Dataset Information

BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations.

ABSTRACT: Sequence variations in regulatory DNA regions are known to cause functionally important consequences for gene expression. DNA sequence variations may have an essential role in determining phenotypes and may be linked to disease; however, their identification through analysis of massive genome-wide sequencing data is a great challenge. In this work, a new computational pipeline, a Bayesian method for protein-DNA interaction with binding affinity ranking (BayesPI-BAR), is proposed for quantifying the effect of sequence variations on protein binding. BayesPI-BAR uses biophysical modeling of protein-DNA interactions to predict single nucleotide polymorphisms (SNPs) that cause significant changes in the binding affinity of a regulatory region for transcription factors (TFs). The method includes two new parameters (TF chemical potentials or protein concentrations and direct TF binding targets) that are neglected by previous methods. The new method is verified on 67 known human regulatory SNPs, of which 47 (70%) have predicted true TFs ranked in the top 10. Importantly, the performance of BayesPI-BAR, which uses principal component analysis to integrate multiple predictions from various TF chemical potentials, is found to be better than that of existing programs, such as sTRAP and is-rSNP, when evaluated on the same SNPs. BayesPI-BAR is a publicly available tool and is able to carry out parallelized computation, which helps to investigate a large number of TFs or SNPs and to detect disease-associated regulatory sequence variations in the sea of genome-wide noncoding regions.

SUBMITTER: Wang J

PROVIDER: S-EPMC4666384 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations.

Wang Junbai J Batmanov Kirill K

Nucleic acids research 20150721 21

Sequence variations in regulatory DNA regions are known to cause functionally important consequences for gene expression. DNA sequence variations may have an essential role in determining phenotypes and may be linked to disease; however, their identification through analysis of massive genome-wide sequencing data is a great challenge. In this work, a new computational pipeline, a Bayesian method for protein-DNA interaction with binding affinity ranking (BayesPI-BAR), is proposed for quantifying ...[more]

PMID: 26202972

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Project description:BackgroundThe detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency.ResultsWe developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded.ConclusionsThe reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3) functional important sites are not exclusively located in cis-regulatory elements, but are rather dispersed through regulatory region. It is of importance that some of the sites with high functional impact in hb, Kr and kni regulatory regions coincide with strong sites annotated and verified in Dnase I footprint assays.

Project description:PurposeLeber hereditary optic neuropathy (LHON), a maternally inherited disorder, results from point mutations in mitochondrial DNA (mtDNA). MtDNA is highly polymorphic in nature with very high mutation rate, 10-17 fold higher as compared to nuclear genome. Identification of new mtDNA sequence variations is necessary to establish a clean link with human disease. Thus this study was aimed to assess or evaluate LHON patients for novel mtDNA sequence variations.Materials and methodsTwenty LHON patients were selected from the neuro-ophthalmology clinic of the All India Institute of Medical Sciences, New Delhi, India. DNA was isolated from whole blood samples. The entire coding region of the mitochondrial genome was amplified by PCR in 20 patients and 20 controls. For structural analysis (molecular modeling and simulation) the MODELER 9.2 program in Discovery Studio (DS 2.0) was used.ResultsMtDNA sequencing revealed a total of 47 nucleotide variations in the 20 LHON patients and 29 variations in 20 controls. Of 47 changes in patients 21.2% (10/47) were nonsynonymous and the remaining 78.72% (37/47) were synonymous. Five nonsynonymous changes, including primary LHON mutations (NADH dehydrogenase subunit 1 [ND1]:p.A52T, NADH dehydrogenase subunit 6 [ND6]:p.M64V, adenosine triphosphate [ATP] synthase subunit a (F-ATPase protein 6) [ATPase6]:p.M181T, NADH dehydrogenase subunit 4 [ND4]:p.R340H, and cytochrome B [CYB]:p.F181L), were found to be pathogenic. A greater number of changes were present in complex I (53.19%; 25/47), followed by complex III (19.14%; 9/47), then complex IV (19.14%; 9/47), then complex V (8.5%; 4/47). Nonsynonymous variations may impair respiratory chain and oxidative phosphorylation (OXPHOS) pathways, which results in low ATP production and elevated reactive oxygen species (ROS) levels. Oxidative stress is the underlying etiology in various diseases and also plays a crucial role in LHON.ConclusionsThis study describes the role of mtDNA sequence variations in LHON patients. Primary LHON mutations of mtDNA are main variants leading to LHON, but mutations in other mitochondrial genes may also play an important role in pathogenesis of LHON as indicated in the present study. Certain alleles in certain haplogroups have protective or deleterious roles and hence there is a need to analyze a large number of cases for correlating phenotype and disease severity with mutation and mtDNA haplogroups.

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BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations.

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BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations.

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