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Metagenomics

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Rapid and accurate identification of ribosomal RNA sequences via deep learning.

Nucleic acids research
Advances in transcriptomic and translatomic techniques enable in-depth studies of RNA activity profiles and RNA-based regulatory mechanisms. Ribosomal RNA (rRNA) sequences are highly abundant among cellular RNA, but if the target sequences do not inc...

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 ...

Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning.

Gut microbes
The human gut microbiome is a complex ecosystem that is closely related to the aging process. However, there is currently no reliable method to make full use of the metagenomics data of the gut microbiome to determine the age of the host. In this stu...

Human host status inference from temporal microbiome changes via recurrent neural networks.

Briefings in bioinformatics
With the rapid increase in sequencing data, human host status inference (e.g. healthy or sick) from microbiome data has become an important issue. Existing studies are mostly based on single-point microbiome composition, while it is rare that the hos...

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...

Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data.

Studies in health technology and informatics
In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in the world, a classification model has bee...

Artificial intelligence and metagenomics in intestinal diseases.

Journal of gastroenterology and hepatology
Gut microbiota has been shown to associate with the development of gastrointestinal diseases. In the last decade, development in whole metagenome sequencing and 16S rRNA sequencing technology has dramatically accelerated the gut microbiome's research...

PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning.

GigaScience
BACKGROUND: Phages and plasmids are the major components of mobile genetic elements, and fragments from such elements generally co-exist with chromosome-derived fragments in sequenced metagenomic data. However, there is a lack of efficient methods th...

MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification.

Methods in molecular biology (Clifton, N.J.)
Metagenomics is the study of microbial community diversity, especially the uncultured microorganisms by shotgun sequencing environmental samples. As the sequencers throughput and the data volume increase, it becomes challenging to develop scalable bi...