05409nas a2201477 4500008004100000022001400041245007800055210006900133260001200202300001400214490000700228520122900235653002601464653001401490653001101504653001501515653003501530653002001565653003801585100001901623700001901642700001801661700002301679700002401702700002301726700002101749700002801770700001801798700002501816700002401841700002701865700001801892700002301910700002001933700001501953700002201968700002001990700002702010700002102037700002202058700001802080700001902098700002702117700002002144700001902164700002002183700002102203700001702224700002102241700002302262700002002285700002202305700002002327700001802347700002302365700001802388700002102406700002602427700001902453700001702472700001802489700002402507700001902531700001602550700001702566700001602583700002102599700002102620700002102641700002102662700002202683700002202705700002102727700001602748700001702764700001502781700002102796700001702817700002402834700002202858700002002880700002402900700001802924700001602942700002302958700002102981700002403002700001703026700002603043700002403069700002503093700002403118700002203142700002103164700001703185700001903202700001803221700001803239700001303257700001703270700001603287700001903303700002703322700002003349700001703369700002203386700001903408700001903427700002603446700001903472700002903491700002103520700001603541700002203557700001603579700002003595700002403615700001903639700002403658700002203682700002003704700001803724710003303742710004903775856010703824 2021 eng d a1546-170X00aReporting guidelines for human microbiome research: the STORMS checklist.0 aReporting guidelines for human microbiome research the STORMS ch c2021 11 a1885-18920 v273 a

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 'Strengthening The Organization and Reporting of Microbiome Studies' (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.

10aComputational Biology10aDysbiosis10aHumans10aMicrobiota10aObservational Studies as Topic10aResearch Design10aTranslational Science, Biomedical1 aMirzayi, Chloe1 aRenson, Audrey1 aZohra, Fatima1 aElsafoury, Shaimaa1 aGeistlinger, Ludwig1 aKasselman, Lora, J1 aEckenrode, Kelly1 avan de Wijgert, Janneke1 aLoughman, Amy1 aMarques, Francine, Z1 aMacIntyre, David, A1 aArumugam, Manimozhiyan1 aAzhar, Rimsha1 aBeghini, Francesco1 aBergstrom, Kirk1 aBhatt, Ami1 aBisanz, Jordan, E1 aBraun, Jonathan1 aBravo, Hector, Corrada1 aBuck, Gregory, A1 aBushman, Frederic1 aCasero, David1 aClarke, Gerard1 aCollado, Maria, Carmen1 aCotter, Paul, D1 aCryan, John, F1 aDemmer, Ryan, T1 aDevkota, Suzanne1 aElinav, Eran1 aEscobar, Juan, S1 aFettweis, Jennifer1 aFinn, Robert, D1 aFodor, Anthony, A1 aForslund, Sofia1 aFranke, Andre1 aFurlanello, Cesare1 aGilbert, Jack1 aGrice, Elizabeth1 aHaibe-Kains, Benjamin1 aHandley, Scott1 aHerd, Pamela1 aHolmes, Susan1 aJacobs, Jonathan, P1 aKarstens, Lisa1 aKnight, Rob1 aKnights, Dan1 aKoren, Omry1 aKwon, Douglas, S1 aLangille, Morgan1 aLindsay, Brianna1 aMcGovern, Dermot1 aMcHardy, Alice, C1 aMcWeeney, Shannon1 aMueller, Noel, T1 aNezi, Luigi1 aOlm, Matthew1 aPalm, Noah1 aPasolli, Edoardo1 aRaes, Jeroen1 aRedinbo, Matthew, R1 aRühlemann, Malte1 aSartor, Balfour1 aSchloss, Patrick, D1 aSchriml, Lynn1 aSegal, Eran1 aShardell, Michelle1 aSharpton, Thomas1 aSmirnova, Ekaterina1 aSokol, Harry1 aSonnenburg, Justin, L1 aSrinivasan, Sujatha1 aThingholm, Louise, B1 aTurnbaugh, Peter, J1 aUpadhyay, Vaibhav1 aWalls, Ramona, L1 aWilmes, Paul1 aYamada, Takuji1 aZeller, Georg1 aZhang, Mingyu1 aZhao, Ni1 aZhao, Liping1 aBao, Wenjun1 aCulhane, Aedin1 aDevanarayan, Viswanath1 aDopazo, Joaquin1 aFan, Xiaohui1 aFischer, Matthias1 aJones, Wendell1 aKusko, Rebecca1 aMason, Christopher, E1 aMercer, Tim, R1 aSansone, Susanna-Assunta1 aScherer, Andreas1 aShi, Leming1 aThakkar, Shraddha1 aTong, Weida1 aWolfinger, Russ1 aHunter, Christopher1 aSegata, Nicola1 aHuttenhower, Curtis1 aDowd, Jennifer, B1 aJones, Heidi, E1 aWaldron, Levi1 aGenomic Standards Consortium1 aMassive Analysis and Quality Control Society uhttps://www.clinbioinfosspa.es/content/reporting-guidelines-human-microbiome-research-storms-checklist02126nas a2200289 4500008004100000022001400041245015000055210006900205260001500274300000700289490000700296520113800303653001501441653001101456653003101467653002101498653001501519653001501534653001701549653001501566100003301581700002001614700002701634700002601661700002001687856012901707 2019 eng d a1745-615000aAntibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.0 aAntibiotic resistance and metabolic profiles as functional bioma c2019 08 20 a150 v143 a

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.

10abiomarkers10aCities10aDrug Resistance, Microbial10aMachine Learning10aMetabolome10aMetagenome10ametagenomics10aMicrobiota1 aCasimiro-Soriguer, Carlos, S1 aLoucera, Carlos1 aFlorido, Javier, Perez1 aLópez-López, Daniel1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/antibiotic-resistance-and-metabolic-profiles-functional-biomarkers-accurately-predict