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CHAPTER 9: Sequence Based Gene Expression Analysis

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Simplified representation of sample processing for next generation sequencing of digital gene expression. RNA-Seq methods use mechanical or chemical shearing to generate appropriate fragment sizes for sequencing, resulting in potentially full transcript coverage. The second method uses restriction cleavage to generate fragments that more closely represent one read per transcript from the sample
General flow chart of DGE data analysis. DGE sequence reads are filtered to remove low quality sequences and clustered to identify unique reads. The resulting set of reads are mapped to a combination of genomic sequences and various known genic sequences to provide identification and, in the case of RNA-seq data, to group them into defined transcripts. Reads that do not map to known exonic regions are merged into existing gene models if in appropriate proximity, or used to construct novel gene models. Mapped read abundances are used to generate normalized values for their respective assigned genes either by percentage or by concentration depending on the experiment type. Normalized expression values are then available for use in a variety of analyses to further investigate the transcriptome of the sample being studied
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