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专业背景:计算机科学 研究方向与兴趣: JavaEE-Web软件开发, 生物信息学, 数据挖掘与机器学习, 智能信息系统 目前工作: 基因组, 转录组, NGS高通量数据分析, 生物数据挖掘, 植物系统发育和比较进化基因组学

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RNA-seq Technical Guide  

2009-11-24 22:49:36|  分类: 生物信息学 |  标签: |举报 |字号 订阅

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RNA-seq Technical Guide

http://www.genomeweb.com/node/926779

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

http://www.bioconductor.org/

BWA

http://bio-bwa.sourceforge.net/bwa.shtml

CisGenome

http://www.biostat.jhsph.edu/~hji/cisgenome/index.htm

GenePattern

http://www.broadinstitute.org/cancer/software/genepattern/

GeneSpring

http://main.g2.bx.psu.edu/

MAQ

http://maq.sourceforge.net/

UCSC Genome Browser

http://genome.ucsc.edu/

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