DEEPAligner: Deep encoding of pathways to align epigenetic signatures
Affiliation:
1. School of Life Science and Bio-Pharmaceutics, Shenyang Pharmaceutical University, Shenyang, Liaoning Province 110016, PR China;2. School of Medical Devices, Shenyang Pharmaceutical University, Benxi, Liaoning Province 117004, PR China;1. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China;2. State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, China;3. Department of Civil & Environmental Engineering, University of Missouri, Columbia, MO 65211-2200, USA;1. Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544-5263, USA;2. Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544-1000, USA;3. Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ 08544-5263, USA;1. Department of Chemistry, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India;2. Department of Physics, Maulana Azad College, Kolkata, India;3. Indian Institute of Astrophysics, Bangalore 560034, India;1. MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
Abstract:
Background and objectiveRecently, differential DNA Methylation is known to affect the regulatory mechanism of biological pathways. A pathway encompasses a set of interacting genes or gene products that altogether perform a given biological function. Pathways often encode strong methylation signatures that are capable of distinguishing biologically distinct subtypes. Even though Next Generation Sequencing techniques such as MeDIP-seq and MBD-isolated genome sequencing (MiGS) allow for genome-wide identification of clinical and biological subtypes, there is a pressing need for computational methods to compare epigenetic signatures across pathways.MethodsA novel alignment method, called DEEPAligner (Deep Encoded Epigenetic Pathway Aligner), is proposed in this paper that finds functionally consistent and topologically sound alignments of epigenetic signatures from pathway networks. A deep embedding framework is used to obtain epigenetic signatures from pathways which are then aligned for functional consistency and local topological similarity.ResultsExperiments on four benchmark cancer datasets reveal epigenetic signatures that are conserved in cancer-specific and across-cancer subtypes.ConclusionThe proposed deep embedding framework obtains highly coherent signatures that are aligned for biological as well as structural orthology. Comparison with state-of-the-art network alignment methods clearly suggest that the proposed method obtains topologically and functionally more consistent alignments.Availabilityhttp://bdbl.nitc.ac.in/DEEPAligner
