Abstract
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.
Highlights
Metagenomics studies and studies of polyploid organisms are two areas of research in genomic analysis that are forced to deal with datasets containing high variation among the individual genetic sequences, which negatively affects the data analysis.Metagenomics studies communities of micro-organisms, like the ones forming the human microbiome [1] or the microbiome of urban environments [2], and are the focus of studies of viral evolution [3]
Understanding of the human microbiome is essential for the future of medicine [4], is a significant part of nutrition studies [5], affects human space flight [6], and the proper study of disease outbreaks [7]; metagenomics studies are critical for agricultural development [8]
To verify the chosen design of the machine learning (ML) input examples, as well as the non-standard way to craft training examples, models constructed using them were tested on metagenomics data and, through a later experiment, on hexaploid wheat data
Summary
Metagenomics studies and studies of polyploid organisms are two areas of research in genomic analysis that are forced to deal with datasets containing high variation among the individual genetic sequences, which negatively affects the data analysis. Metagenomics studies communities of micro-organisms, like the ones forming the human microbiome [1] or the microbiome of urban environments [2], and are the focus of studies of viral evolution [3]. The presence of genetic sequences from multiple similar organisms in the same sample—a feature of great importance from a scientific standpoint—is a big obstacle to the data analysis. The inevitable presence of machine errors in the data, which can be masquerading as legitimate inter-species variation, cannot always be accounted for. The errors cause inaccuracies in the final study results [9,10] and are often resolved by discarding reads suspected to contain errors [11] in their entirety
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