Dataset Information

Computational predictions of common transcription factor binding sites on the genes of proline metabolism in plants.

ABSTRACT: Proline, an imino acid, has been well documented to be associated with the stress response induced by abiotic factors such as drought, cold and salinity in plants and biotic factors such as bacterial and fungal attacks. However, the regulatory mechanisms controlling proline metabolism, intercellular and intracellular transport and connections of proline to other metabolic pathways are poorly understood. F-MATCH analysis combined with composite module analysis (CMA) revealed that the binding sites matching matrices for O2 and OCSBF-1 were overrepresented in the promoters of differentially expressed proline metabolism genes. The presence of MYBAS1 consensus binding sites occurring in combination with O2 and OCSBF1 in the promoters of genes of proline biosynthesis pathway and SBF1 and GT1 consensus binding sites occurring in combination with O2 and OCSBF1 in the promoters of proline catabolic pathway genes suggest their involvement in modulation of proline metabolism and its accumulation in plants.

SUBMITTER: Kiran U

PROVIDER: S-EPMC3490059 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational predictions of common transcription factor binding sites on the genes of proline metabolism in plants.

Kiran Usha U Abdin Malik Zainul MZ

Bioinformation 20120921 18

Proline, an imino acid, has been well documented to be associated with the stress response induced by abiotic factors such as drought, cold and salinity in plants and biotic factors such as bacterial and fungal attacks. However, the regulatory mechanisms controlling proline metabolism, intercellular and intracellular transport and connections of proline to other metabolic pathways are poorly understood. F-MATCH analysis combined with composite module analysis (CMA) revealed that the binding site ...[more]

PMID: 23144545

Similar Datasets

Project description:BackgroundChIP-Seq is widely used to detect genomic segments bound by transcription factors (TF), either directly at DNA binding sites (BSs) or indirectly via other proteins. Currently, there are many software tools implementing different approaches to identify TFBSs within ChIP-Seq peaks. However, their use for the interpretation of ChIP-Seq data is usually complicated by the absence of direct experimental verification, making it difficult both to set a threshold to avoid recognition of too many false-positive BSs, and to compare the actual performance of different models.ResultsUsing ChIP-Seq data for FoxA2 binding loci in mouse adult liver and human HepG2 cells we compared FoxA binding-site predictions for four computational models of two fundamental classes: pattern matching based on existing training set of experimentally confirmed TFBSs (oPWM and SiteGA) and de novo motif discovery (ChIPMunk and diChIPMunk). To properly select prediction thresholds for the models, we experimentally evaluated affinity of 64 predicted FoxA BSs using EMSA that allows safely distinguishing sequences able to bind TF. As a result we identified thousands of reliable FoxA BSs within ChIP-Seq loci from mouse liver and human HepG2 cells. It was found that the performance of conventional position weight matrix (PWM) models was inferior with the highest false positive rate. On the contrary, the best recognition efficiency was achieved by the combination of SiteGA & diChIPMunk/ChIPMunk models, properly identifying FoxA BSs in up to 90% of loci for both mouse and human ChIP-Seq datasets.ConclusionsThe experimental study of TF binding to oligonucleotides corresponding to predicted sites increases the reliability of computational methods for TFBS-recognition in ChIP-Seq data analysis. Regarding ChIP-Seq data interpretation, basic PWMs have inferior TFBS recognition quality compared to the more sophisticated SiteGA and de novo motif discovery methods. A combination of models from different principles allowed identification of proper TFBSs.

Dataset Information

Computational predictions of common transcription factor binding sites on the genes of proline metabolism in plants.

Publications

Computational predictions of common transcription factor binding sites on the genes of proline metabolism in plants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets