Predicting gene expression level by the transcription factor binding signals in human embryonic stem cells

Zhang, Lu-Qiang; Li, Qian‐Zhong; Su, Wei; Jin, Wen

doi:10.1016/j.biosystems.2016.08.011

Cited by 13 publications

(8 citation statements)

References 36 publications

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“…In order to further investigate the effects of TFs and HMs on prediction for genes in independent biological processes, we focus on the Gene Ontology biological processes [32, 33] for the high expression genes in the three cell lines (based on RPKM values, the top fifteen percent of all genes are selected as high expressed genes [3, 23]). Firstly, biological processes containing less than 30 genes are discarded, 1104, 1136 and 1070 sets of genes are remained, respectively, for H1-hESc, Gm12878 and K562 cell line.…”

Section: Resultsmentioning

confidence: 99%

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Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells

Zhang

2017

Oncotarget

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Transcription factors and histone modifications are vital for the regulation of gene expression. Hence, to estimate the effects of transcription factors binding and histone modifications on gene expression, we construct a statistical model for the genome-wide 15 transcription factors binding data, 10 histone modifications profiles and DNase-I hypersensitivity data in three mammalian. Remarkably, our results show POLR2A and H3K36me3 can highly and consistently predict gene expression in three cell lines. And H3K4me3, H3K27me3 and H3K9ac are more reliable predictors than other histone modifications in human embryonic stem cells. Moreover, genome-wide statistical redundancies exist within and between transcription factors and histone modifications, and these phenomena may be caused by the regulation mechanism. In further study, we find that even though transcription factors and histone modifications offer similar effects on expression levels of genome-wide genes, the effects of transcription factors and histone modifications on predictive abilities are different for genes in independent biological processes.

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Section: Resultsmentioning

confidence: 99%

“…Based on our previous study [3], signals of TFs binding are normalized by using the following Eq. (1),

N_{i j}^{k} = (n_{i, j}^{k} \times 10^{9}) / (n_{t a g}^{k} \times 200)

(1)…”

Section: Methodsmentioning

confidence: 99%

Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells

Zhang

2017

Oncotarget

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“…From a computational point of view, machine learning methods are attractive in terms of their ability to derive predictive models without a need for strong assumptions about underlying mechanisms; hence they are especially useful to deal with certain biological questions of which our a priori knowledge is frequently unknown or insufficiently defined 14 . As a proof of concept, gene expression levels can be accurately predicted from a broad set of epigenetic features 16–20 or binding profiles of diverse transcription factors (TFs) 21–24 using various machine-learning-based approaches, although our knowledge about how the selected features determine the expression output is largely unknown. Modeling is, therefore, a key ingredient to derive novel biological insights by integrating large-scale data sets.…”

Section: Introductionmentioning

confidence: 99%

The HTPmod Shiny application enables modeling and visualization of large-scale biological data

Chen

et al. 2018

Commun Biol

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The wave of high-throughput technologies in genomics and phenomics are enabling data to be generated on an unprecedented scale and at a reasonable cost. Exploring the large-scale data sets generated by these technologies to derive biological insights requires efficient bioinformatic tools. Here we introduce an interactive, open-source web application (HTPmod) for high-throughput biological data modeling and visualization. HTPmod is implemented with the Shiny framework by integrating the computational power and professional visualization of R and including various machine-learning approaches. We demonstrate that HTPmod can be used for modeling and visualizing large-scale, high-dimensional data sets (such as multiple omics data) under a broad context. By reinvestigating example data sets from recent studies, we find not only that HTPmod can reproduce results from the original studies in a straightforward fashion and within a reasonable time, but also that novel insights may be gained from fast reinvestigation of existing data by HTPmod.

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“…In other words, highly correlated features often contain redundant information [8] . For example, whereas the dozens of pluripotent factors such as Oct4, Sox2, Klf4, and c-Myc, are all useful to predict genes expressed in stem cells [9] , [10] , [11] , combining some pluripotent factors with endothelial lineage factors such as Lmo2 and Erg would add power to also predict genes expressed in endothelial cells; therefore, it can be more powerful using combined information from transcription factors with distinct functions, as opposed to an analysis using the transcription factors with similar effects on a shared set of target genes. More importantly, colocalization of low-correlation chromatin features may still happen in a biologically meaningful manner to implement important functions.…”

Section: Introductionmentioning

confidence: 99%

MACMIC Reveals a Dual Role of CTCF in Epigenetic Regulation of Cell Identity Genes

Wang

Xia

Zhou³

et al. 2021

Genomics, Proteomics &Amp; Bioinformatics

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Numerous studies of relationship between epigenomic features have focused on their strong correlation across the genome, likely because such relationship can be easily identified by many established methods for correlation analysis. However, two features with little correlation may still colocalize at many genomic sites to implement important functions. There is no bioinformatic tool for researchers to specifically identify such feature pairs. Here, we develop a method to identify feature pairs in which two features have maximal colocalization minimal correlation (MACMIC) across the genome. By MACMIC analysis of 3306 feature pairs in 16 human cell types, we reveal a dual role of CCCTC-binding factor (CTCF) in epigenetic regulation of cell identity genes. Although super-enhancers are associated with activation of target genes, only a subset of super-enhancers colocalized with CTCF regulate cell identity genes. At super-enhancers colocalized with CTCF, CTCF is required for the active marker H3K27ac in cell types requiring the activation, and also required for the repressive marker H3K27me3 in other cell types requiring repression. Our work demonstrates the biological utility of the MACMIC analysis and reveals a key role for CTCF in epigenetic regulation of cell identity. The code for MACMIC is available at https://github.com/bxia888/MACMIC .

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Predicting gene expression level by the transcription factor binding signals in human embryonic stem cells

Cited by 13 publications

References 36 publications

Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells

Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells

The HTPmod Shiny application enables modeling and visualization of large-scale biological data

MACMIC Reveals a Dual Role of CTCF in Epigenetic Regulation of Cell Identity Genes

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