An overview of the analysis of next generation sequencing data.
Next generation sequencing is a common and versatile tool for biological and medical research. We describe the basic steps for analyzing next generation sequencing data, including quality checking and mapping to a reference genome. We also explain the further data analysis for three common applications of next generation sequencing: variant detection, RNA-seq, and ChIP-seq.
- Gogol-Döring A, Chen W. (2012) An overview of the analysis of next generation sequencing data. Methods Mol Biol 802, 249-57. [abstract]
How to Analyze Gene Expression Using RNA-Sequencing Data
RNA-Seq is arising as a powerful method for transcriptome analyses that will eventually make microarrays obsolete for gene expression analyses. Improvements in high-throughput sequencing and efficient sample barcoding are now enabling tens of samples to be run in a cost-effective manner, competing with microarrays in price, excelling in performance. Still, most studies use microarrays, partly due to the ease of data analyses using programs and modules that quickly turn raw microarray data into spreadsheets of gene expression values and significant differentially expressed genes. Instead RNA-Seq data analyses are still in its infancy and the researchers are facing new challenges and have to combine different tools to carry out an analysis. In this chapter, we provide a tutorial on RNA-Seq data analysis to enable researchers to quantify gene expression, identify splice junctions, and find novel transcripts using publicly available software. We focus on the analyses performed in organisms where a reference genome is available and discuss issues with current methodology that have to be solved before RNA-Seq data can utilize its full potential.
- Ramsköld D, Kavak E, Sandberg R. (2012) How to Analyze Gene Expression Using RNA-Sequencing Data. Methods Mol Biol 802, 259-74. [abstract]