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• W4283371129 abstract "Large scale next generation metagenomic sequencing of complex environmental samples paves the way for detailed analysis of nutrient cycles in ecosystems. For such an analysis, large scale unequivocal annotation is a prerequisite, which however is increasingly hampered by growing databases and analysis time. Hereto, we created a hidden Markov model (HMM) database by clustering proteins according to their KEGG indexing. HMM profiles for key genes of specific metabolic pathways and nutrient cycles were organized in subsets to be able to analyze each important elemental cycle separately. An important motivation behind the clustered database was to enable a high degree of resolution for annotation, while decreasing database size and analysis time. Here, we present Metascan, a new tool that can fully annotate and analyze deeply sequenced samples with an average analysis time of 11 min per genome for a publicly available dataset containing 2,537 genomes, and 1.1 min per genome for nutrient cycle analysis of the same sample. Metascan easily detected general proteins like cytochromes and ferredoxins, and additional pmoCAB operons were identified that were overlooked in previous analyses. For a mock community, the BEACON (F1) score was 0.72–0.93 compared to the information in NCBI GenBank. In combination with the accompanying database, Metascan provides a fast and useful annotation and analysis tool, as demonstrated by our proof-of-principle analysis of a complex mock community metagenome." @default.
• W4283371129 created "2022-06-25" @default.
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• W4283371129 date "2022-06-22" @default.
• W4283371129 modified "2023-10-14" @default.
• W4283371129 title "Metascan: METabolic Analysis, SCreening and ANnotation of Metagenomes" @default.
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• W4283371129 doi "https://doi.org/10.3389/fbinf.2022.861505" @default.
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