Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-8122
Authors: | Kallenborn, Felix Cascitti, Julian Schmidt, Bertil |
Title: | CARE 2.0 : reducing false-positive sequencing error corrections using machine learning |
Online publication date: | 31-Oct-2022 |
Year of first publication: | 2022 |
Language: | english |
Abstract: | Background Next-generation sequencing pipelines often perform error correction as a preprocessing step to obtain cleaned input data. State-of-the-art error correction programs are able to reliably detect and correct the majority of sequencing errors. However, they also introduce new errors by making false-positive corrections. These correction mistakes can have negative impact on downstream analysis, such as k-mer statistics, de-novo assembly, and variant calling. This motivates the need for more precise error correction tools. Results We present CARE 2.0, a context-aware read error correction tool based on multiple sequence alignment targeting Illumina datasets. In addition to a number of newly introduced optimizations its most significant change is the replacement of CARE 1.0’s hand-crafted correction conditions with a novel classifier based on random decision forests trained on Illumina data. This results in up to two orders-of-magnitude fewer false-positive corrections compared to other state-of-the-art error correction software. At the same time, CARE 2.0 is able to achieve high numbers of true-positive corrections comparable to its competitors. On a simulated full human dataset with 914M reads CARE 2.0 generates only 1.2M false positives (FPs) (and 801.4M true positives (TPs)) at a highly competitive runtime while the best corrections achieved by other state-of-the-art tools contain at least 3.9M FPs and at most 814.5M TPs. Better de-novo assembly and improved k-mer analysis show the applicability of CARE 2.0 to real-world data. Conclusion False-positive corrections can negatively influence down-stream analysis. The precision of CARE 2.0 greatly reduces the number of those corrections compared to other state-of-the-art programs including BFC, Karect, Musket, Bcool, SGA, and Lighter. Thus, higher-quality datasets are produced which improve k-mer analysis and de-novo assembly in real-world datasets which demonstrates the applicability of machine learning techniques in the context of sequencing read error correction. CARE 2.0 is written in C++/CUDA for Linux systems and can be run on the CPU as well as on CUDA-enabled GPUs. It is available at https://github.com/fkallen/CARE. |
DDC: | 004 Informatik 004 Data processing |
Institution: | Johannes Gutenberg-Universität Mainz |
Department: | FB 08 Physik, Mathematik u. Informatik |
Place: | Mainz |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-8122 |
Version: | Published version |
Publication type: | Zeitschriftenaufsatz |
Document type specification: | Scientific article |
License: | CC BY |
Information on rights of use: | https://creativecommons.org/licenses/by/4.0/ |
Journal: | BMC bioinformatics 23 |
Pages or article number: | 227 |
Publisher: | Springer Nature |
Publisher place: | London |
Issue date: | 2022 |
ISSN: | 1471-2105 |
Publisher DOI: | 10.1186/s12859-022-04754-3 |
Appears in collections: | DFG-491381577-G |
Files in This Item:
File | Description | Size | Format | ||
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![]() | care_20__reducing_falsepositi-20221020145914717.pdf | 1.57 MB | Adobe PDF | View/Open |