AIMC Topic: Archaea
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LucaPCycle: Illuminating microbial phosphorus cycling in deep-sea cold seep sediments using protein language models.
Nature communications
May 26, 2025
Phosphorus is essential for life and critically influences marine productivity. Despite geochemical evidence of active phosphorus cycling in deep-sea cold seeps, the microbial processes involved remain poorly understood. Traditional sequence-based se...
An atlas of protein homo-oligomerization across domains of life.
Cell
Feb 6, 2024
Protein structures are essential to understanding cellular processes in molecular detail. While advances in artificial intelligence revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. We devise a s...
Explainable artificial intelligence as a reliable annotator of archaeal promoter regions.
Scientific reports
Jan 31, 2023
Archaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulat...
Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter.
Nature communications
May 11, 2022
The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely...
Using neural networks to mine text and predict metabolic traits for thousands of microbes.
PLoS computational biology
Mar 2, 2021
Microbes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. I...
Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction.
Scientific reports
Sep 3, 2020
The use of raw amino acid sequences as input for deep learning models for protein functional prediction has gained popularity in recent years. This scheme obliges to manage proteins with different lengths, while deep learning models require same-shap...
Machine-learning approach expands the repertoire of anti-CRISPR protein families.
Nature communications
Jul 29, 2020
The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the development of powerful genome editing and engineering tools. In the incessant host-parasite arms race, viruses evolved multiple anti-defense mechani...
Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes.
Microbiome
Feb 11, 2020
BACKGROUND: Microorganisms in activated sludge (AS) play key roles in the wastewater treatment processes. However, their ecological behaviors and differences from microorganisms in other environments have mainly been studied using the 16S rRNA gene t...
Changes in bacterial and archaeal communities during the concentration of brine at the graduation towers in Ciechocinek spa (Poland).
Extremophiles : life under extreme conditions
Dec 19, 2017
This study evaluates the changes in bacterial and archaeal community structure during the gradual evaporation of water from the brine (extracted from subsurface Jurassic deposits) in the system of graduation towers located in Ciechocinek spa, Poland....
SILVA, RDP, Greengenes, NCBI and OTT - how do these taxonomies compare?
BMC genomics
Mar 14, 2017
BACKGROUND: A key step in microbiome sequencing analysis is read assignment to taxonomic units. This is often performed using one of four taxonomic classifications, namely SILVA, RDP, Greengenes or NCBI. It is unclear how similar these are and how to...
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