ASpred: a Bioinformatics System for Predicting Abiotic Stress related Genes in Plants
  Home | Predict | Datasets | Validation | Contact
Location:  HOME
Related Links: Databases / Prediction Servers

The present tool is developed as a part of collaborative project between the Functional Genomics Laboratory (Bansal Lab), National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India and The Kaundal Bioinformatics Laboratory, Oklahoma State University, Stillwater, USA; to develop a comprehensive bioinformatics-based system for accurately predicting abiotic stress-related genes in plants and their experimental validation. Abiotic stress cause changes in soil-plant-atmosphere continuum and is responsible for reduced yield in several major crops. Therefore, the subject of abiotic stress response in plants - metabolism, productivity and sustainability - is gaining considerable significance in the contemporary world, particularly with the rapid advancement in functional genomics techniques.

In plants, the whole genome sequence has been completed for several species, such as Arabidopsis thaliana, Oryza sativa, Medicago truncatula, Lotus japonicus, Brachypodium distachyon, Populus trichocarpa, and Zea mays, with many of the sequencing projects currently underway. Over the time, various researchers have identified some abiotic stress response elements in plants, such as the ABA responsive element (ABRE) which is represented by the motif ACGTGKC (Hattori et al., 2002), and has been found in Arabidopsis, Rice, Barley, Wheat. The drought responsive element (DRE) element (represented by the motif RCCGAC), and MYB and MYC binding sites are also involved in drought and cold stress (Shinozaki et al., 2003). It has been shown that the ABRE element sometimes co-occurs with other abiotic stress motifs, such as the DRE or the coupling element (CE), thereby forming Transcription Factor Binding Site (TFBS’s) modules (Gómez-Porras et al. 2007; Zhang et al. 2005). In spite of the considerable amount of work done on stress responsive genes, relatively few promoter elements and genes have been discovered and examined thoroughly that are involved in abiotic stress response.

Although there have been some attempts to computationally predict abiotic stress related elements (Sandve and Drabløs, 2006, Cserháti et al., 2011), there is no comprehensive method that can accurately predict the abiotic stress-related genes on a genome-scale. Secondly, with the climate change effects on our agricultural systems all over the world already showing signs, we need to actively identify such stress related pathways in plants and accordingly, develop cultivars which could adapt to changing environmental conditions.

Here, we develop an integrated bioinformatics system called, ASpred for predicting genome-wide abiotic stress related genes with high accuracy. We use an Artificial Intelligence (machine learning) approach combined with the homology-based approach and develop various prediction modules. These modules have been developed by extracting diverse sequence features from known stress-responsive genes and have been tested rigorously on ‘independent’ test datasets. Of all the diverse models developed, five best performing prediction modules have been implemented on the World Wide Web as a dynamic web server 'ASpred' that provides wider options to the users extracting different features from their query protein sequences e.g. the simple amino acid composition, sequence-order based dipeptide composition, terminal-based information, Position Specific Scoring Matrix (PSSM), similarity-based PSI-BLAST, including the best performing hybrid classifier. We believe this tool would be of immense help to the plant research community worldwide.

Experimentally, some of the ‘previously unknown’ genes have been predicted using ASpred and then, experimentally tested under lab / field conditions for their role in abiotic stress (see ‘Validation’). These all validation tests confirm the performance and reliability of the ASpred system.


  ©  2012         |        National Bureau of Plant Genetics Resources, New Delhi, India        |        The Kaundal Bioinformatics Laboratory, Stillwater, USA