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Nucleic Acids Res. 2017 Sep 19;45(16):9244-9259. doi: 10.1093/nar/gkx618.

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis.

Author information

1
Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain.
2
Institut de Biologia Evolutiva, Consejo Superior de Investigaciones Científicas-Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, 08003, Spain.
3
Barcelona Supercomputing Centre (BSC), Barcelona, 08034, Spain.
4
Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Department of Anatomic Pathology, Pharmacology and Microbiology, University of Barcelona, Barcelona, 08036, Spain.
5
Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK.
6
ICREA, Pg. Lluís Companys 23, Barcelona, 08010, Spain.

Abstract

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet.

PMID:
28934481
PMCID:
PMC5716146
DOI:
10.1093/nar/gkx618
[Indexed for MEDLINE]
Free PMC Article

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