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Epigenetic domains found in mouse embryonic stem cells via a hidden Markov model.

Larson JL, Yuan GC - BMC Bioinformatics (2010)

Bottom Line: Recent studies have shown significant epigenetic patterns associated with developmental stages and diseases.We found that each type of domain is associated with distinct biological functions and structural changes during early cell differentiation.The HMM approach successfully detects domains of consistent epigenetic patterns from ChIP-seq data, providing new insights into the role of epigenetics in long-range gene regulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.

ABSTRACT

Background: Epigenetics is an important layer of transcriptional control necessary for cell-type specific gene regulation. Recent studies have shown significant epigenetic patterns associated with developmental stages and diseases. However, previous studies have been mostly limited to focal epigenetic patterns, whereas methods for analyzing large-scale organizations are still lacking.

Results: We developed a hidden Markov model (HMM) approach for detecting the types and locations of epigenetic domains from multiple histone modifications. We used this method to analyze a published ChIP-seq dataset of five histone modification marks (H3K4me2, H3K4me3, H3K27me3, H3K9me3, and H3K36me3) in mouse embryonic stem (ES) cells. We identified three types of domains, corresponding to active, non-active, and states. In total, our three-state HMM identified 258 domains in the mouse genome containing 9.6 genes on average. These domains were validated by a number of criteria. The largest domains correspond to olfactory receptor (OR) gene clusters. Each Hox gene cluster also forms a separate epigenetic domain. We found that each type of domain is associated with distinct biological functions and structural changes during early cell differentiation.

Conclusions: The HMM approach successfully detects domains of consistent epigenetic patterns from ChIP-seq data, providing new insights into the role of epigenetics in long-range gene regulation.

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Average within-domain variance of a modification versus a random distribution. Two-state model (K = 2) results (and corresponding permutation test p-values) are shown in red, three-state in blue (K = 3), random distribution shown in black. For four of the modifications, the HMM domains have a significantly lower average variance (H3K4me2, H3K4me3 & H3K27me3: p-values < 0.001), suggesting that the HMM has produced coherent domain bounds.
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Figure 4: Average within-domain variance of a modification versus a random distribution. Two-state model (K = 2) results (and corresponding permutation test p-values) are shown in red, three-state in blue (K = 3), random distribution shown in black. For four of the modifications, the HMM domains have a significantly lower average variance (H3K4me2, H3K4me3 & H3K27me3: p-values < 0.001), suggesting that the HMM has produced coherent domain bounds.

Mentions: First, we tested whether the histone modification patterns were indeed consistent within each predicted significant domain. To this end, we calculated the within-domain variance of the summary score for each modification in our significant domains, and tested whether this is significantly lower than expected by chance. Using permutation tests, we found that both the two- and three-state HMMs had significantly lower variances in the histone modifications considered in this study (p-values < 0.05), with the exception of H3K9me3 (Figure 4). This suggests that H3K9me3 does not play a major role in determining our domain states.


Epigenetic domains found in mouse embryonic stem cells via a hidden Markov model.

Larson JL, Yuan GC - BMC Bioinformatics (2010)

Average within-domain variance of a modification versus a random distribution. Two-state model (K = 2) results (and corresponding permutation test p-values) are shown in red, three-state in blue (K = 3), random distribution shown in black. For four of the modifications, the HMM domains have a significantly lower average variance (H3K4me2, H3K4me3 & H3K27me3: p-values < 0.001), suggesting that the HMM has produced coherent domain bounds.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2992069&req=5

Figure 4: Average within-domain variance of a modification versus a random distribution. Two-state model (K = 2) results (and corresponding permutation test p-values) are shown in red, three-state in blue (K = 3), random distribution shown in black. For four of the modifications, the HMM domains have a significantly lower average variance (H3K4me2, H3K4me3 & H3K27me3: p-values < 0.001), suggesting that the HMM has produced coherent domain bounds.
Mentions: First, we tested whether the histone modification patterns were indeed consistent within each predicted significant domain. To this end, we calculated the within-domain variance of the summary score for each modification in our significant domains, and tested whether this is significantly lower than expected by chance. Using permutation tests, we found that both the two- and three-state HMMs had significantly lower variances in the histone modifications considered in this study (p-values < 0.05), with the exception of H3K9me3 (Figure 4). This suggests that H3K9me3 does not play a major role in determining our domain states.

Bottom Line: Recent studies have shown significant epigenetic patterns associated with developmental stages and diseases.We found that each type of domain is associated with distinct biological functions and structural changes during early cell differentiation.The HMM approach successfully detects domains of consistent epigenetic patterns from ChIP-seq data, providing new insights into the role of epigenetics in long-range gene regulation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.

ABSTRACT

Background: Epigenetics is an important layer of transcriptional control necessary for cell-type specific gene regulation. Recent studies have shown significant epigenetic patterns associated with developmental stages and diseases. However, previous studies have been mostly limited to focal epigenetic patterns, whereas methods for analyzing large-scale organizations are still lacking.

Results: We developed a hidden Markov model (HMM) approach for detecting the types and locations of epigenetic domains from multiple histone modifications. We used this method to analyze a published ChIP-seq dataset of five histone modification marks (H3K4me2, H3K4me3, H3K27me3, H3K9me3, and H3K36me3) in mouse embryonic stem (ES) cells. We identified three types of domains, corresponding to active, non-active, and states. In total, our three-state HMM identified 258 domains in the mouse genome containing 9.6 genes on average. These domains were validated by a number of criteria. The largest domains correspond to olfactory receptor (OR) gene clusters. Each Hox gene cluster also forms a separate epigenetic domain. We found that each type of domain is associated with distinct biological functions and structural changes during early cell differentiation.

Conclusions: The HMM approach successfully detects domains of consistent epigenetic patterns from ChIP-seq data, providing new insights into the role of epigenetics in long-range gene regulation.

Show MeSH