Dataset Information

In-silico analysis of cis-acting regulatory elements of pathogenesis-related proteins of Arabidopsis thaliana and Oryza sativa.

ABSTRACT: Pathogenesis related (PR) proteins are low molecular weight family of proteins induced in plants under various biotic and abiotic stresses. They play an important role in plant-defense mechanism. PRs have wide range of functions, acting as hydrolases, peroxidases, chitinases, anti-fungal, protease inhibitors etc. In the present study, an attempt has been made to analyze promoter regions of PR1, PR2, PR5, PR9, PR10 and PR12 of Arabidopsis thaliana and Oryza sativa. Analysis of cis-element distribution revealed the functional multiplicity of PRs and provides insight into the gene regulation. CpG islands are observed only in rice PRs, which indicates that monocot genome contains more GC rich motifs than dicots. Tandem repeats were also observed in 5' UTR of PR genes. Thus, the present study provides an understanding of regulation of PR genes and their versatile roles in plants.

SUBMITTER: Kaur A

PROVIDER: S-EPMC5598985 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

In-silico analysis of cis-acting regulatory elements of pathogenesis-related proteins of Arabidopsis thaliana and Oryza sativa.

Kaur Amritpreet A Pati Pratap Kumar PK Pati Aparna Maitra AM Nagpal Avinash Kaur AK

PloS one 20170914 9

Pathogenesis related (PR) proteins are low molecular weight family of proteins induced in plants under various biotic and abiotic stresses. They play an important role in plant-defense mechanism. PRs have wide range of functions, acting as hydrolases, peroxidases, chitinases, anti-fungal, protease inhibitors etc. In the present study, an attempt has been made to analyze promoter regions of PR1, PR2, PR5, PR9, PR10 and PR12 of Arabidopsis thaliana and Oryza sativa. Analysis of cis-element distrib ...[more]

PMID: 28910327

Similar Datasets

Project description:Prediction of transcriptional regulatory mechanisms in Arabidopsis has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work 1, that a combination of correlation measurements and cis-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF-->target relationships on a genome-wide scale.We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and cis-regulatory element (CRE) detection (>or=1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor (TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF-->target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the >or=1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions.These findings provide an approach and guidance for researchers interested in predicting transcriptional regulatory mechanisms using microarray data that they generate (or microarray data that is publically available) combined with CRE detection in promoter sequence data.

Dataset Information

In-silico analysis of cis-acting regulatory elements of pathogenesis-related proteins of Arabidopsis thaliana and Oryza sativa.

Publications

In-silico analysis of cis-acting regulatory elements of pathogenesis-related proteins of Arabidopsis thaliana and Oryza sativa.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets