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pmid:29405013
Region-Specific Methylation Profiling in Acute Myeloid Leukemia.
Agnieszka Cecotka · Joanna Polanska
Interdisciplinary sciences, computational life sciences · 2018 doi:10.1007/s12539-018-0285-4
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Experiment
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Analysis
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Complete file
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There are several tools devoted to the problem of identification of CpG sites' demethylation but majority of them focuses on single site level and does not allow for quantification of region methylation changes. The aim was to create an adaptive algorithm supporting detection of differentially methylated CpG sites and genomic regions specific for acute myeloid leukemia. Knowledge on AML methylation fingerprint helps in better understanding the epigenetics of leukemogenesis. Proposed algorithm is data driven and does not use predefined quantification thresholds. Gaussian mixture modeling supports classification of CpG sites to several levels of demethylation. p value integration allows for translation from single site demethylation to the demethylation of gene promoter and body regions. Methylation profiles of healthy controls and AML patients were examined (GEO:GSE63409). The differences in whole genome methylation profiles were observed. The methylation profile differs significantly among genomic regions. The lowest methylation level was observed for promoter regions, while sites from intergenic regions were by average higher methylated. The observed number of AML related down methylated sites has not substantially exceeded the expected number by chance. Intergenic regions were characterized by the highest percentage of AML up methylated sites. Methylation enhancement/diminution is the most frequent for intergenic region while methylation compensation (positive or negative) is specific for promoter regions. Functional analysis performed for AML down methylated or extreme high up methylated genes showed strong connection to the leukemic processes.", "claims": [{"id": "claim-method", "statement": "Gaussian mixture modeling (GMM) of HL distribution across genome was used to identify different demethylation levels.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29405013", "locator": "sections[2] \"Materials and Methods\" paragraph 5", "quote": "Gaussian mixture modeling (GMM) of HL distribution across genome was used to identify different demethylation levels."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data set GSE63409 [8, 9] consists of raw methylation profiles measured by Infinium HumanMethylation450 microarray (Illumina) from five hematopoietic stem cells’ samples from healthy donors (HSC) and 14 samples of CD34 + 38-cells from AML patients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29405013", "locator": "sections[2] \"Materials and Methods\" paragraph 2", "quote": "The data set GSE63409 [8, 9] consists of raw methylation profiles measured by Infinium HumanMethylation450 microarray (Illumina) from five hematopoietic stem cells’ samples from healthy donors (HSC) and 14 samples of CD34 + 38-cells from AML patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:29405013"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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