MOTIVATION: Microbiome datasets are often constrained by sequencing limitations. GenBank is the largest collection of publicly available DNA sequences, which is maintained by the National Center of Biotechnology Information (NCBI). The metadata of GenBank records are a largely understudied resource and may be uniquely leveraged to access the sum of prior studies focused on microbiome composition. Here, we developed a computational pipeline to analyze GenBank metadata, containing data on hosts, microorganisms and their place of origin. This work provides the first opportunity to leverage the totality of GenBank to shed light on compositional data practices that shape how microbiome datasets are formed as well as examine host-microbiome relationships. RESULTS: The collected dataset contains multiple kingdoms of microorganisms, consisting of bacteria, viruses, archaea, protozoa, fungi, and invertebrate parasites, and hosts of multiple taxonomical classes, including mammals, birds and fish. A human data subset of this dataset provides insights to gaps in current microbiome data collection, which is biased towards clinically relevant pathogens. Clustering and phylogenic analysis reveals the potential to use these data to model host taxonomy and evolution, revealing groupings formed by host diet, environment and coevolution. AVAILABILITY AND IMPLEMENTATION: GenBank Host-Microbiome Pipeline is available at https://github.com/bcbi/genbank_holobiome. The GenBank loader is available at https://github.com/bcbi/genbank_loader. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Microbiota contribute to many dimensions of host phenotype, including disease.To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples.Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions.We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight.Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease.Our method corrected the inflation of pvalues in standard association tests and showed that only a small subset of associations is directly linked to the disease.Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the braingut axis as important factors in the inflammatory bowel disease. Author summaryMicrobiomes are important for better health, sustainable agriculture, and climate management.Since experimental studies of natural microbial communities are often challenging, microbiome wide association studies (MWAS) have been the primary method to reveal the connection between specific microbes and host phenotype.MWAS have established that many diseases are associated with a complex dysbiosis driven by a large number of microbes.We show that many of these associations may not reflect a mechanistic link with the disease, but arise instead due to interspecific interactions such as cross-feeding and competition for resources.We also propose a method grounded in the maximum entropy models of statistical physics to separate direct from indirect associations.Using both synthetic and real microbiome data, we show that this method detects only direct associations, identifies most predictive features of microbiomes, and avoids p-value inflation.We demonstrate the power of this method on one of the largest microbiome data sets on inflammatory bowel disease.