@article {742, title = {Reporting guidelines for human microbiome research: the STORMS checklist.}, journal = {Nat Med}, volume = {27}, year = {2021}, month = {2021 11}, pages = {1885-1892}, abstract = {

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called {\textquoteright}Strengthening The Organization and Reporting of Microbiome Studies{\textquoteright} (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

}, keywords = {Computational Biology, Dysbiosis, Humans, Microbiota, Observational Studies as Topic, Research Design, Translational Science, Biomedical}, issn = {1546-170X}, doi = {10.1038/s41591-021-01552-x}, author = {Mirzayi, Chloe and Renson, Audrey and Zohra, Fatima and Elsafoury, Shaimaa and Geistlinger, Ludwig and Kasselman, Lora J and Eckenrode, Kelly and van de Wijgert, Janneke and Loughman, Amy and Marques, Francine Z and MacIntyre, David A and Arumugam, Manimozhiyan and Azhar, Rimsha and Beghini, Francesco and Bergstrom, Kirk and Bhatt, Ami and Bisanz, Jordan E and Braun, Jonathan and Bravo, Hector Corrada and Buck, Gregory A and Bushman, Frederic and Casero, David and Clarke, Gerard and Collado, Maria Carmen and Cotter, Paul D and Cryan, John F and Demmer, Ryan T and Devkota, Suzanne and Elinav, Eran and Escobar, Juan S and Fettweis, Jennifer and Finn, Robert D and Fodor, Anthony A and Forslund, Sofia and Franke, Andre and Furlanello, Cesare and Gilbert, Jack and Grice, Elizabeth and Haibe-Kains, Benjamin and Handley, Scott and Herd, Pamela and Holmes, Susan and Jacobs, Jonathan P and Karstens, Lisa and Knight, Rob and Knights, Dan and Koren, Omry and Kwon, Douglas S and Langille, Morgan and Lindsay, Brianna and McGovern, Dermot and McHardy, Alice C and McWeeney, Shannon and Mueller, Noel T and Nezi, Luigi and Olm, Matthew and Palm, Noah and Pasolli, Edoardo and Raes, Jeroen and Redinbo, Matthew R and R{\"u}hlemann, Malte and Balfour Sartor, R and Schloss, Patrick D and Schriml, Lynn and Segal, Eran and Shardell, Michelle and Sharpton, Thomas and Smirnova, Ekaterina and Sokol, Harry and Sonnenburg, Justin L and Srinivasan, Sujatha and Thingholm, Louise B and Turnbaugh, Peter J and Upadhyay, Vaibhav and Walls, Ramona L and Wilmes, Paul and Yamada, Takuji and Zeller, Georg and Zhang, Mingyu and Zhao, Ni and Zhao, Liping and Bao, Wenjun and Culhane, Aedin and Devanarayan, Viswanath and Dopazo, Joaquin and Fan, Xiaohui and Fischer, Matthias and Jones, Wendell and Kusko, Rebecca and Mason, Christopher E and Mercer, Tim R and Sansone, Susanna-Assunta and Scherer, Andreas and Shi, Leming and Thakkar, Shraddha and Tong, Weida and Wolfinger, Russ and Hunter, Christopher and Segata, Nicola and Huttenhower, Curtis and Dowd, Jennifer B and Jones, Heidi E and Waldron, Levi} } @article {672, title = {Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.}, journal = {Biol Direct}, volume = {14}, year = {2019}, month = {2019 08 20}, pages = {15}, abstract = {

BACKGROUND: The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles.

RESULTS: Here we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification.

CONCLUSIONS: We demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances.

REVIEWERS: Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.

}, keywords = {biomarkers, Cities, Drug Resistance, Microbial, Machine Learning, Metabolome, Metagenome, metagenomics, Microbiota}, issn = {1745-6150}, doi = {10.1186/s13062-019-0246-9}, author = {Casimiro-Soriguer, Carlos S and Loucera, Carlos and Perez Florido, Javier and L{\'o}pez-L{\'o}pez, Daniel and Dopazo, Joaquin} }