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the data and the formatting of images. The main input consists of a promoter activity
profile in the form of a tab-delimited text file. To perform background subtraction, an
additional file with OD values must be uploaded. The first pre-processing step
involves data smoothing by user-defined averaging of the values from neighbouring
time-points. Any negative values can be set to zero. If the dataset contains experi-
mental or technical replicates, HMG can merge these using either mean or median
values. In the latter case, the user can also opt to keep the values of the clone that is in
general closest to the median, that is, a preferred clone is determined automatically
by HMG. The final processing step includes the scaling of all values to a range from
(0-1). The intermediate output after each data transformation step can be inspected
and downloaded. The processed data are then visualized as an online HTML table
where the cells are coloured according to the promoter activity values. This table
is presented through the online-spreadsheet part of ArrayPipe ( Hokamp et al. ,
2004 ), a microarray data analysis tool. It allows colours to be changed as well as
the filtering and sorting of columns. This is particularly useful after a cluster analysis,
which is available within ArrayPipe as an integrated module. Users can also upload
annotation information for attachment to the table. Once the output is in the desired
format, a heatmap can be generated in PNG or scalable PDF file formats ( Figure 1.4 ).
The web-page for generating the image can be password-protected, bookmarked and
shared with others. Detailed user documentation is available online.
8 CONCLUSIONS
The systems approach attempts to describe and understand biological systems in a
global and interactive manner. This approach has emerged from new technological
capabilities such as next-generation sequencing, microarrays, RNAseq and high-
throughput analysis of the promoter fusions (live cell arrays) to establish transcrip-
tomes, promoter activity profiles, proteomes, interactomes and metabolomes. Here,
we have described the capabilities and uses of gene fusion technology to determine
promoter activity with high temporal resolution on a global scale. The functionality,
reproducibility and low cost of this technology means it can be widely adapted and
applied. The resultant global and dynamic view has revealed several novel regulatory
features of gene expression in E. coli and B. subtilis .When such analyses are integrated
with other global analyses, a very detailed description of the behaviour of a biological
systemcan be established ( Buescher et al. , 2012 ). Clearly, this technologywill make a
seminal contribution towards formulating dynamic gene expression models and the
development of a more global view of how biological systems function.
Acknowledgements
Research in the Devine laboratory is supported by Science Foundation Ireland
Principal Investigator Award 08/IN.1/B1859. Development work on live cell arrays
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