Fuzzy Indication of Reliability in Metagenomics NGS Data Analysis

Abstract

NGS data processing in metagenomics studies has to deal with noisy data that can contain a large amount of reading errors which are difficult to detect and account for. This work introduces a fuzzy indicator of reliability technique to facilitate solutions to this problem. It includes modified Hamming and Levenshtein distance functions that are aimed to be used as drop-in replacements in NGS analysis procedures which rely on distances, such as phylogenetic tree construction. The distances utilise fuzzy sets of reliable bases or an equivalent fuzzy logic, potentially aggregating multiple sources of base reliability.

Application of Machine Learning Models in Error and Variant Detection in High-Variation Genomics Datasets

“…Additionally, the ANN models can make predictions in real number values, as opposed to classification into categories or finite integer values. This allows us to use the ANN predictions inside a previously developed Fuzzy Logic model for combining multiple predictions [19].…”