#Clc sequence viewer restriction map driver
High-throughput genomics approaches including amplicon re-sequencing, exome re-sequencing, full genome re-sequencing, and SNP arrays have contributed to cataloguing driver genes across cancer types. The quest for somatic mutations underlying oncogenic processes is a central theme in today's cancer research. In conclusion, we present novel analysis pipelines for variant calling, variant filtering, and expression normalization on RNA-seq data, and successfully applied these for the detection of translocations, point mutations, INDELs, exon-skipping events, and expression perturbations in T-ALL. Finally, we detected gene fusions, of which several can explain the over-expression of key driver genes such as TLX1, PLAG1, LMO1, or NKX2-1 and others result in novel fusion transcripts encoding activated kinases ( SSBP2-FER and TPM3-JAK2) or involving MLLT10. Next, we determined accurate gene expression levels from the RNA-seq data through normalizations and batch effect removal, and used these to classify patients into T-ALL subtypes. We identified known driver genes with recurrent protein altering variations, as well as several new candidates including H3F3A, PTK2B, and STAT5B. First, we optimized the detection of SNVs in RNA-seq data by comparing the results with exome re-sequencing data. We analyzed 31 T-ALL patient samples and 18 T-ALL cell lines by high-coverage paired-end RNA-seq. These leukemias are caused by a combination of gene fusions, over-expression of transcription factors and cooperative point mutations in oncogenes and tumor suppressor genes. We asked whether RNA-seq is suitable for the detection of driver mutations in T-cell acute lymphoblastic leukemia (T-ALL). RNA-seq is a promising technology to re-sequence protein coding genes for the identification of single nucleotide variants (SNV), while simultaneously obtaining information on structural variations and gene expression perturbations.
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