Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.05.370312v1?rss=1 Authors: Ehrlich, R., Kamga, L., Gil, A., Luzuriaga, K., Selin, L., Ghersi, D. Abstract: Motivation: Computationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to tackle this problem is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance. Results: We compared the performance of SwarmTCR against a state-of-the-art method (TCRdist) and showed that SwarmTCR performed significantly better on epitopes EBV-BRLF1, NS4B with single cell data and epitopes EBV-BRLF1, IAV-M1 with bulk sequencing data (alpha and beta chains). In addition, we show that the weights returned by SwarmTCR are biologically interpretable. Availability: SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub (https://github.com/thecodingdoc/SwarmTCR). Copy rights belong to original authors. Visit the link for more info