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Modeling ChIP sequencing in silico with applications.

ABSTRACT: ChIP sequencing (ChIP-seq) is a new method for genomewide mapping of protein binding sites on DNA. It has generated much excitement in functional genomics. To score data and determine adequate sequencing depth, both the genomic background and the binding sites must be properly modeled. To develop a computational foundation to tackle these issues, we first performed a study to characterize the observed statistical nature of this new type of high-throughput data. By linking sequence tags into clusters, we show that there are two components to the distribution of tag counts observed in a number of recent experiments: an initial power-law distribution and a subsequent long right tail. Then we develop in silico ChIP-seq, a computational method to simulate the experimental outcome by placing tags onto the genome according to particular assumed distributions for the actual binding sites and for the background genomic sequence. In contrast to current assumptions, our results show that both the background and the binding sites need to have a markedly nonuniform distribution in order to correctly model the observed ChIP-seq data, with, for instance, the background tag counts modeled by a gamma distribution. On the basis of these results, we extend an existing scoring approach by using a more realistic genomic-background model. This enables us to identify transcription-factor binding sites in ChIP-seq data in a statistically rigorous fashion.

SUBMITTER: Zhang ZD

PROVIDER: S-EPMC2507756 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Modeling ChIP sequencing in silico with applications.

Zhang Zhengdong D ZD Rozowsky Joel J Snyder Michael M Chang Joseph J Gerstein Mark M

PLoS computational biology 20080822 8

ChIP sequencing (ChIP-seq) is a new method for genomewide mapping of protein binding sites on DNA. It has generated much excitement in functional genomics. To score data and determine adequate sequencing depth, both the genomic background and the binding sites must be properly modeled. To develop a computational foundation to tackle these issues, we first performed a study to characterize the observed statistical nature of this new type of high-throughput data. By linking sequence tags into clus ...[more]

PMID: 18725927

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Project description:BackgroundThe ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.ResultsIn this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length.ConclusionThe Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.

Project description:Textile dyes and microbial contamination of surface water bodies have been recognized as emerging quality concerns around the globe. The simultaneous resolve of such impurities can pave the route for an amicable technological solution. This study reports the photocatalytic performance and the biocidal potential of nitrogen-doped TiO2 against reactive black 5 (RB5), a double azo dye and E. coli. Molecular docking was performed to identify and quantify the interactions of the TiO2 with ?-lactamase enzyme and to predict the biocidal mechanism. The sol-gel technique was employed for the synthesis of different mol% nitrogen-doped TiO2. The synthesized photocatalysts were characterized using thermal gravimetric analysis (TGA), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Brunauer-Emmett-Teller (BET) and diffuse reflectance spectroscopy (DRS). The effects of different synthesis and reaction parameters were studied. RB5 dye degradation was monitored by tracking shifts in the absorption spectrum and percent chemical oxygen demand (COD) removal. The best nanomaterial depicted 5.57 nm crystallite size, 49.54 m2 g-1 specific surface area, 11-40 nm particle size with spherical morphologies, and uniform distribution. The RB5 decolorization data fits well with the pseudo-first-order kinetic model, and the maximum monolayer coverage capacity for the Langmuir adsorption model was found to be 40 mg g-1 with Kads of 0.113 mg-1. The LH model yielded a higher coefficient KC (1.15 mg L-1 h-1) compared to the adsorption constant KLH (0.3084 L mg-1). 90% COD removal was achieved in 60 min of irradiation, confirmed by the disappearance of spectral peaks. The best-optimized photocatalysts showed a noticeable biocidal potential against human pathogenic strain E. coli in 150 min. The biocidal mechanism of best-optimized photocatalyst was predicted by molecular docking simulation against E. coli ?-lactamase enzyme. The docking score (-7.6 kcal mol-1) and the binding interaction with the active site residues (Lys315, Thr316, and Glu272) of ?-lactamase further confirmed that inhibition of ?-lactamase could be a most probable mechanism of biocidal activity.

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Modeling ChIP sequencing in silico with applications.

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Modeling ChIP sequencing in silico with applications.

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