Literature DB >> 32154832

immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking.

Cédric R Weber1, Rahmad Akbar2, Alexander Yermanos1, Milena Pavlović3, Igor Snapkov2, Geir K Sandve3, Sai T Reddy1, Victor Greiff2.   

Abstract

SUMMARY: B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full-length variable region immune receptor sequences by tuning the following immune receptor features: (i) species and chain type (BCR, TCR, single and paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis, such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis and machine learning methods for motif detection.
AVAILABILITY AND IMPLEMENTATION: The package is available via https://github.com/GreiffLab/immuneSIM and on CRAN at https://cran.r-project.org/web/packages/immuneSIM. The documentation is hosted at https://immuneSIM.readthedocs.io. CONTACT: sai.reddy@ethz.ch or victor.greiff@medisin.uio.no. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Year:  2020        PMID: 32154832     DOI: 10.1093/bioinformatics/btaa158

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  11 in total

Review 1.  Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

Authors:  Rahmad Akbar; Habib Bashour; Puneet Rawat; Philippe A Robert; Eva Smorodina; Tudor-Stefan Cotet; Karine Flem-Karlsen; Robert Frank; Brij Bhushan Mehta; Mai Ha Vu; Talip Zengin; Jose Gutierrez-Marcos; Fridtjof Lund-Johansen; Jan Terje Andersen; Victor Greiff
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

2.  Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data.

Authors:  Tommaso Andreani; Linda M Slot; Samuel Gabillard; Carsten Strübing; Claus Reimertz; Veeranagouda Yaligara; Aleida M Bakker; Reza Olfati-Saber; René E M Toes; Hans U Scherer; Franck Augé; Deimantė Šimaitė
Journal:  NAR Genom Bioinform       Date:  2022-07-13

3.  Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification.

Authors:  Chakravarthi Kanduri; Milena Pavlović; Lonneke Scheffer; Keshav Motwani; Maria Chernigovskaya; Victor Greiff; Geir K Sandve
Journal:  Gigascience       Date:  2022-05-25       Impact factor: 7.658

4.  Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information.

Authors:  James M Heather; Matthew J Spindler; Marta Herrero Alonso; Yifang Ivana Shui; David G Millar; David S Johnson; Mark Cobbold; Aaron N Hata
Journal:  Nucleic Acids Res       Date:  2022-07-08       Impact factor: 19.160

Review 5.  Immune Literacy: Reading, Writing, and Editing Adaptive Immunity.

Authors:  Lucia Csepregi; Roy A Ehling; Bastian Wagner; Sai T Reddy
Journal:  iScience       Date:  2020-09-01

6.  Novel Allele Detection Tool Benchmark and Application With Antibody Repertoire Sequencing Dataset.

Authors:  Xiujia Yang; Yan Zhu; Sen Chen; Huikun Zeng; Junjie Guan; Qilong Wang; Chunhong Lan; Deqiang Sun; Xueqing Yu; Zhenhai Zhang
Journal:  Front Immunol       Date:  2021-10-26       Impact factor: 7.561

7.  In silico proof of principle of machine learning-based antibody design at unconstrained scale.

Authors:  Rahmad Akbar; Philippe A Robert; Cédric R Weber; Michael Widrich; Robert Frank; Milena Pavlović; Lonneke Scheffer; Maria Chernigovskaya; Igor Snapkov; Andrei Slabodkin; Brij Bhushan Mehta; Enkelejda Miho; Fridtjof Lund-Johansen; Jan Terje Andersen; Sepp Hochreiter; Ingrid Hobæk Haff; Günter Klambauer; Geir Kjetil Sandve; Victor Greiff
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

Review 8.  T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer.

Authors:  Lucia Mazzotti; Anna Gaimari; Sara Bravaccini; Roberta Maltoni; Claudio Cerchione; Manel Juan; Europa Azucena-Gonzalez Navarro; Anna Pasetto; Daniela Nascimento Silva; Valentina Ancarani; Vittorio Sambri; Luana Calabrò; Giovanni Martinelli; Massimiliano Mazza
Journal:  Int J Mol Sci       Date:  2022-08-02       Impact factor: 6.208

Review 9.  Machine Learning Approaches to TCR Repertoire Analysis.

Authors:  Yotaro Katayama; Ryo Yokota; Taishin Akiyama; Tetsuya J Kobayashi
Journal:  Front Immunol       Date:  2022-07-15       Impact factor: 8.786

10.  An innovative and affordable quantitative assessment of human TCR repertoire.

Authors:  Roberta Amoriello; Clara Ballerini
Journal:  EBioMedicine       Date:  2020-10-20       Impact factor: 8.143

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