MLP: Microbial load predictor
MLP is a computational tool designed to predict the fecal microbial load (microbial cells per gram or cell density) of adult fecal samples directly from the taxonomic profile of the gut microbiome. This tool enables the estimation of microbial load and the quantification of the absolute abundance of each microbial species without the need for additional experiments. By utilizing large-scale datasets of fecal metagenomes and fecal microbial load in the GALAXY/MicrobLiver (n = 1,894) and MetaCardis (n = 1,812) study populations, the model was extensively trained and validated.
Input file
The input file is species-level taxonomic profiles. Following taxonomic profilers (the default output) are supported. Please see the details of the format in the documentation.
- mOTUs v2.5 (Milanese A et al., 2019)
- mOTUs v3.0 (Ruscheweyh HJ et al., 2022)
- MetaPhlAn3 (Beghini F et al., 2021)
- MetaPhlAn4 (Blanco-Míguez A et al., 2023)
Output file
There are two output options, predicted microbial load and QMP.
- Microbial load: predicted number of microbial cells per gram for each sample
- Quantitative microbiome profile (QMP): taxonomic profile of the microbiome where each abundance represents the absolute abundance of the species
Citation
Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations
Suguru Nishijima, Evelina Stankevic, Oliver Aasmets, Thomas S.B. Schmidt, Naoyoshi Nagata, Marisa Isabell Keller, Pamela Ferretti, Helene Bæk Juel, Anthony Fullam, Shahriyar Mahdi Robbani, Christian Schudoma, Johanne Kragh Hansen, Louise Aas Holm, Mads Israelsen, Robert Schierwagen, Nikolaj Torp, Anja Telzerow, Rajna Hercog, Stefanie Kandels, Diënty H.M. Hazenbrink, Manimozhiyan Arumugam, Flemming Bendtsen, Charlotte Brøns, Cilius Esmann Fonvig, Jens-Christian Holm, Trine Nielsen, Julie Steen Pedersen, Maja Sofie Thiele, Jonel Trebicka, Elin Org, Aleksander Krag, Torben Hansen, Michael Kuhn, and Peer Bork, on behalf of the GALAXY and MicrobLiver Consortia
Cell. 2024 Nov 4:S0092-8674(24)01204-2. doi: 10.1016/j.cell.2024.10.022