RNA-seq Technical Guide
Table of Contents
Letter from the Editor
Index of Experts
Q1: What are the advantages of using RNA-seq?
Q2: What RNA purification method do you use, and why?
Q3: What library preparation method do you use, and why?
Q4: What strategies do you use to improve dynamic range?
Q5: What quality-control steps do you include?
Q6: What tools do you use, and why, to analyze your RNA-seq data?
List of Resources
http://main.g2.bx.psu.edu/), e.g. assigning tags to different gene models, etc. After tags are assigned to gene models, the data can be viewed and analyzed in all of the standard ways we are used to for microarray experiments (such as Gene Pattern, Bioconductor, or GeneSpring). The output of RNA-MATE can be used to view the genomic context of gene expression in the UCSC Genome Browser. Other analyses are often done by custom bioinformatics on an as-needed basis.
— Nicole Cloonan
For read-mapping, we use ELAND written by Illumina and seqMap written by our lab. For quality control and gene expression calculation, we use rSEQ, a tool written by our lab. For data visualization, we use UCSC Genome Browser written by UCSC and CisGenome browser written by our lab. We also use TopHat written by the University of Maryland and our in-house tool SpliceMap to detect novel splicing junctions. For all other possible analyses depending on the experiment, we write programs using Linux shell script, python, Matlab, R, and C++. We use our own tools mostly because they are easier to control and also because right now there are not many tools for RNA-seq analysis out there.
— Hui Jiang
We use a variety of tools including both published open-source algorithms and programs developed in-house. For read mapping, we use both MAQ and BWA, which perform well with colorspace data. For expression analysis and visualization, we rely entirely on scripts and tools developed in-house. Although there are some commercial software packages already available to deal with some of these aspects, because RNA-seq data is so rich, it will be difficult for a single software package to cover ever possible types of analysis that researchers might want to do. Developing an in-house analysis pipeline also has the advantage that individual algorithms can be replaced when better ones are developed or additional (possibly project-specific) software can be seamlessly integrated.
— Brian Wilhelm
UCSC Genome Browser