Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells

Cheng, Chao; Gerstein, Mark

doi:10.1093/nar/gkr752

Cited by 118 publications

(146 citation statements)

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“…These findings indicate that as a general rule, most differences in RNA levels between cell types are accounted for by transcription. This is in agreement with earlier studies showing that gene expression levels can be accurately predicted using only chromatin modifications 36,43 or in addition based on transcriptionfactor binding data 44,45 . For tissue-specific miRNAs, expression is anti-correlated with mRNA expression (corresponding to ∆exon) of the cognate target genes 46,47 .…”

Section: Application Of Eisa To Different Cell Lines and Tissuessupporting

confidence: 93%

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

et al. 2015

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A n A ly s i s RNA-seq experiments generate reads derived not only from mature RNA transcripts but also from pre-mRNA. Here we present a computational approach called exon-intron split analysis (EISA) that measures changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. We apply EISA to 17 diverse data sets to show that most intronic reads arise from nuclear RNA and changes in intronic read counts accurately predict changes in transcriptional activity. Furthermore, changes in posttranscriptional regulation can be predicted from differences between exonic and intronic changes. EISA reveals both transcriptional and post-transcriptional contributions to expression changes, increasing the amount of information that can be gained from RNA-seq data sets.Cellular RNAs are regulated at multiple stages, including transcription, RNA maturation and degradation. Several analytic methods have been developed to measure these processes on a transcriptomewide scale. For example, global run-on sequencing (GRO-seq) 1 uses incorporation of a nucleotide analog to enrich for nascent RNA. In Nascent-seq 2,3 , newly transcribed RNAs are isolated by purification of their complex with proteins and the DNA template. Cellular fractionation techniques 4 have also been adapted to measure nascent transcripts, which are enriched in the nucleus. mRNA half-lives have been determined, for example, by blockage of transcription followed by transcriptional profiling 5 . RNA sequencing, the most widely used method for transcriptome analysis, has been applied in numerous studies to determine steady-state mRNA levels 6,7 and alternative splicing events 8 and to identify previously unknown transcripts and noncoding RNAs 9-11 . In general, these protocols aim to enrich for mature mRNA by selection of polyadenylated RNA or by depletion of ribosomal RNA.Many computational methods (reviewed in refs. 12,13) have been developed for the analysis of RNA-seq data, to enable spliced alignment 14,15 , transcript assembly 16,17 , transcript quantification 14,18 and differential expression analysis [19][20][21] . Although RNA-seq mostly generates reads that map to exons, it also captures less abundant intronic sequences 6 . However, their interpretation has remained controversial. Some have suggested that they originate from DNA contamination and can thus be used as a quality metric for RNA-seq data 22 (see also RNA-seq guidelines of the Roadmap Epigenomics Consortium, http://www.roadmapepigenomics.org/), whereas others have hypothesized that they stem from unknown exons or intronic enhancers 6,7 . In a study based on exon arrays, probes mapping to introns were used to investigate pre-mRNA dynamics 23 . Three recent studies based on RNA-seq provided evidence that intronic reads might correlate with transcriptional activity. In two of these, the read coverage along introns was related to nascent transcription in combination with co-transcriptional splicing e...

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Section: Application Of Eisa To Different Cell Lines and Tissuessupporting

confidence: 93%

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

et al. 2015

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“…We find significant enrichment for some GO categories, e.g., involvement in cell cycle control (Supplemental Table S6). In addition, TSSs whose expression levels are underestimated by the TF model ( y > y _ ) tend to have higher expression variance across different cell lines.We have previously shown that the histone-modification model for gene expression prediction is tissue specific (Cheng and Gerstein 2011). In this work, we show that the TF model is also tissue specific, or more precisely, cell line specific (Fig.…”

supporting

confidence: 59%

“…Meanwhile, the binding sites of ;120 TFs in the human genome were determined by ChIP-seq experiments . These data sets enable us to investigate the relationship between TF binding and gene expression in a systematic and quantitative manner.We have previously shown in mouse that the expression levels of transcripts can be accurately reflected by TF-binding signals in their TSS regions (Cheng and Gerstein 2011). In this study, we aim at validating this result using data from CAGE that directly measures the expression levels of TSSs, and to investigate the influences of different technologies and RNA extraction methods on TSS expression quantification.…”

