Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA.
Institute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, USA.
Nat Biotechnol. 2024 May;42(5):790-802. doi: 10.1038/s41587-023-01872-y. Epub 2023 Sep 11.
The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.
人类和其他宿主相关微生物组研究的文献正在迅速扩展,但很难对已发表的宿主相关微生物组差异丰度特征的结果进行系统比较。我们介绍了 BugSigDB,这是一个社区编辑的数据库,其中包含来自已发表的差异丰度研究的经过人工整理的微生物特征,这些特征附有关于研究地理位置、健康结果、宿主身体部位以及使用受控词汇的实验、流行病学和统计方法的信息。该数据库的初始版本包含来自三个宿主物种的 >600 项已发表研究的 >2500 个经过人工整理的特征,可实现特征相似性、分类群富集、共现和共排斥以及共识特征的高通量分析。这些数据可用于评估微生物组在实验条件、环境或身体部位内和之间的差异丰度。全数据库分析揭示了报告独立研究的特征具有最高一致性的实验条件,并确定了与疾病相关的特征之间的共同之处,包括口腔病原体经常被引入肠道。