Integrative annotation of chromatin elements from ENCODE data.

Area of Science:

• Genomics
• Epigenetics
• Computational Biology

Background:

• The ENCODE Project provides extensive chromatin data for human cell lines.
• Understanding regulatory elements requires integrating diverse chromatin datasets.

Purpose of the Study:

• To develop methods for integrating ENCODE chromatin data.
• To systematically annotate regulatory elements and chromatin architecture.
• To uncover interrelations within chromatin datasets for a comprehensive genome map.

Main Methods:

• Application of unsupervised learning methodologies.
• Conversion of chromatin datasets into discrete annotation maps.
• Analysis of regulatory regions and chromatin elements across the human genome.

Main Results:

• Successful rediscovery and summarization of chromatin architecture.
• Elucidation of the interplay between chromatin activity and RNA transcription.
• Identification of a large quiescent proportion of the genome across cell types.
• Annotation of non-coding regulatory elements correlating with mammalian evolutionary constraint.

Conclusions:

• Unsupervised learning provides an interpretable summary of massive ENCODE datasets.
• The generated annotations offer an unbiased approach for evaluating evolutionary constraint.
• Regulatory annotations facilitate hypothesis generation for previously uncharacterized disease-associated loci.