AIMC Topic: Metagenomics

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A machine-learning informed circulating microbial DNA signature for early diagnosis of esophageal adenocarcinoma.

Gut microbes
Esophageal adenocarcinoma (EAC) has seen a dramatic rise in incidence in developed countries over the past three decades. Early detection of its precursors-gastroesophageal reflux disease (GERD), Barrett's esophagus (BE), and high-grade dysplasia (HG...

Faecal metabolites as a readout of habitual diet capture dietary interactions with the gut microbiome.

Nature communications
The interplay between diet and gut microbiome composition is complex. Faecal metabolites, the end products of human and microbial metabolism, provide insights into these interactions. Here, we integrate faecal metabolomics, metagenomics, and habitual...

Functional archetypes in the human gut microbiome reveal metabolic diversity, stability, and influence disease-associated signatures.

Microbiome
BACKGROUND: Understanding the functional diversity of the gut microbiome is critical for elucidating its roles in human health and disease. While traditional approaches focus on taxonomic composition, functional configurations of the microbiome remai...

Protein language models uncover carbohydrate-active enzyme function in metagenomics.

BMC bioinformatics
BACKGROUND: The functional annotation of uncharacterized microbial enzymes from metagenomic data remains a significant challenge, limiting our understanding of microbial metabolic dynamics. Traditional annotation methods often rely on sequence homolo...

Machine learning-guided discovery of thermophilic carbonic anhydrases from environmental metagenomes.

Scientific reports
Thermophilic carbonic anhydrases (CAs) are promising biocatalysts for carbon capture utilization and storage (CCUS) due to their stability and efficiency at elevated temperatures. This study presents a machine learning (ML)-guided approach to discove...

Charting the virosphere: computational synergies of AI and bioinformatics in viral discovery and evolution.

Journal of virology
The advancement of metagenomic sequencing has revealed a vast viral diversity while simultaneously exposing limitations of homology-based tools such as BLAST and HMMER, which often fail to detect highly divergent viral genomes. The integration of art...

Metagenomic next-generation sequencing unraveled the characteristic of lung microbiota in patients with checkpoint inhibitor pneumonitis: results from a prospective cohort study.

Journal for immunotherapy of cancer
BACKGROUND: Checkpoint inhibitor pneumonitis (CIP) is among the most lethal immune-related adverse events in patients with cancer receiving immunotherapy. This study aims to characterize the lung microbiome in patients with CIP and evaluate its diagn...

Harnessing machine learning for metagenomic data analysis: trends and applications.

mSystems
Metagenomic sequencing has revolutionized our understanding of microbial ecosystems by enabling high-resolution profiling of microbes across diverse environments. However, the resulting data are high-dimensional, sparse, and noisy, posing challenges ...

Machine learning and statistical inference in microbial population genomics.

Genome biology
The availability of large genome datasets has changed the microbiology research landscape. Analyzing such data requires computationally demanding analyses, and new approaches have come from different data analysis philosophies. Machine learning and s...

Tracking putative viruses and virus-host associations across distinct phases of a -dominated bloom.

mSystems
Viruses significantly impact microbial community composition and function. Yet their role in the fate of freshwater cyanobacterial harmful algal blooms (cHABs), an increasing threat to freshwater systems, remains poorly understood. Here, we address t...