Published in BioRxiv, 2020
SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features.
Recommended citation: Shaffer, M.†, Thurimella, K.†, & Lozupone, C. A. (2020). SCNIC: Sparse Correlation Network Investigation for Compositional Data. bioRxiv. † joint first-authors http://kthurimella.github.io/files/SCNIC-preprint.pdf
Published in BMC Bioinformatics, 2019
Untargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. We address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to annotate which compounds in the metabolome could have been produced by bacteria present or the host, to evaluate pathway enrichment of host verses microbial metabolites, and to visualize which compounds may have been produced by host versus microbial enzymes in KEGG pathway maps.
Recommended citation: Shaffer, M., Thurimella, K., Quinn, K., Doenges, K., Zhang, N., Bokatzian, S., Reisdorph, N. and Lozupone, C., 2019. AMON: Annotation of metabolite origins via networks to integrate microbiome and metabolome data. BMC Bioinformatics, 2019;20(1):614. Published 2019 Nov 28. doi:10.1186/s12859-019-3176-8 PMID: 31779604 http://kthurimella.github.io/files/AMON.pdf