mBodyMap: a curated database for microbes across human body and their associations with health and diseases

Abstract mBodyMap is a curated database for microbes across the human body and their associations with health and diseases. Its primary aim is to promote the reusability of human-associated metagenomic data and assist with the identification of disease-associated microbes by consistently annotating the microbial contents of collected samples using state-of-the-art toolsets and manually curating the meta-data of corresponding human hosts. mBodyMap organizes collected samples based on their association with human diseases and body sites to enable cross-dataset integration and comparison. To help users find microbes of interest and visualize and compare their distributions and abundances/prevalence within different body sites and various diseases, the mBodyMap database is equipped with an intuitive interface and extensive graphical representations of the collected data. So far, it contains a total of 63 148 runs, including 14 401 metagenomes and 48 747 amplicons related to health and 56 human diseases, from within 22 human body sites across 136 projects. Also available in the database are pre-computed abundances and prevalence of 6247 species (belonging to 1645 genera) stratified by body sites and diseases. mBodyMap can be accessed at: https://mbodymap.microbiome.cloud.

[1]  P. Bork,et al.  Human and preclinical studies of the host–gut microbiome co-metabolite hippurate as a marker and mediator of metabolic health , 2021, Gut.

[2]  Puzi Jiang,et al.  Treatment regimens may compromise gut-microbiome-derived signatures for liver cirrhosis. , 2021, Cell metabolism.

[3]  G. Hillebrand,et al.  Temporal Variation of the Facial Skin Microbiome: A 2-Year Longitudinal Study in Healthy Adults. , 2021, Plastic and reconstructive surgery.

[4]  X. Bai,et al.  New Insights Into the Skin Microbial Communities and Skin Aging , 2020, Frontiers in Microbiology.

[5]  Wenlian Zhang,et al.  MicroPhenoDB Associates Metagenomic Data with Pathogenic Microbes, Microbial Core Genes, and Human Disease Phenotypes , 2020, bioRxiv.

[6]  Annika Lundqvist,et al.  The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study. , 2020, Cell metabolism.

[7]  P. Bork,et al.  Diversity within species: interpreting strains in microbiomes , 2020, Nature Reviews Microbiology.

[8]  Luis Pedro Coelho,et al.  Statin therapy is associated with lower prevalence of gut microbiota dysbiosis , 2020, Nature.

[9]  Xingming Zhao,et al.  Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment , 2020, BMC Genomics.

[10]  Laxmi Parida,et al.  Human Skin, Oral, and Gut Microbiomes Predict Chronological Age , 2020, mSystems.

[11]  M. Eisenstein.  The hunt for a healthy microbiome , 2020, Nature.

[12]  Lu Sun,et al.  NCBI Taxonomy: a comprehensive update on curation, resources and tools , 2020, Database J. Biol. Databases Curation.

[13]  Xingming Zhao,et al.  Metagenomic Analysis of Common Intestinal Diseases Reveals Relationships among Microbial Signatures and Powers Multidisease Diagnostic Models , 2019, mSystems.

[14]  Xing-Ming Zhao,et al.  GMrepo: a database of curated and consistently annotated human gut metagenomes , 2019, Nucleic Acids Res..

[15]  Kevin S. Bonham,et al.  Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases , 2019, Nature.

[16]  Jennifer M. Fettweis,et al.  The Integrative Human Microbiome Project , 2019, Nature.

[17]  Paul Theodor Pyl,et al.  Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer , 2019, Nature Medicine.

[18]  Christoph A. Merten,et al.  Antibiotics-induced monodominance of a novel gut bacterial order , 2019, Gut.

[19]  A. Gasbarrini,et al.  What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases , 2019, Microorganisms.

[20]  Thomas M. Keane,et al.  The European Nucleotide Archive in 2018 , 2018, Nucleic Acids Res..

[21]  P. Bork,et al.  Population-level analysis of Blastocystis subtype prevalence and variation in the human gut microbiota , 2018, Gut.

[22]  Gary Tse,et al.  Association Between Bacteremia From Specific Microbes and Subsequent Diagnosis of Colorectal Cancer. , 2018, Gastroenterology.

[23]  P. Bork,et al.  Metagenomic analysis of gut microbial communities from a Central Asian population , 2018, BMJ Open.

[24]  J. Nielandt,et al.  Disbiome database: linking the microbiome to disease , 2018, BMC Microbiology.

[25]  Xing-Ming Zhao,et al.  MVP: a microbe–phage interaction database , 2017, Nucleic Acids Res..

[26]  M. Yassour,et al.  A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients , 2017, Genome Medicine.

[27]  F. Alaei-Shahmiri,et al.  Comparison of gut microbiota in adult patients with type 2 diabetes and healthy individuals. , 2017, Microbial pathogenesis.

[28]  Christian von Mering,et al.  MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis , 2017, Bioinform..

[29]  David Torrents,et al.  Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug , 2017, Nature Medicine.

[30]  Morris A. Swertz,et al.  Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity , 2016, Science.

[31]  Anumantha Kanthasamy,et al.  Gut microbiome in health and disease: Linking the microbiome-gut-brain axis and environmental factors in the pathogenesis of systemic and neurodegenerative diseases. , 2016, Pharmacology & therapeutics.

[32]  P. Bork,et al.  Gut Microbiota Linked to Sexual Preference and HIV Infection , 2016, EBioMedicine.

[33]  Jun Wang,et al.  Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota , 2015, Nature.

[34]  Adolfo Suárez,et al.  Intestinal microbiota in health and disease: role of bifidobacteria in gut homeostasis. , 2014, World journal of gastroenterology.

[35]  Byunghan Lee,et al.  CASPER: context-aware scheme for paired-end reads from high-throughput amplicon sequencing , 2014, BMC Bioinformatics.

[36]  Anthony A. Fodor,et al.  Microbial genomic analysis reveals the essential role of inflammation in bacteria-induced colorectal cancer , 2014, Nature Communications.

[37]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

[38]  M. Meyerson,et al.  Microbes and Inflammation in Colorectal Cancer , 2013, Cancer Immunology Research.

[39]  Qiang Feng,et al.  A metagenome-wide association study of gut microbiota in type 2 diabetes , 2012, Nature.

[40]  Timothy L. Tickle,et al.  Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment , 2012, Genome Biology.

[41]  C. Huttenhower,et al.  Metagenomic microbial community profiling using unique clade-specific marker genes , 2012, Nature Methods.

[42]  Katherine H. Huang,et al.  Structure, Function and Diversity of the Healthy Human Microbiome , 2012, Nature.

[43]  D. Gevers,et al.  The interpersonal and intrapersonal diversity of human-associated microbiota in key body sites. , 2012, The Journal of allergy and clinical immunology.

[44]  M. Blaser,et al.  The human microbiome: at the interface of health and disease , 2012, Nature Reviews Genetics.

[45]  Rasko Leinonen,et al.  The sequence read archive: explosive growth of sequencing data , 2011, Nucleic Acids Res..

[46]  C. Huttenhower,et al.  Metagenomic biomarker discovery and explanation , 2011, Genome Biology.

[47]  R. Knight,et al.  Moving pictures of the human microbiome , 2011, Genome Biology.

[48]  P. Bork,et al.  A human gut microbial gene catalogue established by metagenomic sequencing , 2010, Nature.

[49]  E. Mardis,et al.  An obesity-associated gut microbiome with increased capacity for energy harvest , 2006, Nature.

[50]  Yang Wang,et al.  An analysis of human microbe‐disease associations , 2017, Briefings Bioinform..