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a catalog of worldwide biological databases

Understanding the principles of abiotic and biotic stress responses, tolerance and adaptation remains important in plant physiology research to develop better varieties of crop plants. Better understanding of plant stress response mechanisms and application of knowledge derived from integrated experimental and bioinformatics approaches are gaining importance. Earlier, we showed that compiling a database of stress-responsive transcription factors and their corresponding target binding sites in the form of Hidden Markov models at promoter, untranslated and upstream regions of stress-up-regulated genes from expression analysis can help in elucidating various aspects of the stress response in Arabidopsis. In addition to the extensive content in the first version, STIFDB2 is now updated with 15 stress signals, 31 transcription factors and 5,984 stress-responsive genes from three species (Arabidopsis thaliana, Oryza sativa subsp. japonica and Oryza sativa subsp. indica). We have employed an integrated biocuration and genomic data mining approach to characterize the data set of transcription factors and consensus binding sites from literature mining and stress-responsive genes from the Gene Expression Omnibus. STIFDB2 currently has 38,798 associations of stress signals, stress-responsive genes and transcription factor binding sites predicted using the Stress-responsive Transcription Factor (STIF) algorithm, along with various functional annotation data. As a unique plant stress regulatory genomics data platform, STIFDB2 can be utilized for targeted as well as high-throughput experimental and computational studies to unravel principles of the stress regulome in dicots and gramineae. STIFDB2 is available from the URL: http://caps.ncbs.res.in/stifdb2.

Elucidating the key players of molecular mechanism that mediate the complex stress-responses in plants system is an important step to develop improved variety of stress tolerant crops. Understanding the effects of different types of biotic and abiotic stress is a rapidly emerging domain in the area of plant research to develop better, stress tolerant plants. Information about the transcription factors, transcription factor binding sites, function annotation of proteins coded by genes expressed during abiotic stress (for example: drought, cold, salinity, excess light, abscisic acid, and oxidative stress) response will provide better understanding of this phenomenon. STIFDB is a database of abiotic stress responsive genes and their predicted abiotic transcription factor binding sites in Arabidopsis thaliana. We integrated 2269 genes upregulated in different stress related microarray experiments and surveyed their 1000 bp and 100 bp upstream regions and 5'UTR regions using the STIF algorithm and identified putative abiotic stress responsive transcription factor binding sites, which are compiled in the STIFDB database. STIFDB provides extensive information about various stress responsive genes and stress inducible transcription factors of Arabidopsis thaliana. STIFDB will be a useful resource for researchers to understand the abiotic stress regulome and transcriptome of this important model plant system.

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The expressions of proteins in the cell are carefully regulated by transcription factors that interact with their downstream targets in specific signal transduction cascades. Our understanding of the regulation of functional genes responsive to stress signals is still nascent. Plants like Arabidopsis thaliana, are convenient model systems to study fundamental questions related to regulation of the stress transcriptome in response to stress challenges. Microarray results of the Arabidopsis transcriptome indicate that several genes could be upregulated during multiple stresses, such as cold, salinity, drought etc. Experimental biochemical validations have proved the involvement of several transcription factors could be involved in the upregulation of these stress responsive genes. In order to follow the intricate and complicated networks of transcription factors and genes that respond to stress situations in plants, we have developed a computer algorithm that can identify key transcription factor binding sites upstream of a gene of interest. Hidden Markov models of the transcription factor binding sites enable the identification of predicted sites upstream of plant stress genes. The search algorithm, STIF, performs very well, with more than 90% sensitivity, when tested on experimentally validated positions of transcription factor binding sites on a dataset of 60 stress upregulated genes.

Availability

Supplementary data is available at http://caps.ncbs.res.in/download/stif.