dc.date.accessioned | 2022-10-28T15:31:01Z | |
dc.date.available | 2022-10-28T15:31:01Z | |
dc.date.created | 2022-01-29T07:49:19Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Dahal-Koirala, Shiva Balaban, Gabriel Neumann, Ralf Stefan Scheffer, Lonneke Lundin, Knut Greiff, Victor Sollid, Ludvig Magne Qiao, Shuo-Wang Sandve, Geir Kjetil Ferkingstad . TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences. Briefings in Bioinformatics. 2022 | |
dc.identifier.uri | http://hdl.handle.net/10852/97392 | |
dc.description.abstract | 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 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. | |
dc.language | EN | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences | |
dc.title.alternative | ENEngelskEnglishTCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences | |
dc.type | Journal article | |
dc.creator.author | Dahal-Koirala, Shiva | |
dc.creator.author | Balaban, Gabriel | |
dc.creator.author | Neumann, Ralf Stefan | |
dc.creator.author | Scheffer, Lonneke | |
dc.creator.author | Lundin, Knut | |
dc.creator.author | Greiff, Victor | |
dc.creator.author | Sollid, Ludvig Magne | |
dc.creator.author | Qiao, Shuo-Wang | |
dc.creator.author | Sandve, Geir Kjetil Ferkingstad | |
cristin.unitcode | 185,53,18,12 | |
cristin.unitname | Immunologi og transfusjonsmedisin | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |
dc.identifier.cristin | 1993048 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Briefings in Bioinformatics&rft.volume=&rft.spage=&rft.date=2022 | |
dc.identifier.jtitle | Briefings in Bioinformatics | |
dc.identifier.volume | 23 | |
dc.identifier.issue | 2 | |
dc.identifier.pagecount | 14 | |
dc.identifier.doi | https://doi.org/10.1093/bib/bbab566 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 1467-5463 | |
dc.type.version | PublishedVersion | |
cristin.articleid | bbab566 | |
dc.relation.project | SIGMA2/NN9603K, NS9603K | |