Transcriptome data sets

These new omics technologies will also enable us to efficiently identify novel compounds of value to both the Agrochemical and Pharmaceutical industries. It is of crucial importance that the data that results from high throughput analyses of the genome, transcriptome, proteome and metabolome of crop plants and foeir relatives is archived in an appropriately structured and indexed manner. This will enable it to be fully integrated and data-mined over an extensive period as our ability to formulate increasingly sophisticated queries on these data sets develops. 

A new subdiscipline of bioinformatics, comparative proteogenomics, is based on the possibility of mapping sets of sequenced peptides onto the positions in the genome that code for them, i.e., integration of data from MS-based proteomics with DNA sequence data sets. Databases and software to determine if the available data are over- or underrepresented include Gene Ontology, protein domains, and pathway databases. Another application (of many) is the combination of MS-based proteomic data with information from transcriptomics to study the processes and regulation of cellular functions. 

RADPRE was designed to use the transcriptome data from tilling arrays [5]. The underlining principle of how the tiling data might be employed to identify the differential APA events was illustrated in Fig. 1 and detailed previously [5]. The RADPRE pipeline constituted three major steps. In the first step, RADPRE took a set of tiling array data as input and calculated the ratios of normalized probe intensities between two conditions (Fig. 2a). 

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