:

fCCAC applies functional canonical correlation analysis to nucleic acid sequencing datasets to assess covariance and shared patterns of variation and to compare biological and technical replicates, particularly for ChIP-seq data.