PT  - JOURNAL ARTICLE
AU  - Shifu Chen
AU  - Yanqing Zhou
AU  - Yaru Chen
AU  - Tanxiao Huang
AU  - Wenting Liao
AU  - Yun Xu
AU  - Zhicheng Li
AU  - Jia Gu
TI  - gencore: An Efficient Tool to Generate Consensus Reads for Error Suppressing and Duplicate Removing of NGS data
AID  - 10.1101/501502
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 501502
4099  - http://biorxiv.org/content/early/2019/09/06/501502.short
4100  - http://biorxiv.org/content/early/2019/09/06/501502.full
AB  - Background Removing duplicates might be considered as a well-resolved problem in next-generation sequencing (NGS) data processing domain. However, as NGS technology gains more recognition in clinical applications (i.e. cancer testing), researchers start to pay more attention to its sequencing errors, and prefer to remove these errors while performing deduplication operations. Recently, a new technology called unique molecular identifier (UMI) has been developed to better identify sequencing reads derived from different DNA fragments. Most existing duplicate removing tools cannot handle the UMI-integrated data. Some modern tools can work with UMIs, but are usually slow and use too much memory, making them not suitable for cloud-based deployment. Furthermore, existing tools rarely report rich statistical results, which are very important for quality control and downstream analysis. These unmet requirements drove us to develop an ultra-fast, simple, little-weighted but powerful tool for duplicate removing and sequence error suppressing, with features of handling UMIs and reporting informative results.Results This paper presents an efficient tool gencore for duplicate removing and sequence error suppressing of NGS data. This tool clusters the mapped sequencing reads and merges reads in each cluster to generate one single consensus read. While the consensus read is generated, the random errors introduced by library construction and sequencing can be removed. This error-suppressing feature makes gencore very suitable for the application of detecting ultra-low frequency mutations from deep sequencing data. When unique molecular identifier (UMI) technology is applied, gencore can use them to identify the reads derived from same original DNA fragment. gencore reports statistical results in both HTML and JSON formats. The HTML format report contains many interactive figures plotting statistical coverage and duplication information. The JSON format report contains all the statistical results, and is interpretable for downstream programs.Conclusions Comparing to the conventional tools like Picard and SAMtools, gencore greatly reduces the output data’s mapping mismatches, which are mostly caused by errors. Comparing to some new tools like UMI-Reducer and UMI-tools, gencore runs much faster, uses less memory, generates better consensus reads and provides simpler interfaces. To our best knowledge, gencore is the only duplicate removing tool that generates both informative HTML and JSON reports. This tool is available at: https://github.com/OpenGene/gencorectDNAcell-free tumor DNA;NGSnext generation sequencing;IGVintegrative genome viewer;MAFmutated allele frequency;INDELinsertion and deletion;SNPsingle-nucleotide polymorphism;SNVsingle-nucleotide variation;EGFRepidermal growth factor receptor;UMIunique molecular identifier;HTMLHypertext Markup Language;JSONJavaScript Object Notation;NCCLNational Center for Clinical Laboratories;