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The organization of nucleosomes around splice sites.

ABSTRACT: The occupancy of nucleosomes along chromosome is a key factor for gene regulation. However, except promoter regions, genome-wide properties and functions of nucleosome organization remain unclear in mammalian genomes. Using the computational model of Increment of Diversity with Quadratic Discriminant (IDQD) trained from the microarray data, the nucleosome occupancy score (NOScore) was defined and applied to splice junction regions of constitutive, cassette exon, alternative 3' and 5' splicing events in the human genome. We found an interesting relation between NOScore and RNA splicing: exon regions have higher NOScores compared with their flanking intron sequences in both constitutive and alternative splicing events, indicating the stronger nucleosome occupation potential of exon regions. In addition, NOScore valleys present at approximately 25 bp upstream of the acceptor site in all splicing events. By defining folding diversity-to-energy ratio to describe RNA structural flexibility, we demonstrated that primary RNA transcripts from nucleosome occupancy regions are relatively rigid and those from nucleosome depleted regions are relatively flexible. The negative correlation between nucleosome occupation/depletion of DNA sequence and structural flexibility/rigidity of its primary transcript around splice junctions may provide clues to the deeper understanding of the unexpected role for nucleosome organization in the regulation of RNA splicing.

SUBMITTER: Chen W

PROVIDER: S-EPMC2875036 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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The organization of nucleosomes around splice sites.

Chen Wei W Luo Liaofu L Zhang Lirong L

Nucleic acids research 20100121 9

The occupancy of nucleosomes along chromosome is a key factor for gene regulation. However, except promoter regions, genome-wide properties and functions of nucleosome organization remain unclear in mammalian genomes. Using the computational model of Increment of Diversity with Quadratic Discriminant (IDQD) trained from the microarray data, the nucleosome occupancy score (NOScore) was defined and applied to splice junction regions of constitutive, cassette exon, alternative 3' and 5' splicing ev ...[more]

PMID: 20097656

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Project description:In computational methods, position weight matrices (PWMs) are commonly applied for transcription factor binding site (TFBS) prediction. Although these matrices are more accurate than simple consensus sequences to predict actual binding sites, they usually produce a large number of false positive (FP) predictions and so are impoverished sources of information. Several studies have employed additional sources of information such as sequence conservation or the vicinity to transcription start sites to distinguish true binding regions from random ones. Recently, the spatial distribution of modified nucleosomes has been shown to be associated with different promoter architectures. These aligned patterns can facilitate DNA accessibility for transcription factors. We hypothesize that using data from these aligned and periodic patterns can improve the performance of binding region prediction. In this study, we propose two effective features, "modified nucleosomes neighboring" and "modified nucleosomes occupancy", to decrease FP in binding site discovery. Based on these features, we designed a logistic regression classifier which estimates the probability of a region as a TFBS. Our model learned each feature based on Sp1 binding sites on Chromosome 1 and was tested on the other chromosomes in human CD4+T cells. In this work, we investigated 21 histone modifications and found that only 8 out of 21 marks are strongly correlated with transcription factor binding regions. To prove that these features are not specific to Sp1, we combined the logistic regression classifier with the PWM, and created a new model to search TFBSs on the genome. We tested the model using transcription factors MAZ, PU.1 and ELF1 and compared the results to those using only the PWM. The results show that our model can predict Transcription factor binding regions more successfully. The relative simplicity of the model and capability of integrating other features make it a superior method for TFBS prediction.

Dataset Information

The organization of nucleosomes around splice sites.

Publications

The organization of nucleosomes around splice sites.

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