AIMC Topic: Metagenome

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Deep learning methods in metagenomics: a review.

Microbial genomics
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such a...

CoCoPyE: feature engineering for learning and prediction of genome quality indices.

GigaScience
BACKGROUND: The exploration of the microbial world has been greatly advanced by the reconstruction of genomes from metagenomic sequence data. However, the rapidly increasing number of metagenome-assembled genomes has also resulted in a wide variation...

PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.

Bioinformatics (Oxford, England)
MOTIVATION: Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resi...

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and funct...

Virtifier: a deep learning-based identifier for viral sequences from metagenomes.

Bioinformatics (Oxford, England)
MOTIVATION: Viruses, the most abundant biological entities on earth, are important components of microbial communities, and as major human pathogens, they are responsible for human mortality and morbidity. The identification of viral sequences from m...

Tiara: deep learning-based classification system for eukaryotic sequences.

Bioinformatics (Oxford, England)
MOTIVATION: With a large number of metagenomic datasets becoming available, eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step toward a better understanding ...

CoCoNet: an efficient deep learning tool for viral metagenome binning.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It con...

DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach.

GigaScience
BACKGROUND: Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for elucidating their different roles in interactio...

Prediction of prokaryotic transposases from protein features with machine learning approaches.

Microbial genomics
Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea usin...