Objectives

To enable interactive visualization of the vaginal microbiome across the pregnancy and facilitate discovery of novel insights and generation of new hypotheses.

Material and methods

Vaginal Microbiome Atlas during Pregnancy (VMAP) was created with R shiny to generate visualizations of structured vaginal microbiome data from multiple studies.

Results

VMAP (http://vmapapp.org) visualizes 3880 vaginal microbiome samples of 1402 pregnant individuals from 11 studies, aggregated via open-source tool MaLiAmPi. Visualized features include diversity measures, VALENCIA community state types, and composition (phylotypes, taxonomy) that can be filtered by various categories.

Discussion

This work represents one of the largest and most geographically diverse aggregations of the vaginal microbiome in pregnancy to date and serves as a user-friendly resource to further analyze vaginal microbiome data and better understand pregnancies and associated outcomes.

Conclusion

VMAP can be obtained from https://github.com/msirota/vmap.git and is currently deployed as an online app for non-R users.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.