Lonneke Scheffer

• Kanduri, Chakravarthi; Scheffer, Lonneke; Pavlović, Milena; Rand, Knut Dagestad; Chernigovskaia, Maria & Pirvandy, Oz [Show all 9 contributors for this article] (2023). simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods. GigaScience. ISSN 2047-217X. 12, p. 1–16. doi: 10.1093/gigascience/giad074. Full text in Research Archive
• Robert, Philippe Paul Auguste; Akbar, Rahmad; Frank, Robert; Pavlović, Milena; Widrich, Michael & Snapkov, Igor [Show all 25 contributors for this article] (2022). Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nature Computational Science. ISSN 2662-8457. 2(12), p. 845–865. doi: 10.1038/s43588-022-00372-4.
• Rognes, Torbjørn; Scheffer, Lonneke; Greiff, Victor & Sandve, Geir Kjetil (2022). CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching. Bioinformatics. ISSN 1367-4803. 38(17), p. 4230–4232. doi: 10.1093/bioinformatics/btac505. Full text in Research Archive
• Kanduri, Chakravarthi; Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Chernigovskaia, Maria & Greiff, Victor [Show all 7 contributors for this article] (2022). Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification. GigaScience. ISSN 2047-217X. 11(05). doi: 10.1093/gigascience/giac046. Full text in Research Archive
• Akbar, Rahmad; Robert, Philippe Paul Auguste; Weber, Cédric R.; Widrich, Michael; Frank, Robert & Pavlovic, Milena [Show all 19 contributors for this article] (2022). In silico proof of principle of machine learning-based antibody design at unconstrained scale. mAbs. ISSN 1942-0862. 14:e2031482(1), p. 1–18. doi: 10.1080/19420862.2022.2031482. Full text in Research Archive
• Dahal-Koirala, Shiva; Balaban, Gabriel; Neumann, Ralf Stefan; Scheffer, Lonneke; Lundin, Knut & Greiff, Victor [Show all 9 contributors for this article] (2022). TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences. Briefings in Bioinformatics. ISSN 1467-5463. 23(2), p. 1–14. doi: 10.1093/bib/bbab566. Full text in Research Archive Show summary

  T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5′ RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.

• Slabodkin, Andrei; Chernigovskaia, Maria; Mikocziova, Ivana; Akbar, Rahmad; Scheffer, Lonneke & Pavlović, Milena [Show all 16 contributors for this article] (2021). Individualized VDJ recombination predisposes the available Ig sequence space. Genome Research. ISSN 1088-9051. 31(12), p. 2209–2225. doi: 10.1101/gr.275373.121. Full text in Research Archive
• Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Kanduri, Chakravarthi; Kompova, Radmila & Vazov, Nikolay Aleksandrov [Show all 41 contributors for this article] (2021). The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nature Machine Intelligence. 3(11), p. 936–944. doi: 10.1038/s42256-021-00413-z.
• Akbar, Rahmad; Robert, Philippe Paul Auguste; Pavlović, Milena; Jeliazkov, Jeliazko R.; Snapkov, Igor & Slabodkin, Andrei [Show all 15 contributors for this article] (2021). A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Cell reports. ISSN 2211-1247. 34:108856(11), p. 1–21. doi: 10.1016/j.celrep.2021.108856. Full text in Research Archive

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• Torgersen, Eivind; Geir Kjetil, Sandve; Greiff, Victor; Pavlovic, Milena & Scheffer, Lonneke (2022). Fra bare én blodprøve kan kunstig intelligens gi diagnose for mange ulike sykdommer. Titan.uio.no.

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