scholarly journals MAnorm2 for quantitatively comparing groups of ChIP-seq samples

2020 ◽  
Author(s):  
Shiqi Tu ◽  
Mushan Li ◽  
Fengxiang Tan ◽  
Haojie Chen ◽  
Jian Xu ◽  
...  
Keyword(s):  
Gene Transcription ◽  
Binding Sites ◽  
Empirical Bayes ◽  
Large Cohort ◽  
Data Sets ◽  
Differential Analysis ◽  
Computational Tool ◽  
Eukaryotic Gene ◽  
Associated Proteins ◽  
Differential Binding

AbstractEukaryotic gene transcription is regulated by a large cohort of chromatin associated proteins, and inferring their differential binding sites between cellular contexts requires a rigorous comparison of the corresponding ChIP-seq data. We present MAnorm2, a new computational tool for quantitatively comparing groups of ChIP-seq samples. MAnorm2 uses a hierarchical strategy to normalize ChIP-seq data and then performs differential analysis by assessing within-group variability of ChIP-seq signals under an empirical Bayes framework. In this framework, MAnorm2 considers the abundance of differential ChIP-seq signals between groups of samples and the possibility of different within-group variability between groups. When samples in each group are biological replicates, MAnorm2 can reliably identify differential binding events even between highly similar cellular contexts. Using a number of real ChIP-seq data sets, we observed that MAnorm2 clearly outperformed existing tools for differential ChIP-seq analysis, with the improvement in performance being most dramatic when the groups of samples being compared had distinct global within-group variability.

scholarly journals Transcription signals and protein binding sites for sericin gene transcription in vitro

1989 ◽  
Vol 264 (31) ◽  
pp. 18707-18713 ◽  
Author(s):  
K Matsuno ◽  
C C Hui ◽  
S Takiya ◽  
T Suzuki ◽  
K Ueno ◽  
...  
Keyword(s):  
Protein Binding ◽  
Gene Transcription ◽  
Binding Sites ◽  
Protein Binding Sites ◽  
Transcription In Vitro ◽  
Transcription Signals

scholarly journals A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Magdalena Murawska ◽  
Dimitris Rizopoulos ◽  
Emmanuel Lesaffre
Keyword(s):  
Empirical Bayes ◽  
Cox Model ◽  
Data Sets ◽  
Two Stage ◽  
Nonlinear Mixed Effects Model ◽  
Resistance Evolution ◽  
Longitudinal Response ◽  
Longitudinal Measurements ◽  
Subject Specific ◽  
The Impact

In transplantation studies, often longitudinal measurements are collected for important markers prior to the actual transplantation. Using only the last available measurement as a baseline covariate in a survival model for the time to graft failure discards the whole longitudinal evolution. We propose a two-stage approach to handle this type of data sets using all available information. At the first stage, we summarize the longitudinal information with nonlinear mixed-effects model, and at the second stage, we include the Empirical Bayes estimates of the subject-specific parameters as predictors in the Cox model for the time to allograft failure. To take into account that the estimated subject-specific parameters are included in the model, we use a Monte Carlo approach and sample from the posterior distribution of the random effects given the observed data. Our proposal is exemplified on a study of the impact of renal resistance evolution on the graft survival.

scholarly journals HOT or not: examining the basis of high-occupancy target regions

2017 ◽  
Author(s):  
Katarzyna Wreczycka ◽  
Vedran Franke ◽  
Bora Uyar ◽  
Ricardo Wurmus ◽  
Altuna Akalin
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Cell Types ◽  
Data Sets ◽  
Gene Promoters ◽  
Quadruplex Dna ◽  
Golden Standard ◽  
Multiple Cell ◽  
Multiple Species

AbstractHigh-occupancy target (HOT) regions are the segments of the genome with unusually high number of transcription factor binding sites. These regions are observed in multiple species and thought to have biological importance due to high transcription factor occupancy. Furthermore, they coincide with house-keeping gene promoters and the associated genes are stably expressed across multiple cell types. Despite these features, HOT regions are solemnly defined using ChIP-seq experiments and shown to lack canonical motifs for transcription factors that are thought to be bound there. Although, ChIP-seq experiments are the golden standard for finding genome-wide binding sites of a protein, they are not noise free. Here, we show that HOT regions are likely to be ChIP-seq artifacts and they are similar to previously proposed “hyper-ChIPable” regions. Using ChIP-seq data sets for knocked-out transcription factors, we demonstrate presence of false positive signals on HOT regions. We observe sequence characteristics and genomic features that are discriminatory of HOT regions, such as GC/CpG-rich k-mers and enrichment of RNA-DNA hybrids (R-loops) and DNA tertiary structures (G-quadruplex DNA). The artificial ChIP-seq enrichment on HOT regions could be associated to these discriminatory features. Furthermore, we propose strategies to deal with such artifacts for the future ChIP-seq studies.

scholarly journals Trans-activation of the human SOX3 promoter by MAZ in NT2/D1 cells

