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dc.date.accessioned2021-02-15T18:57:26Z
dc.date.available2021-02-15T18:57:26Z
dc.date.created2020-05-21T17:25:49Z
dc.date.issued2020
dc.identifier.citationSmakaj, Erand Babrak, Lmar Ohlin, Mats Shugay, Mikhail Briney, Bryan Tosoni, Deniz Galli, Christopher Grobelsek, Vendi D'Angelo, Igor Olson, Branden Reddy, Sai T. Greiff, Victor Truck, Johannes Marquez, Susanna Lees, William D. Miho, Enkelejda . Benchmarking immunoinformatic tools for the analysis of antibody repertoire sequences. Bioinformatics. 2020, 36(6), 1731-1739
dc.identifier.urihttp://hdl.handle.net/10852/83279
dc.description.abstractAbstract Summary Antibody repertoires reveal insights into the biology of the adaptive immune system and empower diagnostics and therapeutics. There are currently multiple tools available for the annotation of antibody sequences. All downstream analyses such as choosing lead drug candidates depend on the correct annotation of these sequences; however, a thorough comparison of the performance of these tools has not been investigated. Here, we benchmark the performance of commonly used immunoinformatic tools, i.e. IMGT/HighV-QUEST, IgBLAST and MiXCR, in terms of reproducibility of annotation output, accuracy and speed using simulated and experimental high-throughput sequencing datasets. We analyzed changes in IMGT reference germline database in the last 10 years in order to assess the reproducibility of the annotation output. We found that only 73/183 (40%) V, D and J human genes were shared between the reference germline sets used by the tools. We found that the annotation results differed between tools. In terms of alignment accuracy, MiXCR had the highest average frequency of gene mishits, 0.02 mishit frequency and IgBLAST the lowest, 0.004 mishit frequency. Reproducibility in the output of complementarity determining three regions (CDR3 amino acids) ranged from 4.3% to 77.6% with preprocessed data. In addition, run time of the tools was assessed: MiXCR was the fastest tool for number of sequences processed per unit of time. These results indicate that immunoinformatic analyses greatly depend on the choice of bioinformatics tool. Our results support informed decision-making to immunoinformaticians based on repertoire composition and sequencing platforms. Availability and implementation All tools utilized in the paper are free for academic use. Supplementary information Supplementary data are available at Bioinformatics online.
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleBenchmarking immunoinformatic tools for the analysis of antibody repertoire sequences
dc.typeJournal article
dc.creator.authorSmakaj, Erand
dc.creator.authorBabrak, Lmar
dc.creator.authorOhlin, Mats
dc.creator.authorShugay, Mikhail
dc.creator.authorBriney, Bryan
dc.creator.authorTosoni, Deniz
dc.creator.authorGalli, Christopher
dc.creator.authorGrobelsek, Vendi
dc.creator.authorD'Angelo, Igor
dc.creator.authorOlson, Branden
dc.creator.authorReddy, Sai T.
dc.creator.authorGreiff, Victor
dc.creator.authorTruck, Johannes
dc.creator.authorMarquez, Susanna
dc.creator.authorLees, William D.
dc.creator.authorMiho, Enkelejda
cristin.unitcode185,53,18,12
cristin.unitnameAvdeling for immunologi og transfusjonsmedisin
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1812070
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Bioinformatics&rft.volume=36&rft.spage=1731&rft.date=2020
dc.identifier.jtitleBioinformatics
dc.identifier.volume36
dc.identifier.issue6
dc.identifier.startpage1731
dc.identifier.endpage1739
dc.identifier.doihttps://doi.org/10.1093/bioinformatics/btz845
dc.identifier.urnURN:NBN:no-86030
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1367-4803
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/83279/1/Postnr%2B1812070_Smakaj_Greiff_Bioinformatics_btz845.pdf
dc.type.versionPublishedVersion


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