Biology subjects

Teraguchi, S.

Publications and source records attributed to Teraguchi, S..

3 recordsLinked to original sources

bioRxiv2022

InterClone: Store, Search and Cluster Adaptive Immune Receptor Repertoires

B and T cell receptor repertoire data has the potential to fundamentally change the way we diagnose and treat a wide range of diseases. However, there are few resources for storing or analyzing repertoire data. InterClone provides tools for storing, searching, and clustering repertoire datasets. Efficiency is achieved by encoding the complementarity-determining regions of sequences as mmseqs2 databases. Single chain search or cluster results can be merged into paired (alpha-beta or heavy-light) results for analysis of single-cell sequencing data. We illustrate the use of InterClone with two recently reported examples: 1) searching for SARS-CoV-2 infection-enhancing antibodies in bulk COVID-19 and healthy donor repertoires; 2) identification of SARS-CoV-2 specific TCRs by clustering paired and bulk sequences from COVID-19, BNT162b2 vaccinated and healthy unvaccinated donors. The core functions of InterClone have been implemented as a web server and integrated database (https://sysimm.org/interclone). All source code is available upon request.

bioinformatics↗

bioRxiv2022

Improved antibody-specific epitope prediction using AlphaFold and AbAdapt

Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody-antigen specific features for epitope prediction. In this study, we assess the impact of integrating the state-of-the-art protein modeling method AlphaFold with the AbAdapt pipeline and demonstrate significant improvement in the prediction of antibody-specific epitope residues.

bioinformatics↗

bioRxiv2021

Unbiased integration of single cell transcriptomes using a linear hybrid method

Single cell transcriptomic approaches are becoming mainstream, with replicate experiments commonly performed with the same single cell technology. Methods that enable integration of these datasets by removing batch effects while preserving biological information are required for unbiased data interpretation. Here we introduce Canek for this purpose. Canek leverages information from mutual nearest neighbor to combine local linear corrections with cell-specific non-linear corrections within a fuzzy logic framework. Using a combination of real and synthetic datasets, we show that Canek corrects batch effects while introducing the least amount of bias compared with competing methods. Canek is computationally efficient and can easily integrate thousands of single-cell transcriptomes from replicated experiments.

bioinformatics↗