2008 ◽  
Vol 60 (3) ◽  
pp. 379-387 ◽  
Author(s):  
Natasa Kovacevic-Grujicic ◽  
Kazunari Yokoyama ◽  
Milena Stevanovic
Keyword(s):  
Gene Expression ◽  
Neuronal Differentiation ◽  
Gene Transcription ◽  
Binding Sites ◽  
Promoter Activity ◽  
Zinc Finger Protein ◽  
Mobility Shift ◽  
Finger Protein ◽  
Sox3 Gene

In this study, we examine the role of three highly conserved putative binding sites for Myc-associated zinc finger protein (MAZ) in regulation of the human SOX3 gene expression. Electrophoretic mobility shift and supershift assays indicate that complexes formed at two out of three MAZ sites of the human SOX3 promoter involve ubiquitously expressed MAZ protein. Furthermore, in cotransfection experiments we demonstrate that MAZ acts as a positive regulator of SOX3 gene transcription in both undifferentiated and RA-differentiated NT2/D1 cells. Although MAZ increased both basal and RA-induced promoter activity, our results suggest that MAZ does not contribute to RA inducibility of the SOX3 promoter during neuronal differentiation of NT2/D1 cells.

Construction and Analysis of Protein-Protein Interaction Network

2019 ◽  
pp. 204-220
Author(s):  
Divya Dasagrandhi ◽  
Arul Salomee Kamalabai Ravindran ◽  
Anusuyadevi Muthuswamy ◽  
Jayachandran K. S.
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Data Sets ◽  
Protein Protein Interaction ◽  
Huge Data ◽  
Associated Proteins ◽  
High Throughput Experiments ◽  
Generation Sequencing ◽  
Protein Protein Interaction Network

Understanding the mechanisms of a disease is highly complicated due to the complex pathways involved in the disease progression. Despite several decades of research, the occurrence and prognosis of the diseases is not completely understood even with high throughput experiments like DNA microarray and next-generation sequencing. This is due to challenges in analysis of huge data sets. Systems biology is one of the major divisions of bioinformatics and has laid cutting edge techniques for the better understanding of these pathways. Construction of protein-protein interaction network (PPIN) guides the modern scientists to identify vital proteins through protein-protein interaction network, which facilitates the identification of new drug target and associated proteins. The chapter is focused on PPI databases, construction of PPINs, and its analysis.

scholarly journals EBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples

2019 ◽  
Vol 18 (6) ◽  
Author(s):  
Tobias Madsen ◽  
Michał Świtnicki ◽  
Malene Juul ◽  
Jakob Skou Pedersen
Keyword(s):  
Differential Expression ◽  
Empirical Bayes ◽  
T Test ◽  
Joint Analysis ◽  
Small Data ◽  
Data Sets ◽  
Data Types ◽  
Fast Evaluation ◽  
Equality Of Means ◽  
Small Data Sets

Abstract DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types. Here, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets. EBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

scholarly journals Estrogen Regulates Transcription of the Ovine Oxytocin Receptor Gene through GC-Rich SP1 Promoter Elements

Endocrinology ◽  
2006 ◽  
Vol 147 (2) ◽  
pp. 899-911 ◽  
Author(s):  
JoAnn G. W. Fleming ◽  
Thomas E. Spencer ◽  
Stephen H. Safe ◽  
Fuller W. Bazer
Keyword(s):  
Gene Expression ◽  
Progesterone Receptor ◽  
Gene Transcription ◽  
Binding Sites ◽  
Promoter Activity ◽  
Receptor Gene ◽  
Prostaglandin F2α ◽  
Oxytocin Receptor ◽  
Oxytocin Receptor Gene ◽  
Esr1 Gene

Establishment of pregnancy in ruminants results from paracrine signaling by interferon τ (IFNT) from the conceptus to uterine endometrial luminal epithelia (LE) that prevents release of luteolytic prostaglandin F2α pulses. In cyclic and pregnant ewes, progesterone down-regulates progesterone receptor (PGR) gene expression in LE. In cyclic ewes, loss of PGR allows for increases in estrogen receptor α (ESR1) and then oxytocin receptor (OXTR) gene expression followed by oxytocin-induced prostaglandin F2α pulses. In pregnant ewes, IFNT inhibits transcription of the ESR1 gene, which presumably inhibits OXTR gene transcription. Alternatively, IFNT may directly inhibit OXTR gene transcription. The 5′ promoter/enhancer region of the ovine OXTR gene was cloned and found to contain predicted binding sites for activator protein 1, SP1, and PGR, but not for ESR1. Deletion analysis showed that the basal promoter activity was dependent on the region from −144 to −4 bp that contained only SP1 sites. IFNT did not affect activity of the OXTR promoter. In cells transfected with ESR1, E2, and ICI 182,780 increased promoter activity due to GC-rich SP1 binding sites at positions −104 and −64. Mutation analyses showed that the proximal SP1 sites mediated ESR1 action as well as basal activity of the promoter. In response to progesterone, progesterone receptor B also increased OXTR promoter activity. SP1 protein was constitutively expressed and abundant in the LE of the ovine uterus. These results support the hypothesis that the antiluteolytic effects of IFNT are mediated by direct inhibition or silencing of ESR1 gene transcription, thereby precluding ESR1/SP1 from stimulating OXTR gene transcription.

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