smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers
| Autor: | Zhong Wu, Yexun Wang, Raghavendra Padmanabhan, John DiCarlo, Xiujing Gu, Chang Xu, Quan Peng |
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| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Sequence analysis DNA polymerase Computer science Pipeline (computing) Sequencing data medicine.disease_cause computer.software_genre Polymerase Chain Reaction Biochemistry law.invention 03 medical and health sciences chemistry.chemical_compound Gene Frequency law medicine Code (cryptography) Molecular Biology Allele frequency Polymerase chain reaction 030304 developmental biology 0303 health sciences Mutation biology business.industry 030302 biochemistry & molecular biology High-Throughput Nucleotide Sequencing Pattern recognition Sequence Analysis DNA Original Papers Pipeline (software) Computer Science Applications Identifier Computational Mathematics Computational Theory and Mathematics chemistry Mutation (genetic algorithm) biology.protein Data mining Artificial intelligence business Sequence Analysis computer Software DNA |
| Zdroj: | Bioinformatics |
| DOI: | 10.1101/281659 |
| Popis: | Motivation Low-frequency DNA mutations are often confounded with technical artifacts from sample preparation and sequencing. With unique molecular identifiers (UMIs), most of the sequencing errors can be corrected. However, errors before UMI tagging, such as DNA polymerase errors during end repair and the first PCR cycle, cannot be corrected with single-strand UMIs and impose fundamental limits to UMI-based variant calling. Results We developed smCounter2, a UMI-based variant caller for targeted sequencing data and an upgrade from the current version of smCounter. Compared to smCounter, smCounter2 features lower detection limit that decreases from 1 to 0.5%, better overall accuracy (particularly in non-coding regions), a consistent threshold that can be applied to both deep and shallow sequencing runs, and easier use via a Docker image and code for read pre-processing. We benchmarked smCounter2 against several state-of-the-art UMI-based variant calling methods using multiple datasets and demonstrated smCounter2’s superior performance in detecting somatic variants. At the core of smCounter2 is a statistical test to determine whether the allele frequency of the putative variant is significantly above the background error rate, which was carefully modeled using an independent dataset. The improved accuracy in non-coding regions was mainly achieved using novel repetitive region filters that were specifically designed for UMI data. Availability and implementation The entire pipeline is available at https://github.com/qiaseq/qiaseq-dna under MIT license. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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