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Massively parallel signature sequencing (MPSS) is a technology capable of sequencing simultaneously almost all the DNA molecules in a sample. This technology is well suited for deep profiling of mRNA and small RNA by the sequencing of cDNA tags. A series of mRNA MPSS databases has been created from various libraries derived from four different species (Arabidopsis, rice, grape, and Magnaporthe grisea, the rice blast fungus). Our mRNA MPSS databases measure the absolute expression level of most genes in the sample and provide information about potentially novel transcripts (antisense transcripts, alternative splice isoforms, and regulatory intergenic transcripts). In addition to these data, we have recently built an extensive database from MPSS-derived Arabidopsis small RNA samples. All the databases are accessible thorough our Web interface (http://mpss.udel.edu), and the individual pages are equipped with various graphical and analytical tools. Here, we focus on a subset of these tools (e.g., Gene Analysis [GA], Chromosome Viewer [CV], and Library Analysis [LIBAN]) and describe how the users can analyze and interpret our MPSS expression data. [ABSTRACT FROM AUTHOR]

The whole-genome sequence of rice (Oryza sativa subsp. japonica cv Nipponbare) has been sequenced and the annotation of this genome has been performed and continues to be updated and improved by several organizations (Yuan et al. 2003; IRGSP 2005). In this chapter, we used the fourth annotation of the rice genome released from TIGR (The Institute of Genomic Research) to analyze our massively parallel signature sequencing (MPSS) data. In this version (TIGR v4.0), 62,827 gene models or transcription units (TUs) were identified, including 4,734 genes with multiple splice isoforms. Among the 55,890 genes, 13,237 were annotated as transposable element (TE)-related genes, while the rest (42,653) were annotated as protein-coding genes or non-TE genes. [ABSTRACT FROM AUTHOR]

Massively parallel signature sequencing is a sequencing-based method that provides quantitative gene expression data for nearly all transcripts in a particular ribonucleic acid sample. Although the sequencing technology is practiced as a service by a California-based company, we have developed methods for the handling and analysis of these data. This chapter describes the steps involved in obtaining data from massively parallel signature sequencing, aligning the signatures to genomic sequence, identifying novel transcripts, and performing quantitative analyses of genes expressed under conditions such as disease treatments. [ABSTRACT FROM AUTHOR]