A maximum-likelihood approach for building cell-type trees by lifting. (January 2016)

• Record Type: Journal Article
• Authors: Nair, Nishanth; Hunter, Laura; Shao, Mingfu; Grnarova, Paulina; Lin, Yu; Bucher, Philipp; E. Moret, Bernard
• Is Part Of: BMC genomics. Volume 17:Number 1(2016)
• Page Start: 33
• Page End: 40
• Publication Date: 2016-01
• Subjects: Cell-type trees -- Histone modifications -- Epigenomics -- Phylogeny -- Evolution -- Cell-differentiation; Genomes -- Periodicals; Gene mapping -- Periodicals; Genomics -- Periodicals; Base Sequence -- Periodicals; Chromosome Mapping -- Periodicals; Genetic Techniques -- Periodicals; Sequence Analysis, DNA -- Periodicals; 572.8605
• DOI: 10.1186/s12864-015-2297-3
• Languages: English
• ISSNs: 1471-2164

Abstract

Abstract Background In cell differentiation, a less specialized cell differentiates into a more specialized one, even though all cells in one organism have (almost) the same genome. Epigenetic factors such as histone modifications are known to play a significant role in cell differentiation. We previously introduce cell-type trees to represent the differentiation of cells into more specialized types, a representation that partakes of both ontogeny and phylogeny. Results We propose a maximum-likelihood (ML) approach to build cell-type trees and show that this ML approach outperforms our earlier distance-based and parsimony-based approaches. We then study the reconstruction of ancestral cell types; since both ancestral and derived cell types can coexist in adult organisms, we propose a lifting algorithm to infer internal nodes. We present results on our lifting algorithm obtained both through simulations and on real datasets. Conclusions We show that our ML-based approach outperforms previously proposed techniques such as distance-based and parsimony-based methods. We show our lifting-based approach works well on both simulated and real data.