Chintalapati Janaki | Search PubMed

PubMed2004

Motif detection in Arabidopsis: correlation with gene expression data.

The genes having similar expression profiles are considered to have common regulatory mechanisms and are controlled by the binding of transcription factors to the regulatory elements present in their upstream regions. The detection of cis-regulatory elements can help in further understanding of co-expression of genes. This paper deals with the detection of motifs in the upstream regions of genes involved in diurnal rhythms of Arabidopsis and also deals with the correlation of expression data with sequence information. We detected motifs in the upstream regions of genes involved in diurnal cycles and checked for their presence in circadian regulated, dark induced and in light induced genes of Arabidopsis. Ten motifs were reported in this study, out of which five were already reported in available transcription factor databases as the elements involved in light responsiveness. Significance study of ten motifs was done by taking random sets of same data size. One of the ten motifs namely GGCCCA, which was found without any base variations in 62 genes, was further studied by analyzing the expression profiles of its respective genes within the set of diurnal regulated genes using SOM clustering method. It was found that the genes were clustered together into two major groups, out of which one group had glycine rich proteins and the second group had genes belonging to dehydrogenase and oxidoreductase family.

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PubMed2003

Accelerating comparative genomics using parallel computing.

In the past decade there has been an increase in the number of completely sequenced genomes due to the race of multibillion-dollar genome-sequencing projects. The enormous biological sequence data thus flooding into the sequence databases necessitates the development of efficient tools for comparative genome sequence analysis. The information deduced by such analysis has various applications viz. structural and functional annotation of novel genes and proteins, finding gene order in the genome, gene fusion studies, constructing metabolic pathways etc. Such study also proves invaluable for pharmaceutical industries, such as in silico drug target identification and new drug discovery. There are various sequence analysis tools available for mining such useful information of which FASTA and Smith-Waterman algorithms are widely used. However, analyzing large datasets of genome sequences using the above codes seems to be impractical on uniprocessor machines. Hence there is a need for improving the performance of the above popular sequence analysis tools on parallel cluster computers. Performance of the Smith-Waterman (SSEARCH) and FASTA programs were studied on PARAM 10000, a parallel cluster of workstations designed and developed in-house. FASTA and SSEARCH programs, which are available from the University of Virginia, were ported on PARAM and were optimized. In this era of high performance computing, where the paradigm is shifting from conventional supercomputers to the cost-effective general-purpose cluster of workstations and PCs, this study finds extreme relevance. Good performance of sequence analysis tools on a cluster of workstations was demonstrated, which is important for accelerating identification of novel genes and drug targets by screening large databases.

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