Name
Abstract | ||
|---|---|---|
Background: With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to predict T cell receptor specificity is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity ofT 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 another nearest-neighbor method and showed that SwarmTCR performs well both with bulk sequencing data and with single cell data. In addition, we show that the weights returned by SwarmTCR are biologically interpretable.Conclusions: Computationally predicting the specificity ofT cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. 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/Swarm TCR). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1186/s12859-021-04335-w | BMC BIOINFORMATICS |
Keywords | DocType | Volume |
TCR, Immunoinformatics, Binding specificity | Journal | 22 |
Issue | ISSN | Citations |
1 | 1471-2105 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan Ehrlich | 1 | 0 | 0.34 |
| Larisa Kamga | 2 | 0 | 0.34 |
| Anna Gil | 3 | 0 | 0.34 |
| Katherine Luzuriaga | 4 | 0 | 0.34 |
| Liisa K Selin | 5 | 0 | 0.34 |
| Dario Ghersi | 6 | 66 | 7.04 |