There are several downstream analysis goals for which RNA-seq is well suited. Main categories of these are described briefly below with reference to supporting materials. Refer to S2 Table for specific tools relevant to many of these areas. For each application, a basic data recommendation is provided. It is important to remember that these are simply examples. In addition to the varying demand...

Table 1. Technology parameters for CAZM 0:5m CMOS drivers 18]. Table 2. Technology parameters based on (a) MCM10 technology 5] and (b) CAZM 0:5m CMOS model 18]. Table 3. Average signal delay (ns) for (a) 1-critical-sink net and (b) multi-critical-sinks net under the MCM10 technology. Table 4. Average signal delay (ns) for (a) 1-critical-sink net and (b) multi-critical-sinks net under the CAZM 0...

All tools used in the online tutorial (www.rnaseq.wiki) are referenced below (in bold) along with alternative tools in each category. Where possible a citation is provided. Links are also provided to help the user evaluate the code and the level of maintenance. Where possible the link goes directly to a source controlled repository such as a git repo. Additional lists of tools can be found here...

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A table cartogram of a two dimensional m × n table A of non-negative weights in a rectangle R, whose area equals the sum of the weights, is a partition of R into convex quadrilateral faces corresponding to the cells of A such that each face has the same adjacency as its corresponding cell and has area equal to the cell’s weight. Such a partition acts as a natural way to visualize table data ari...

Yes. RNA-seq can be used to quantify transcript levels from a sample. In order to perform useful statistics, one sample is insufficient. Replicates must be used to appropriately power such statistics. The RNA-seq method is an impressive advancement with many applications for studying RNA biology but it does not eliminate biological variability. If the input samples are heavily degraded or have ...