High-resolution bioinformatics
Cluster-free filtering (CFF) of 16S data
The standard approach to analyzing 16S "tag sequence" data (think QIIME…), which relies on clustering reads by sequence similarity into Operational Taxonomic Units (OTUs), underexploits the accuracy of modern Illumina sequencing. I used a publicly available dataset to demonstrate that even single-nucleotide differences are ecologically relevant and can be meaningfully interpreted, and developed a single-nucleotide resolution methodology for analyzing multi-sample datasets (cluster-free filtering; CFF). Thanks to a collaboration with Robert Leach, a programmer, this method was implemented and made available as a professionally interfaced command-line tool. |
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CFF is no longer actively developed and has since been superseded by Ben Callahan's DADA2, which is a wonderful tool that everyone should be using. (Check out his paper in Nature Methods; and also the works by Meren that go way beyond 16S). However, at the time of publication, CFF was the first high-resolution method that was fully automatic and could handle a large (MiSeq-sized) dataset in a reasonable time, and served as my ticket into the high-resolution bioinformatics community.
MT, Robert W Leach, and Ned S Wingreen (2015).“Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution.” ISME J 9: 68-80.
Diet effects on gut microbiota in mice
Looking at data through a better magnifying lens invariably reveals something new, and I was fortunate to develop collaborations with many excellent experimental groups this way. In particular, the Sonnenburg lab (Stanford) investigated how the ability of a mouse gut community to digest dietary fiber declined over generations due to diet-induced extinctions. High-resolution sequence analysis revealed extinction events continuing throughout the 4-generation experiment. The result of this study is an intriguing example of (predictable) evolution of a macroscopic functional property by means of (less predictable) compositional reorganization. Other collaborations are ongoing; check back for updates! And I am always open for more; feel free to contact me. |  |
ED Sonnenburg, SA Smits, MT, SK Higginbottom, NS Wingreen, JL Sonnenburg (2016). “Diet-induced extinctions in the gut microbiota compound over generations.” Nature 529: 212-215.