Identification, assembly and characterization of tumor immunoglobulin transcripts from RNA sequencing data using IgSeqR.

Abstract

Immunoglobulin gene analysis provides fundamental insight into B cell receptor structure and function. In B cell tumors, it can provide information on the cell of origin and predict clinical outcomes. Its clinical value has been established in the two main types of chronic lymphocytic leukemia, which are distinguished by the expression of unmutated or mutated immunoglobulin heavy chain variable region (IGHV) genes, and is emerging in other B cell tumors. The traditional PCR and Sanger sequencing-based techniques for immunoglobulin gene analysis are labor-intensive and rely on attaining either a dominant sequence or a small number of subclonal sequences. Extraction of the expressed tumor immunoglobulin transcripts by using high-throughput RNA-sequencing (RNA-seq) can be faster, allow the collection of the tumor immunoglobulin sequence and match this with the rest of the RNA-seq data. Analytical tools are regularly sought to increase the accuracy, depth and speed of acquisition of the immunoglobulin transcript sequences and combine the immunoglobulin characteristics with other tumor features. We provide here a user-friendly protocol for the rapid (~1 h) de novo assembly, identification and accurate characterization of the full (leader to constant region) tumor immunoglobulin templated and non-templated transcript sequence from RNA-seq data ( https://github.com/ForconiLab/IgSeqR ). The derived amino acid sequences can be interrogated for their physicochemical characteristics and, in certain lymphomas, be used to predict tumor glycan types occupying acquired N-glycosylation sites. These features will then be available for association studies with the tumor transcriptome. The resulting information can also help refine diagnosis, prognosis and potential therapeutic targeting in the most common lymphomas.