Evidence map›Paper›PMID 24884369›Full record

ArticleBMC genomics2014

Regression hidden Markov modeling reveals heterogeneous gene expression regulation: a case study in mouse embryonic stem cells.

Yeonok Lee, Debashis Ghosh, Yu Zhang

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 42% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Yeonok Lee
Debashis GhoshDepartment of Statistics, Penn State University, University Park, PA 16802, USA. ghoshd@psu.edu.
Yu Zhang
Pennsylvania State University · US

Funding

Penn State Clinical and Translational Science InstituteUL1TR000127 · NCATS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI SINOWAY, LAWRENCE I · 2012 to 2015
$17.1M
Statistical Methods for Cancer BiomarkersR01CA129102 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GHOSH, DEBASHIS, TAYLOR, JEREMY M.G. · 2009 to 2021
$3.3M
New Bayesian algorithms for genome-wide association mappingR01HG004718 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI ZHANG, YU · 2008 to 2014
$964k
NCATS NIH HHS NIH UL1TR000127NCATS NIH HHS UL1 TR000127NCI NIH HHS R01 CA129102NHGRI NIH HHS R01 HG004718
6 · The paper itself

Abstract

backgroundStudies have shown the strong association between histone modification levels and gene expression levels. The detailed relationships between the two can vary substantially due to differential regulation, and hence a simple regression model may not be adequate. We apply a regression hidden Markov model (regHMM) to further investigate the potential multiple relationships between genes and histone methylation levels in mouse embryonic stem cells.

resultsSeven histone methylation levels are used in the study. Averaged histone modifications over non-overlapping 200 bp windows on the range transcription starting site (TSS) ± 1 Kb are used as predictors, and in total 70 explanatory variables are generated. Based on regHMM results, genes segregated into two groups, referred to as State 1 and State 2, have distinct association strengths. Genes in State 1 are better explained by histone methylation levels with R(2)=.72 while those in State 2 have weaker association strength with R(2)=.38. The regression coefficients in the two states are not very different in magnitude except in the intercept,.25 and 1.15 for State 1 and State 2, respectively. We found specific GO categories that may be attributed to the different relationships. The GO categories more frequently observed in State 2 match those of housekeeping genes, such as cytoplasm, nucleus, and protein binding. In addition, the housekeeping gene expression levels are significantly less explained by histone methylation in mouse embryonic stem cells, which is consistent with the constitutive expression patterns that would be expected.

conclusionGene expression levels are not universally affected by histone methylation levels, and the relationships between the two differ by the gene functions. The expression levels of the genes that perform the most common housekeeping genes' GO categories are less strongly associated with histone methylation levels. We suspect that additional biological factors may also be strongly associated with the gene expression levels in State 2. We discover that the effect of the presence of CpG island in TSS ± 1 Kb is larger in State 2.

Indexed as

Gene Expression RegulationModels, StatisticalAlgorithmsAnimalsCluster AnalysisComputational BiologyCpG IslandsDNA MethylationEmbryonic Stem CellsGene Expression ProfilingGenes, EssentialHistonesMethylationMiceMolecular Sequence AnnotationOrgan SpecificityHistones

Identifiers

PMID24884369
PMCPMC4144088
OpenAlexW1968158793

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.