RNA sequencing techniques are used to determine the sequence of nucleotide bases, adenine (A), cytosine (C), guanine (G) and uracil (U) in RNA molecules.
RNA-Seq delivers an unbiased and unprecedented high-resolution view of the global transcriptional landscape, which allows an affordable and accurate approach for gene expression quantification and differential gene expression analysis between multiple groups of samples. RNA-Seq can identify novel and previously-unexpected transcripts without the need for a reference genome, allowing de novo assembly of new transcriptome that is not previously studied before. It also enables the discovery of novel gene structures, alternatively spliced isoforms, gene fusions, SNPs/InDel, and allele-specific expression (ASE).
i. RNA-Seq is a sensitive tool for gene expression profiling. Compared to microarray, RNA-Seq offers a digital read that is more accurate for all gene expression.
The authors found 1089 genes differentially expressed between the CLL and normal B cells (Table 1). As was expected, the most differentially expressed genes are immunoglobulins due to the clonality of the CLL cells. Pathway analyses revealed that genes involved in metabolic pathways had higher expression in CLL, while genes related to splicesome, proteasome, and ribosome were substantially down-regulated in CLL.
Figure 1. CLL transcriptional landscape. (A) The coding potential of differentially expressed genes between the CLL and normal samples. (B) Normalized expression of transposable elements (TEs). (C) Genes with condition-specific splicing ratios. (D) Allele-specific expression of somatic mutations.
Figure 4. Major transcriptional CLL subgroups. (A) Clustering of CLL and normal samples. (B) Consensus cluster. (C) Multidimensional scaling of CLL and normal samples based on gene expression. (D&E) Enrichment score plot.