Primera herramienta bioinformática escrita en Raku
Publicado: 2022-01-14 19:30 @854https://twitter.com/raku_lang/status/14 ... 2312814594
We are proud to annouce the first bioinformatics tool that is written in the Raku language and published in a peer-reviewed journal:
BepiTBR: T-B reciprocity enhances B cell epitope prediction
The ability to predict B cell epitopes is critical for biomedical research and many clinical applications. Investigators have observed the phenomenon of T-B reciprocity, in which candidate B cell epitopes with nearby CD4+ T-cell epitopes have higher chances of being immunogenic. To our knowledge, existing B cell epitope prediction algorithms have not considered this interesting observation. We developed a linear B cell epitope prediction model, BepiTBR, based on T-B reciprocity. We showed that explicitly including the enrichment of putative CD4+ T-cell epitopes (predicted HLA class II epitopes) in the model leads to significant enhancement in the prediction of linear B cell epitopes. Curiously, the positive impact on B cell epitope generation is specific to the enrichment of DQ allele binders. Overall, our work provides interesting mechanistic insights into the generation of B cell epitopes and points to a new avenue to improve B cell epitope prediction for the field.
(La referencia aparece en la página 23 del PDF, como una de las herramientas utilizadas para el análisis estadístico).
We are proud to annouce the first bioinformatics tool that is written in the Raku language and published in a peer-reviewed journal:
BepiTBR: T-B reciprocity enhances B cell epitope prediction
The ability to predict B cell epitopes is critical for biomedical research and many clinical applications. Investigators have observed the phenomenon of T-B reciprocity, in which candidate B cell epitopes with nearby CD4+ T-cell epitopes have higher chances of being immunogenic. To our knowledge, existing B cell epitope prediction algorithms have not considered this interesting observation. We developed a linear B cell epitope prediction model, BepiTBR, based on T-B reciprocity. We showed that explicitly including the enrichment of putative CD4+ T-cell epitopes (predicted HLA class II epitopes) in the model leads to significant enhancement in the prediction of linear B cell epitopes. Curiously, the positive impact on B cell epitope generation is specific to the enrichment of DQ allele binders. Overall, our work provides interesting mechanistic insights into the generation of B cell epitopes and points to a new avenue to improve B cell epitope prediction for the field.
(La referencia aparece en la página 23 del PDF, como una de las herramientas utilizadas para el análisis estadístico).