Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation.

Area of Science:

• Environmental microbiology
• Bioinformatics
• Genomics

Background:

• Metagenomics, analyzing environmental DNA, characterizes microbial communities.
• Accurate taxonomic and functional annotation of sequenced reads is crucial.
• Current alignment methods face limitations in speed, accuracy, and detecting diverse genetic elements like viruses and plasmids.

Purpose of the Study:

• To review and assess methods for improving metagenomic sequence annotation.
• To evaluate the efficiency of deep learning models compared to traditional approaches.

Main Methods:

• Review of existing sequence alignment techniques.
• Exploration of machine and deep learning models for metagenomic data.
• Comparison of deep learning performance against k-mer alignment tools.

Main Results:

• Deep learning models achieve performance comparable to k-mer alignment methods.
• Deep learning offers improved accuracy in specific annotation scenarios.
• Newly reconstructed genomes from *de novo* binning can be utilized by machine learning models.

Conclusions:

• Deep learning presents a promising alternative for metagenomic sequence annotation.
• Further validation is needed to establish the robustness of deep learning across diverse environmental samples and expanding genomic databases.