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| Main Authors: | , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Scientific reports
2026
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| Online Access: | https://pubmed.ncbi.nlm.nih.gov/42092044/ |
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| _version_ | 1868266051123281920 |
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| author | Loukas, Alexios Kalaentzis, Konstantinos Venetsianou, Nefeli Kleopatra Damianou, Christina Paragkamian, Savvas Lagani, Vincenzo Jensen, Lars Juhl Pafilis, Evangelos |
| author_facet | Loukas, Alexios Kalaentzis, Konstantinos Venetsianou, Nefeli Kleopatra Damianou, Christina Paragkamian, Savvas Lagani, Vincenzo Jensen, Lars Juhl Pafilis, Evangelos Loukas, Alexios Kalaentzis, Konstantinos Venetsianou, Nefeli Kleopatra Damianou, Christina Paragkamian, Savvas Lagani, Vincenzo Jensen, Lars Juhl Pafilis, Evangelos |
| collection | PubMed - marine biology |
| contents | CCMRI: a classification and curated database of climate change-related microbiome studies. Loukas, Alexios Kalaentzis, Konstantinos Venetsianou, Nefeli Kleopatra Damianou, Christina Paragkamian, Savvas Lagani, Vincenzo Jensen, Lars Juhl Pafilis, Evangelos Climate Change (CC) is reshaping all ecosystem processes and structures. Microbial data provide valuable insights into how microbial processes contribute to CC and how CC, in turn, alters microbial communities. However, the growing volume of environmental genomics data makes identifying CC-related records challenging. The Climate Change Metagenomic Record Index (CCMRI) has been developed to harvest metagenomic/microbiome records pertaining to CC and to provide researchers with a curated database of CC-related microbiome studies (https://ccmri.hcmr.gr). To guide interpretation, the database's 169 metagenomic studies have been labelled according to their relation to CC as CC-caused, CC-causing, and CC-mitigating. They have also been annotated with the CC phenomena they explore, like methane production, temperature rise, permafrost thawing, greenhouse gas emission, methanotrophy, and ocean acidification. To ease navigation, they have also been classified according to their biome as aquatic, terrestrial, host-associated, and engineered. The CCMRI database was initially constructed through manual curation of all aquatic and terrestrial studies in the MGnify resource. It was then expanded with the help of the CCMRI curation-assistant system. This leveraged Large Language Models to scan the remaining MGnify studies, filtered them for relevance, and proposed candidates for inclusion. With a recall greater than 90%, the system achieved high accuracy in identifying CC-related studies. The final decisions on CC-relatedness and categorization were performed by a human curator. This approach combines the efficiency of automation with human oversight and greatly reduces the curation effort, ensuring sustainability and scalability. |
| format | Artículo científico |
| id | pubmed_42092044 |
| institution | PubMed |
| language | en |
| publishDate | 2026 |
| publisher | Scientific reports |
| record_format | pubmed |
| spellingShingle | CCMRI: a classification and curated database of climate change-related microbiome studies. Loukas, Alexios Kalaentzis, Konstantinos Venetsianou, Nefeli Kleopatra Damianou, Christina Paragkamian, Savvas Lagani, Vincenzo Jensen, Lars Juhl Pafilis, Evangelos CCMRI: a classification and curated database of climate change-related microbiome studies. Loukas, Alexios Kalaentzis, Konstantinos Venetsianou, Nefeli Kleopatra Damianou, Christina Paragkamian, Savvas Lagani, Vincenzo Jensen, Lars Juhl Pafilis, Evangelos Climate Change (CC) is reshaping all ecosystem processes and structures. Microbial data provide valuable insights into how microbial processes contribute to CC and how CC, in turn, alters microbial communities. However, the growing volume of environmental genomics data makes identifying CC-related records challenging. The Climate Change Metagenomic Record Index (CCMRI) has been developed to harvest metagenomic/microbiome records pertaining to CC and to provide researchers with a curated database of CC-related microbiome studies (https://ccmri.hcmr.gr). To guide interpretation, the database's 169 metagenomic studies have been labelled according to their relation to CC as CC-caused, CC-causing, and CC-mitigating. They have also been annotated with the CC phenomena they explore, like methane production, temperature rise, permafrost thawing, greenhouse gas emission, methanotrophy, and ocean acidification. To ease navigation, they have also been classified according to their biome as aquatic, terrestrial, host-associated, and engineered. The CCMRI database was initially constructed through manual curation of all aquatic and terrestrial studies in the MGnify resource. It was then expanded with the help of the CCMRI curation-assistant system. This leveraged Large Language Models to scan the remaining MGnify studies, filtered them for relevance, and proposed candidates for inclusion. With a recall greater than 90%, the system achieved high accuracy in identifying CC-related studies. The final decisions on CC-relatedness and categorization were performed by a human curator. This approach combines the efficiency of automation with human oversight and greatly reduces the curation effort, ensuring sustainability and scalability. |
| title | CCMRI: a classification and curated database of climate change-related microbiome studies. |
| url | https://pubmed.ncbi.nlm.nih.gov/42092044/ |