Congratulations to all on this recently published paper in the journal Microbiome, from BioBE co-authors James Meadow & Ashley Bateman, with collaborators Rachel Adams & Holly Bik. This project was born during a 2013 NESCent Catalysis Meeting on “Evolution in the Indoor Biome”. Hoping to address broad-scale ecological questions with as many high-throughput sequencing datasets as possible, the published manuscript describes several significant technical and biological findings. See this microbenet post for a summary of the most important results.
This past September, Sloan-funded biologists Ashley Bateman, James Meadow, Rachel Adams, and Holly Bik met at UC-Berkeley to begin collaboration on an exciting new project!
The past few years have seen a dramatic increase in the number of microbiological studies undertaken in the indoor environment. It seems that these studies have arrived at the same general conclusions regarding microbial richness and diversity, but they also suggest that different processes are structuring microbiological communities differently depending on many variables. We began a meta-analysis of the publicly-available indoor biome data sets to compile and assess the state of current knowledge on the microbiology of the built environment. We hope to use the online QIIME database to analyze 16s and fungal clone data sets from multiple sequencing platforms. This kind of meta-analysis will hopefully help us to answer some of the following questions:
1. Are there consistent patterns for the processes (geography, building type, etc) structuring microbial communities indoors?
2. Can we identify consistent source habitats for different habitats in the BE?
3. Are there similar patterns between fungi and bacteria?
4. How does study design/sequencing method (e.g. clones vs. 454 vs. Illumina data) affect patterns?
5. Related to point 4, a meta-analysis will inform further studies’ experimental design, elucidating where/when/how we currently do not have any/few data (i.e. undeveloped nations, winter, rural communities, fungi in general).
We are hoping to undertake computational analysis in October, once all of our datasets have been put together!