File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Benchmarking of computational error-correction methods for next-generation sequencing data

TitleBenchmarking of computational error-correction methods for next-generation sequencing data
Authors
Issue Date2020
Citation
Genome Biology, 2020, v. 21, n. 1, article no. 71 How to Cite?
Abstract© 2020 The Author(s). Background: Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown. Results: In this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods. Conclusions: In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.
Persistent Identifierhttp://hdl.handle.net/10722/285863
ISSN
2012 Impact Factor: 10.288
2015 SCImago Journal Rankings: 9.860
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorMitchell, Keith-
dc.contributor.authorBrito, Jaqueline J.-
dc.contributor.authorMandric, Igor-
dc.contributor.authorWu, Qiaozhen-
dc.contributor.authorKnyazev, Sergey-
dc.contributor.authorChang, Sei-
dc.contributor.authorMartin, Lana S.-
dc.contributor.authorKarlsberg, Aaron-
dc.contributor.authorGerasimov, Ekaterina-
dc.contributor.authorLittman, Russell-
dc.contributor.authorHill, Brian L.-
dc.contributor.authorWu, Nicholas C.-
dc.contributor.authorYang, Harry Taegyun-
dc.contributor.authorHsieh, Kevin-
dc.contributor.authorChen, Linus-
dc.contributor.authorLittman, Eli-
dc.contributor.authorShabani, Taylor-
dc.contributor.authorEnik, German-
dc.contributor.authorYao, Douglas-
dc.contributor.authorSun, Ren-
dc.contributor.authorSchroeder, Jan-
dc.contributor.authorEskin, Eleazar-
dc.contributor.authorZelikovsky, Alex-
dc.contributor.authorSkums, Pavel-
dc.contributor.authorPop, Mihai-
dc.contributor.authorMangul, Serghei-
dc.date.accessioned2020-08-18T04:56:50Z-
dc.date.available2020-08-18T04:56:50Z-
dc.date.issued2020-
dc.identifier.citationGenome Biology, 2020, v. 21, n. 1, article no. 71-
dc.identifier.issn1474-7596-
dc.identifier.urihttp://hdl.handle.net/10722/285863-
dc.description.abstract© 2020 The Author(s). Background: Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown. Results: In this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods. Conclusions: In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.-
dc.languageeng-
dc.relation.ispartofGenome Biology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleBenchmarking of computational error-correction methods for next-generation sequencing data-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s13059-020-01988-3-
dc.identifier.pmid32183840-
dc.identifier.pmcidPMC7079412-
dc.identifier.scopuseid_2-s2.0-85082008336-
dc.identifier.volume21-
dc.identifier.issue1-
dc.identifier.spagearticle no. 71-
dc.identifier.epagearticle no. 71-
dc.identifier.eissn1474-760X-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats