Date: Saturday, May 30, 2020
Session Time: 3:15pm-4:00pm
Presentation Time: 3:30pm-4:00pm
*Purpose: T and B cells are crucial parts of the human immune system and play an important role in transplantation. Since the mechanisms leading to a rejection are not entirely understood, we have developed a bioinformatics pipeline that facilitates the analysis of immunological next-generation sequencing (NGS) data of T cell receptor (TCR) and immunoglobulin (IG) repertoires. Using this pipeline, we are able to identify and analyze genetic factors that are associated with a higher chance of allograft rejection in transplant recipients.
*Methods: The pipeline is implemented in Python 3.7 and provides several methods for interpreting the clonality and the diversity of TCR and IG repertoires. Multiple NGS samples are automatically compared and analyzed with respect to shared clonotypes, similarities in variable (V) and joining (J) genes, and third complementarity-determining region (CDR3) amino acid sequence length. The pipeline aggregates the most important information about the samples in a single user-friendly report. In addition it allows to export features from the samples in formats commonly used by machine learning frameworks.
*Results: We show that our pipeline covers the whole process from raw sequencing data, through clonotype identification, to final comparison of characteristic measures of different immune repertoires. It serves as quality control, allows the analysis of immune repertoire NGS samples and provides researchers with results such as clonality and diversity measures, clonotype overlap, similarity in V(D)J usage and even alloreactive clonotypes if longitudinal samples are available. All results are summarized in a compact form and accompanied by publication ready figures.
*Conclusions: We have designed and implemented a bioinformatics pipeline that enables biological and medical scientists to analyze NGS data of TCR and IG repertoires from transplant patients. Through using descriptive statistics and machine learning approaches the pipeline can help to differentiate between patients experiencing a rejection event from non-rejectors.
To cite this abstract in AMA style:Vetter J, Heinzel A, Aschauer C, Reindl-Schwaighofer R, Jelencsics K, Hu K, Oberbauer R, Winkler S, Schaller S. A Bioinformatics Pipeline for the Identification of Crucial Factors for Transplant Rejection Based on NGS Data [abstract]. Am J Transplant. 2020; 20 (suppl 3). https://atcmeetingabstracts.com/abstract/a-bioinformatics-pipeline-for-the-identification-of-crucial-factors-for-transplant-rejection-based-on-ngs-data/. Accessed January 19, 2021.
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