The main aim of the RNA-seq analysis is to detect specific gene expression patterns which can be correlated with peptide sequences and clinical parameters. These expression patterns will give clues to cell surface receptor expression and intracellular pathway activity. Patients will be divided based on the peptide sequences internalized by B-CLL cells, and a statistical test will be performed in order to detect differentially expressed genes. Pathway analysis will be performed in order to infer functional changes which may shade a light on the possible role of the peptide receptors in the cell signaling cascade. Trim galore software will be used for adapter trimming, and for removing low quality bases from the ends of reads. Trimmed reads will be mapped to the human genome (hg38) using TopHat2 software. The number of reads overlaping each of the annotated genes will be counted using the HT-seq python package. DESeq within the Bioconductor framework will be used for normalization and differential expression analysis using variants of Fisher's exact test. The Generally Applicable Gene-set Enrichment (GAGE) method will be applied to detect up and down-regulated pathways. RNA-seq is also used to detect fusion gene events in the patient's cancer cells and can be used as an independent validation method to test the sensitivity of the peptide-dye conjugate used as for disease monitoring. For this aim, three different tools TopHat-fusion, defuse and ChimeraScan will be used to identify fusion transcripts. Several filtration steps will be applied in order to remove low-quality candidates as described before, and only those candidates detected by all the three tools will be chosen for RT-PCR validation.