@article {28301,
	title = {TCRPower: Quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences},
	journal = {Briefings in Bioinformatics},
	volume = {23},
	year = {2022},
	month = {01/2022},
	pages = {bbab566},
	publisher = {Oxford Academic},
	abstract = {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 mis-diagnosis 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 cut-off. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones as spike-in TCRs. We sequenced the spike-in TCRs from T-cell clones, together with TCRs from peripheral blood, using a 5{\textquoteright} 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 cut-off, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0\%, 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.\&nbsp;},
	keywords = {Adaptive Immune Receptor Repertoire sequencing, Bulk T-cell receptor sequencing, computational model, Spike-in standards, T-cell receptor, TCRpower},
	doi = {https://doi.org/10.1093/bib/bbab566},
	author = {Dahal-Koirala, Shiva and Balaban, Gabriel and Ralf Stefan {Neumann} and Scheffer, Lonneke and Knut Erik Aslak {Lundin} and Greiff, Victor and Ludvig Magne {Sollid} and Qiao, Shuo-Wang and Sandve, Geir-Kjetil}
}
@article {28310,
	title = {The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires},
	journal = {Nature Machine Intelligence},
	volume = {3},
	year = {2021},
	month = {Nov/2021},
	pages = {936 - 944},
	publisher = {Nature Publishing},
	doi = {10.1038/s42256-021-00413-z},
	url = {https://www.nature.com/articles/s42256-021-00413-zhttps://www.nature.com/articles/s42256-021-00413-z.pdfhttps://www.nature.com/articles/s42256-021-00413-z.pdfhttps://www.nature.com/articles/s42256-021-00413-z},
	author = {Pavlovi{\'c}, Milena and Scheffer, Lonneke and Motwani, Keshav and Kanduri, Chakravarthi and Kompova, Radmila and Vazov, Nikolay and Waagan, Knut and Fabian L. M. {Bernal} and Alexandre Almeida {Costa} and Corrie, Brian and Akbar, Rahmad and Ghadi S. Al {Hajj} and Balaban, Gabriel and Todd M. {Brusko} and Chernigovskaya, Maria and Christley, Scott and Lindsay G. {Cowell} and Frank, Robert and Grytten, Ivar and Gundersen, Sveinung and Ingrid Hob{\ae}k {Haff} and Hovig, Eivind and Hsieh, Ping-Han and Klambauer, G{\"u}nter and Marieke L. {Kuijjer} and Lund-Andersen, Christin and Martini, Antonio and Minotto, Thomas and Pensar, Johan and Rand, Knut and Riccardi, Enrico and Philippe A. {Robert} and Rocha, Artur and Slabodkin, Andrei and Snapkov, Igor and Ludvig M. {Sollid} and Titov, Dmytro and C{\'e}dric R. {Weber} and Widrich, Michael and Yaari, Gur and Greiff, Victor and Geir Kjetil {Sandve}}
}
@article {27949,
	title = {Ten simple rules for quick and dirty scientific programming},
	journal = {PLOS Computational Biology},
	volume = {17},
	year = {2021},
	month = {Mar-11-2021},
	pages = {e1008549},
	publisher = {PLOS},
	url = {https://dx.plos.org/10.1371/journal.pcbi.1008549},
	author = {Balaban, Gabriel and Grytten, Ivar and Knut Dagestad {Rand} and Scheffer, Lonneke and Geir Kjetil {Sandve}}
}
