Literature Review
Metagenomic tools analyze microbiomes from sequencing data, which makes them useful in biological research to detect pathogens, study antimicrobial resistance, and predict illnesses, among others. Although not yet used in clinical settings, many researchers have developed models that can process metagenomic data with increasing efficacy [].
Selective amplification (e.g. 16S, 18S, ITS) of specific regions of microbial genomes have been widely used in metagenomics studies, but they introduce bias and omit elements during analysis [3]. Shotgun sequencing is thus becoming a more reliable way to study microbiomes for a variety of tasks:
Classification
Abundance estimation
Identification
Classification
Several methods can classify sequences of a sample into taxa:
Alignment of sequences
Composition with k-mer analysis
Phylogenetics using models of sequence evolution
GPU Acceleration
2012 homology search: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036060
2012 analysis: https://link.springer.com/article/10.1186/1752-0509-6-S1-S16
2021 sequence assembly: https://dl.acm.org/doi/abs/10.1145/3458817.3476212
2021 classification: https://dl.acm.org/doi/abs/10.1145/3472456.3472460
References
Alexander Fritz, Patrick Hofmann, Stefan Majda, Elisabeth Dahms, Helene Marie Draese, Till R Lesker, Peter Belmann, Matthew Z DeMaere, Aaron E Darling, Alexander Sczyrba, Andreas Bremges, and Alice C McHardy. Camisim: simulating metagenomes and microbial communities. Microbiome, 7(1):17, 2019. URL: https://doi.org/10.1186/s40168-019-0633-6, doi:10.1186/s40168-019-0633-6.
Dirk Hackenberger, Hamna Imtiaz, Amogelang R. Raphenya, Brian P. Alcock, Hendrik N. Poinar, Gerard D. Wright, and Andrew G. McArthur. Carpdm: cost-effective antibiotic resistome profiling of metagenomic samples using targeted enrichment. bioRxiv, 2024. URL: https://www.biorxiv.org/content/early/2024/03/30/2024.03.27.587061, arXiv:https://www.biorxiv.org/content/early/2024/03/30/2024.03.27.587061.full.pdf, doi:10.1101/2024.03.27.587061.
Alison B.R. McIntyre, Rachid Ounit, Ebrahim Afshinnekoo, and others. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biology, 18(1):182, 2017. URL: https://doi.org/10.1186/s13059-017-1299-7, doi:10.1186/s13059-017-1299-7.