Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data

Metagenomics studies, as well as genomics studies of polyploid species such as wheat, deal with the analysis of high variation data. Such data contain sequences from similar, but distinct genetic chains. This fact presents an obstacle to analysis and research. In particular, the detection of instrum...

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Main Authors:	Milko Krachunov, Maria Nisheva, Dimitar Vassilev
Format:	Article
Language:	English
Published:	MDPI AG 2019-03-01
Series:	Information
Subjects:	machine learning neural network NGS errors metagenomics polyploid genomes
Online Access:	http://www.mdpi.com/2078-2489/10/3/110

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spelling	doaj-f92df43a78bb48ef84e956aa5c10f5a02020-11-25T00:32:57ZengMDPI AGInformation2078-24892019-03-0110311010.3390/info10030110info10030110Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing DataMilko Krachunov0Maria Nisheva1Dimitar Vassilev2Faculty of Mathematics and Informatics, University of Sofia “St. Kliment Ohridski”, 5 James Bourchier Blvd., 1164 Sofia, BulgariaFaculty of Mathematics and Informatics, University of Sofia “St. Kliment Ohridski”, 5 James Bourchier Blvd., 1164 Sofia, BulgariaFaculty of Mathematics and Informatics, University of Sofia “St. Kliment Ohridski”, 5 James Bourchier Blvd., 1164 Sofia, BulgariaMetagenomics studies, as well as genomics studies of polyploid species such as wheat, deal with the analysis of high variation data. Such data contain sequences from similar, but distinct genetic chains. This fact presents an obstacle to analysis and research. In particular, the detection of instrumentation errors during the digitalization of the sequences may be hindered, as they can be indistinguishable from the real biological variation inside the digital data. This can prevent the determination of the correct sequences, while at the same time make variant studies significantly more difficult. This paper details a collection of ML-based models used to distinguish a real variant from an erroneous one. The focus is on using this model directly, but experiments are also done in combination with other predictors that isolate a pool of error candidates.http://www.mdpi.com/2078-2489/10/3/110machine learningneural networkNGS errorsmetagenomicspolyploid genomes
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Milko Krachunov Maria Nisheva Dimitar Vassilev
spellingShingle	Milko Krachunov Maria Nisheva Dimitar Vassilev Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data Information machine learning neural network NGS errors metagenomics polyploid genomes
author_facet	Milko Krachunov Maria Nisheva Dimitar Vassilev
author_sort	Milko Krachunov
title	Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data
title_short	Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data
title_full	Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data
title_fullStr	Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data
title_full_unstemmed	Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data
title_sort	machine learning models for error detection in metagenomics and polyploid sequencing data
publisher	MDPI AG
series	Information
issn	2078-2489
publishDate	2019-03-01
description	Metagenomics studies, as well as genomics studies of polyploid species such as wheat, deal with the analysis of high variation data. Such data contain sequences from similar, but distinct genetic chains. This fact presents an obstacle to analysis and research. In particular, the detection of instrumentation errors during the digitalization of the sequences may be hindered, as they can be indistinguishable from the real biological variation inside the digital data. This can prevent the determination of the correct sequences, while at the same time make variant studies significantly more difficult. This paper details a collection of ML-based models used to distinguish a real variant from an erroneous one. The focus is on using this model directly, but experiments are also done in combination with other predictors that isolate a pool of error candidates.
topic	machine learning neural network NGS errors metagenomics polyploid genomes
url	http://www.mdpi.com/2078-2489/10/3/110
work_keys_str_mv	AT milkokrachunov machinelearningmodelsforerrordetectioninmetagenomicsandpolyploidsequencingdata AT marianisheva machinelearningmodelsforerrordetectioninmetagenomicsandpolyploidsequencingdata AT dimitarvassilev machinelearningmodelsforerrordetectioninmetagenomicsandpolyploidsequencingdata
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Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data

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