Science
The ability to generate data that describes the microbial world continues to grow rapidly. This requires that we develop and assess computational tools that allow us to synthesize these data to tell robust and compelling stories that solve important problems. We have been at the fore front of efforts to develop tools that are widely used by microbial ecologists for the past 15 years. Our tools have facilitated the analysis of 16S rRNA gene, metagenomic, and metatranscriptomic sequence data, metabolomics data, and clinical data.
Take a look at our software for links to documentation and other information, and check out our papers below:
Afiaz A, Ivanov A, Chamberlin J, Hanauer D, Savonen C, Goldman MJ, Morgan M, Reich M, Getka A, Holmes A, Pati S, Knight D, Boutros PC, Bakas S, Caporaso JG, Del Fiol G, Hochheiser H, Haas B, Schloss PD, Eddy JA, Albrecht J, Fedorov A, Waldron L, Hoffman AM, Bradshaw RL, Leek JT, Wright C.
2024.
Best practices to evaluate the impact of biomedical research software-metric collection beyond citations.
Bioinformatics.
40:
btae469.
DOI:
10.1093/bioinformatics/btae469.
Schloss PD.
2024.
Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses.
mSphere.
DOI:
10.1128/msphere.00354-23.
Schloss PD.
2024.
Preprint: Removal of rare amplicon sequence variants from 16S rRNA gene sequence surveys biases the interpretation of community structure data.
DOI:
10.1101/2020.12.11.422279.
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