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Prediction of bipartite transcriptional regulatory elements using transcriptome data of Arabidopsis.

ABSTRACT: In our previous study, a methodology was established to predict transcriptional regulatory elements in promoter sequences using transcriptome data based on a frequency comparison of octamers. Some transcription factors, including the NAC family, cannot be covered by this method because their binding sequences have non-specific spacers in the middle of the two binding sites. In order to remove this blind spot in promoter prediction, we have extended our analysis by including bipartite octamers that are composed of '4 bases-a spacer with a flexible length-4 bases'. 8,044 pre-selected bipartite octamers, which had an overrepresentation of specific spacer lengths in promoter sequences and sequences related to core elements removed, were subjected to frequency comparison analysis. Prediction of ER stress-responsive elements in the BiP/BiPL promoter and an ANAC017 target sequence resulted in precise detection of true positives, judged by functional analyses of a reported article and our own in vitro protein-DNA binding assays. These results demonstrate that incorporation of bipartite octamers with continuous ones improves promoter prediction significantly.

SUBMITTER: Yamamoto YY

PROVIDER: S-EPMC5499772 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Prediction of bipartite transcriptional regulatory elements using transcriptome data of Arabidopsis.

Yamamoto Yoshiharu Y YY Ichida Hiroyuki H Hieno Ayaka A Obata Daichi D Tokizawa Mutsutomo M Nomoto Mika M Tada Yasuomi Y Kusunoki Kazutaka K Koyama Hiroyuki H Hayami Natsuki N

DNA research : an international journal for rapid publication of reports on genes and genomes 20170601 3

In our previous study, a methodology was established to predict transcriptional regulatory elements in promoter sequences using transcriptome data based on a frequency comparison of octamers. Some transcription factors, including the NAC family, cannot be covered by this method because their binding sequences have non-specific spacers in the middle of the two binding sites. In order to remove this blind spot in promoter prediction, we have extended our analysis by including bipartite octamers th ...[more]

PMID: 28158431

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Project description:BackgroundA complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and algorithms for predicting transcriptional regulatory mechanisms from sequence, gene expression, and binding data. However, most of these studies involved the use of yeast which has much simpler regulatory networks than human and has many genome wide binding data and gene expression data under diverse conditions. Studies of genome wide transcriptional networks of human genomes currently lag behind those of yeast.ResultsWe report herein a new method that combines gene expression data analysis with promoter analysis to infer transcriptional regulatory elements of human genes. The Z scores from the application of gene set analysis with gene sets of transcription factor binding sites (TFBSs) were successfully used to represent the activity of TFBSs in a given microarray data set. A significant correlation between the Z scores of gene sets of TFBSs and individual genes across multiple conditions permitted successful identification of many known human transcriptional regulatory elements of genes as well as the prediction of numerous putative TFBSs of many genes which will constitute a good starting point for further experiments. Using Z scores of gene sets of TFBSs produced better predictions than the use of mRNA levels of a transcription factor itself, suggesting that the Z scores of gene sets of TFBSs better represent diverse mechanisms for changing the activity of transcription factors in the cell. In addition, cis-regulatory modules, combinations of co-acting TFBSs, were readily identified by our analysis.ConclusionBy a strategic combination of gene set level analysis of gene expression data sets and promoter analysis, we were able to identify and predict many transcriptional regulatory elements of human genes. We conclude that this approach will aid in decoding some of the important transcriptional regulatory elements of human genes.

Dataset Information

Prediction of bipartite transcriptional regulatory elements using transcriptome data of Arabidopsis.

Publications

Prediction of bipartite transcriptional regulatory elements using transcriptome data of Arabidopsis.

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