RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e. g., tissues, perturbations), while optionally adjusting for other systematic factors that affect the data collection process. There are a number of subtle yet critical aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a "state-of-the-art" computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and in particular, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e. g., 4-10 samples) can be <1 hour, with computation time <1 day using a standard desktop PC.

Giant congenital naevi are pigmented childhood lesions that frequently lead to melanoma, the most aggressive skin cancer. The mechanisms underlying this malignancy are largely unknown, and there are no effective therapies. Here we describe a mouse model for giant congenital naevi and show that naevi and melanoma prominently express Sox10, a transcription factor crucial for the formation of melanocytes from the neural crest. Strikingly, Sox10 haploinsufficiency counteracts Nras(Q61K)-driven congenital naevus and melanoma formation without affecting the physiological functions of neural crest derivatives in the skin. Moreover, Sox10 is also crucial for the maintenance of neoplastic cells in vivo. In human patients, virtually all congenital naevi and melanomas are SOX10 positive. Furthermore, SOX10 silencing in human melanoma cells suppresses neural crest stem cell properties, counteracts proliferation and cell survival, and completely abolishes in vivo tumour formation. Thus, SOX10 represents a promising target for the treatment of congenital naevi and melanoma in human patients.

Nonsense-mediated mRNA decay (NMD) is traditionally portrayed as a quality-control mechanism that degrades mRNAs with truncated open reading frames (ORFs). However, it is meanwhile clear that NMD also contributes to the post-transcriptional gene regulation of numerous physiological mRNAs. To identify endogenous NMD substrate mRNAs and analyze the features that render them sensitive to NMD, we performed transcriptome profiling of human cells depleted of the NMD factors UPF1, SMG6, or SMG7. It revealed that mRNAs up-regulated by NMD abrogation had a greater median 39-UTR length compared with that of the human mRNAome and were also enriched for 39-UTR introns and uORFs. Intriguingly, most mRNAs coding for NMD factors were among the NMD-sensitive transcripts, implying that the NMD process is autoregulated. These mRNAs all possess long 39 UTRs, and some of them harbor uORFs. Using reporter gene assays, we demonstrated that the long 39 UTRs of UPF1, SMG5, and SMG7 mRNAs are the main NMD-inducing features of these mRNAs, suggesting that long 39 UTRs might be a frequent trigger of NMD.

There are a number of leukemogenic protein-tyrosine kinases (PTKs) associated with leukemic transformation. Although each is linked with a specific disease their functional activity poses the question whether they have a degree of commonality in their effects upon target cells. Exon array analysis of the effects of six leukemogenic PTKs (BCR/ABL, TEL/PDGFR␤, FIP1/PDGFR␣, D816V KIT, NPM/ALK, and FLT3ITD) revealed few common effects on the transcriptome. It is apparent, however, that proteome changes are not directly governed by transcriptome changes. Therefore, we assessed and used a new generation of iTRAQ tagging, enabling eight-channel relative quantification discovery proteomics, to analyze the effects of these six leukemogenic PTKs. Again these were found to have disparate effects on the proteome with few common targets. BCR/ABL had the greatest effect on the proteome and had more effects in common with FIP1/PDGFR␣. The proteomic effects of the four type III receptor kinases were relatively remotely related. The only protein commonly affected was eosinophil-associated ribonuclease 7. Five of six PTKs affected the motility-related proteins CAPG and vimentin, although this did not correspond to changes in motility. However, correlation of the proteomics data with that from the exon microarray not only showed poor levels of correlation between transcript and protein levels but also revealed alternative patterns of regulation of the CAPG protein by different oncogenes, illustrating the utility of such a combined approach. Molecular & Cellular Proteomics 7: 853-863, 2008.