Metagenomics, a scientific discipline focused on the analysis of DNA sequences derived from entire microbial communities, holds significant promise across various fields, such as in Medicine and Environmental Monitoring. However, it presents formidable computational hurdles due to the large sizes of input data. Machine Learning techniques, particularly Deep Learning, have emerged as powerful tools to tackle these challenges. In this thesis, we focus on the contigs binning problem, that is the clusterization of partially assembled DNA sequences, and propose an autoencoder-based architecture denoted as XAVAMB. Through a comprehensive evaluation against the State of the Art, our model demonstrated notable efficacy, particularly in discerning strains at the most granular taxonomic level.
La Metagenomica, disciplina scientifica focalizzata sull’analisi di sequenze di DNA derivate da intere comunità microbiche, offre significative promesse in vari settori, come la medicina e il monitoraggio ambientale. Tuttavia, essa presenta notevoli ostacoli computazionali a causa delle grandi dimensioni dei dati di input. Le tecniche di Machine Learning, in particolare il Deep Learning, sono emerse come potenti strumenti per affrontare tali sfide. In questa tesi, ci concentriamo sul problema del raggruppamento dei contigs, cioè la clusterizzazione di sequenze di DNA parzialmente assemblate, e proponiamo un’architettura basata su autoencoder denominata XAVAMB. Attraverso una valutazione completa rispetto allo stato dell’arte, il nostro modello ha dimostrato un’elevata efficacia, in particolare nel discernere ceppi al livello tassonomico più granulare.
Applicazione di Autoencoders al binning metagenomico
SPINA, LORENZO
2023/2024
Abstract
Metagenomics, a scientific discipline focused on the analysis of DNA sequences derived from entire microbial communities, holds significant promise across various fields, such as in Medicine and Environmental Monitoring. However, it presents formidable computational hurdles due to the large sizes of input data. Machine Learning techniques, particularly Deep Learning, have emerged as powerful tools to tackle these challenges. In this thesis, we focus on the contigs binning problem, that is the clusterization of partially assembled DNA sequences, and propose an autoencoder-based architecture denoted as XAVAMB. Through a comprehensive evaluation against the State of the Art, our model demonstrated notable efficacy, particularly in discerning strains at the most granular taxonomic level.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/65031