Gutenberg Open Science: CARE 2.0 : reducing false-positive sequencing error corrections using machine learning

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8122

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DC Field	Value	Language
dc.contributor.author	Kallenborn, Felix	-
dc.contributor.author	Cascitti, Julian	-
dc.contributor.author	Schmidt, Bertil	-
dc.date.accessioned	2022-10-31T08:39:13Z	-
dc.date.available	2022-10-31T08:39:13Z	-
dc.date.issued	2022	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/8137	-
dc.description.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.	en_GB
dc.description.sponsorship	Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	004 Informatik	de_DE
dc.subject.ddc	004 Data processing	en_GB
dc.title	CARE 2.0 : reducing false-positive sequencing error corrections using machine learning	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-8122	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	BMC bioinformatics	de
jgu.journal.volume	23	de
jgu.pages.alternative	227	de
jgu.publisher.year	2022	-
jgu.publisher.name	Springer Nature	de
jgu.publisher.place	London	de
jgu.publisher.issn	1471-2105	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	004	de
jgu.publisher.doi	10.1186/s12859-022-04754-3	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Ingenieurwissenschaften	de
Appears in collections:	DFG-491381577-G

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