4.5 Article

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

期刊

GENOME BIOLOGY
卷 21, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s13059-020-01988-3

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资金

  1. NSF [1705197, DBI-1564899, CCF-1619110]
  2. NIH [K99 AI139445, 1R01EB025022-01, 1R01EB025022]
  3. Mangul Lab at USC School of Pharmacy
  4. National Science Foundation [1705197, 1910885]
  5. National Institutes of Health [U01-DA041602, R01-MH115979]
  6. Molecular Basis of Disease at Georgia State University
  7. Direct For Computer & Info Scie & Enginr [1910885] Funding Source: National Science Foundation
  8. Div Of Information & Intelligent Systems [1910885] Funding Source: National Science Foundation

向作者/读者索取更多资源

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.

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