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confidence: 91%

“…Although systematic gene expression quantification has been available for a decade from microarray experiments (Schena et al 1995), only recently has the genome-wide identification of TF-binding sites become possible, owing to the development of chromatin immunoprecipitation followed by microarray (ChIP-chip) and sequencing (ChIP-seq) technologies (Ren et al 2000;Johnson et al 2007). In several previous studies, statistical models were constructed to study the regulatory functions of TF on gene expression based on the gene expression and TF-binding data (Ouyang et al 2009;Cheng and Gerstein 2011). These studies showed that TFbinding signals around the transcription start sites (TSSs) of genes are predictive of gene expression levels with fairly high accuracy.…”

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confidence: 99%

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Understanding transcriptional regulation by integrative analysis of transcription factor binding data

et al. 2012

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Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TFbinding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner.[Supplemental material is available for this article.]Transcription factors (TFs) are critical for the transcriptional regulation of gene expression (Takahashi and Yamanaka 2006;Vaquerizas et al. 2009). In humans, they represent the largest family of proteins, accounting for around 10% of genes (Babu et al. 2004). There are two types of TFs: general and sequence-specific. The former TFs act cooperatively with RNA polymerase II and are ubiquitously involved in the transcription of a large fraction of genes (Lee and Young 2000). The latter TFs bind specific subsets of target genes, leading to distinct spatiotemporal patterns of gene expression (Kadonaga 2004). Although systematic gene expression quantification has been available for a decade from microarray experiments (Schena et al. 1995), only recently has the genome-wide identification of TF-binding sites become possible, owing to the development of chromatin immunoprecipitation followed by microarray (ChIP-chip) and sequencing (ChIP-seq) technologies (Ren et al. 2000;Johnson et al. 2007).In several previous studies, statistical models were constructed to study the regulatory functions of TF on gene expression based on the gene expression and TF-binding data (Ouyang et al. 2009;Cheng and Gerstein 2011). These studies showed that TFbinding signals around the transcription start sites (TSSs) of genes are predictive of gene expression levels with fairly high accuracy. But these studies have the following limitations: First, estimates of gene expression have relied on probes (microarray) or sequence reads (RNA-seq) spread across a gene, possibly across multiple unknown isoforms of that gene. It is often difficult to accurately determine the expression level of each transcript based on such data, which...

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“…Importantly, our work differs from recent studies that have used large collections of ChIP-seq data sets to segment the genome Ernst and Kellis 2012;Hoffman et al 2012) into regulatory regions like promoters, enhancers, and insulators (Barski et al 2007;Cuddapah et al 2009;Moqtaderi et al 2010;Rada-Iglesias et al 2011), in that we aim to discover what factors bind the regulatory regions as protein complexes. Other studies have also shown that TF binding (Ouyang et al 2009;Cheng and Gerstein 2011;Cheng et al 2012), HMs (Karlic et al 2010;Cheng et al 2012;Wang et al 2012a), and recently even DNase I hypersensitive sites (Natarajan et al 2012) can explain a fraction of gene expression variation, but none of them have directly modeled the impact of complexes on gene expression. Thus, currently there are no broadly used integrative analytical approaches that can systematically infer the impact of protein complexes on gene expression.…”

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confidence: 99%

Inferring chromatin-bound protein complexes from genome-wide binding assays

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Elemento

2013

Genome Res.

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Genome-wide binding assays can determine where individual transcription factors bind in the genome. However, these factors rarely bind chromatin alone, but instead frequently bind to cis-regulatory elements (CREs) together with other factors thus forming protein complexes. Currently there are no integrative analytical approaches that can predict which complexes are formed on chromatin. Here, we describe a computational methodology to systematically capture protein complexes and infer their impact on gene expression. We applied our method to three human cell types, identified thousands of CREs, inferred known and undescribed complexes recruited to these CREs, and determined the role of the complexes as activators or repressors. Importantly, we found that the predicted complexes have a higher number of physical interactions between their members than expected by chance. Our work provides a mechanism for developing hypotheses about gene regulation via binding partners, and deciphering the interplay between combinatorial binding and gene expression.

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Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells

Cited by 118 publications

References 45 publications

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

Understanding transcriptional regulation by integrative analysis of transcription factor binding data

Inferring chromatin-bound protein complexes from genome-wide binding assays

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