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These simulations can be further divided into smaller tasks, such as the input file generation by a so-called job generator, then the job submission to the DCI and the calculation by the QC code, which produces the corresponding output.
The details of the RNA-Seq data and BAM file generation can obtained from the TCGA consortia, which generated them, specifically in the supplementary methods of Koboldt et al. (2012).
The system consists of four modules: cross-sectional image measurement, 3D reconstruction, stereo lithography (STL) file generation, and RP slice file generation.
For rapid prototyping, an automated STL file generation algorithm is also presented in this paper.
Unfortunately, the rendering is broken, but the RIB file generation works fine.
A bioinformatic pipeline was constructed whereby the following steps were employed; fastQC, pre-processing, read alignment using Tophat2, htseq-count file generation and DESeq2 analysis.
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This pipeline has been structured in three analysis modules: (i) read filtering, FastQC quality control, bisulfite sequence alignment and methylation calling using Bismark, (ii) depth filtering and output files generation and (iii) an optional interval methylation percentages calculation (Fig. 1).
Additional file 2: Generation of missing data.
Additional file 3: Generation of Ppα-DOX-GUS lines.
Additional file 5: Generation of overexpressing and knockout Ppα-DOX lines.
All used primers are listed in Additional file 3. Generation of competent E. coli cells and subsequent transformation was performed according to standard protocols using CaCl2.
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