Ontology highlight
ABSTRACT: 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.
SUBMITTER: Mitchell K
PROVIDER: S-EPMC7079412 | biostudies-literature | 2020 Mar
REPOSITORIES: biostudies-literature
Mitchell Keith K Brito Jaqueline J JJ Mandric Igor I Wu Qiaozhen Q Knyazev Sergey S Chang Sei S Martin Lana S LS Karlsberg Aaron A Gerasimov Ekaterina E Littman Russell R Hill Brian L BL Wu Nicholas C NC Yang Harry Taegyun HT Hsieh Kevin K Chen Linus L Littman Eli E Shabani Taylor T Enik German G Yao Douglas D Sun Ren R Schroeder Jan J Eskin Eleazar E Zelikovsky Alex A Skums Pavel P Pop Mihai M Mangul Serghei S
Genome biology 20200317 1
<h4>Background</h4>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 ...[more]