Dataset Information

Markov chain-based promoter structure modeling for tissue-specific expression pattern prediction.

ABSTRACT: Transcriptional regulation is the first level of regulation of gene expression and is therefore a major topic in computational biology. Genes with similar expression patterns can be assumed to be co-regulated at the transcriptional level by promoter sequences with a similar structure. Current approaches for modeling shared regulatory features tend to focus mainly on clustering of cis-regulatory sites. Here we introduce a Markov chain-based promoter structure model that uses both shared motifs and shared features from an input set of promoter sequences to predict candidate genes with similar expression. The model uses positional preference, order, and orientation of motifs. The trained model is used to score a genomic set of promoter sequences: high-scoring promoters are assumed to have a structure similar to the input sequences and are thus expected to drive similar expression patterns. We applied our model on two datasets in Caenorhabditis elegans and in Ciona intestinalis. Both computational and experimental verifications indicate that this model is capable of predicting candidate promoters driving similar expression patterns as the input-regulatory sequences. This model can be useful for finding promising candidate genes for wet-lab experiments and for increasing our understanding of transcriptional regulation.

SUBMITTER: Vandenbon A

PROVIDER: S-EPMC2650632 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Markov chain-based promoter structure modeling for tissue-specific expression pattern prediction.

Vandenbon Alexis A Miyamoto Yuki Y Takimoto Noriko N Kusakabe Takehiro T Nakai Kenta K

DNA research : an international journal for rapid publication of reports on genes and genomes 20080207 1

Transcriptional regulation is the first level of regulation of gene expression and is therefore a major topic in computational biology. Genes with similar expression patterns can be assumed to be co-regulated at the transcriptional level by promoter sequences with a similar structure. Current approaches for modeling shared regulatory features tend to focus mainly on clustering of cis-regulatory sites. Here we introduce a Markov chain-based promoter structure model that uses both shared motifs an ...[more]

PMID: 18258700

Similar Datasets

Project description:BackgroundLong-range interactions between regulatory DNA elements such as enhancers, insulators and promoters play an important role in regulating transcription. As chromatin contacts have been found throughout the human genome and in different cell types, spatial transcriptional control is now viewed as a general mechanism of gene expression regulation. Chromosome Conformation Capture Carbon Copy (5C) and its variant Hi-C are techniques used to measure the interaction frequency (IF) between specific regions of the genome. Our goal is to use the IF data generated by these experiments to computationally model and analyze three-dimensional chromatin organization.ResultsWe formulate a probabilistic model linking 5C/Hi-C data to physical distances and describe a Markov chain Monte Carlo (MCMC) approach called MCMC5C to generate a representative sample from the posterior distribution over structures from IF data. Structures produced from parallel MCMC runs on the same dataset demonstrate that our MCMC method mixes quickly and is able to sample from the posterior distribution of structures and find subclasses of structures. Structural properties (base looping, condensation, and local density) were defined and their distribution measured across the ensembles of structures generated. We applied these methods to a biological model of human myelomonocyte cellular differentiation and identified distinct chromatin conformation signatures (CCSs) corresponding to each of the cellular states. We also demonstrate the ability of our method to run on Hi-C data and produce a model of human chromosome 14 at 1Mb resolution that is consistent with previously observed structural properties as measured by 3D-FISH.ConclusionsWe believe that tools like MCMC5C are essential for the reliable analysis of data from the 3C-derived techniques such as 5C and Hi-C. By integrating complex, high-dimensional and noisy datasets into an easy to interpret ensemble of three-dimensional conformations, MCMC5C allows researchers to reliably interpret the result of their assay and contrast conformations under different conditions.Availabilityhttp://Dostielab.biochem.mcgill.ca.

Project description:Air pollution is a matter of concern among the public, especially for those living in urban and industrial areas. Markov chain modeling is often used to model the underlying dynamics of air pollution, which involves describing the transition probability of going from one air pollution state to another. Thus, estimating the transition probability matrix for the data of the air pollution index (API) is an essential process in the modeling. However, one may observe many zero probabilities in the transition probability matrix, especially when faced with a small sample, interpreting the results with respect to the climate condition less realistic. This study proposes a robust empirical Bayes method, which incorporates a method of smoothing the zero frequencies in the count matrix, contributing to an improved estimation of the transition probability matrix. The robustness of the empirical Bayesian estimation is investigated based on Bayes risk. The transition probability matrices estimated based on the robust empirical Bayes method for the hourly API data collected from seven monitoring stations in Malaysia for the period 2012 to 2014 are used for determining the air pollution characteristics such as the mean residence time, the steady-state probability and the mean recurrence time. Furthermore, the proposed method has been evaluated by Monte Carlo simulations. Results suggest that it is quite effective in producing non-zero transition probability estimates, and superior to the maximum likelihood method in terms of minimizing the mean squared error for individual and entire transition probabilities. Therefore, the robust empirical Bayes method proves to be an improved approach to the estimation of the Markov chain. When applied to API data, it could provide important information on air pollution dynamics that may help guiding the development of proper strategies for managing the impact of air quality.Supplementary informationThe online version contains supplementary material available at 10.1007/s40201-020-00607-4.

Project description:BACKGROUND:Tissue specific promoters may be utilized for a variety of applications, including programmed gene expression in cell types, tissues and organs of interest, for developing different cell culture models or for use in gene therapy. We report a novel, tissue-specific promoter that was identified and engineered from the native upstream regulatory region of the human gene NDUFV1 containing an endogenous retroviral sequence. RESULTS:Among seven established human cell lines and five primary cultures, this modified NDUFV1 upstream sequence (mNUS) was active only in human undifferentiated germ-derived cells (lines Tera-1 and EP2102), where it demonstrated high promoter activity (approximately twice greater than that of the SV40 early promoter, and comparable to the routinely used cytomegaloviral promoter). To investigate the potential applicability of the mNUS promoter for biotechnological needs, a construct carrying a recombinant cytosine deaminase (RCD) suicide gene under the control of mNUS was tested in cell lines of different tissue origin. High cytotoxic effect of RCD with a cell-death rate approximately 60% was observed only in germ-derived cells (Tera-1), whereas no effect was seen in a somatic, kidney-derived control cell line (HEK293). In further experiments, we tested mNUS-driven expression of a hyperactive Sleeping Beauty transposase (SB100X). The mNUS-SB100X construct mediated stable transgene insertions exclusively in germ-derived cells, thereby providing further evidence of tissue-specificity of the mNUS promoter. CONCLUSIONS:We conclude that mNUS may be used as an efficient promoter for tissue-specific gene expression in human germ-derived cells in many applications. Our data also suggest that the 91 bp-long sequence located exactly upstream NDUFV1 transcriptional start site plays a crucial role in the activity of this gene promoter in vitro in the majority of tested cell types (10/12), and an important role--in the rest two cell lines.

Dataset Information

Markov chain-based promoter structure modeling for tissue-specific expression pattern prediction.

Publications

Markov chain-based promoter structure modeling for tissue-specific expression pattern prediction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets