Claire Duvallet

Claire Duvallet

STEM equity @ Science for America | formerly: data science @ Biobot Analytics, PhD @ MIT, Luce Scholar in Cambodia | public health data, microbiome, personalized medicine | gender and racial equity in STEM | she/hers

Publications

Academic publications

Check out my Google Scholar profile for a full list of my publications.

A highlighted subset are listed below - these are papers I played a large role in or am otherwise proud to have as part of my portfolio.

Claire Duvallet, Sean M. Gibbons, Thomas Gurry, Rafael A. Irizarry, and Eric J. Alm. “Meta-analysis of gut microbiome studies identifies disease-specific and shared responses.” Nature Communications (2017). doi: 10.1038/s41467-017-01973-8. Associated github: cduvallet/microbiomeHD. Associated data:zenodo record 840333.

This was my primary PhD paper, and the work I’m proudest of by far. I re-analyzed 30 different microbiome datasets to figure out whether there were consistent associations with disease or if the same bugs showed up all the time. I’m super proud of how the paper is written, and that I also made all my data available and analyses and figures reproducible via github. I learned the majority of my transferrable technical skills from this work.

Claire Duvallet, Fuqing Wu, Kyle A. McElroy, Maxim Imakaev … Eric J. Alm, and Mariana Matus. “Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States.” ACS ES&T Water (2022). doi: 10.1021/acsestwater.1c00434

I spearheaded Biobot’s first big Biobot-led publication. We presented our work scaling a nationwide wastewater surveillance system for Covid-19. This was before we knew how well WBE for Covid-19 would work, and at the time, the way the wastewater and clinical case time series aligned was truly mind-blowing.

Amy Xiao … Claire Duvallet … Janelle Thompson, and Eric J Alm. “Metrics to relate COVID-19 wastewater data to clinical testing dynamics.” Water research (2022). doi: 10.1016/j.watres.2022.118070

I worked closely with Amy to really lay out how we thought that wastewater data should be used: in combination with other public health data. Importantly, this paper demonstrated what we’d theoretically described in our Making Waves paper: wastewater’s lead time changes throughout the pandemic, and depends a lot on other public health factors!

Scott W Olesen, Maxim Imakaev, and Claire Duvallet. “Making waves: Defining the lead time of wastewater-based epidemiology for COVID-19.” Water Research (2021). doi: 10.1016/j.watres.2021.117433

This perspective came from being asked “what is wastewater’s lead time?” a million times, and wanting to give a better answer than “it depends.” The answer is still “it depends,” of course, but in this piece we go through what it depends on a bit more.

Wei Lin Lee … Claire Duvallet … Mariana Matus, Janelle Thompson, and Eric J Alm. “Quantitative SARS-CoV-2 alpha variant B.1.1.7 tracking in wastewater by allele-specific RT-qPCR.” Environmental Science & Technology Letters (2021). doi: 10.1021/acs.estlett.1c00375

Our collaborators in Singapore developed a qPCR-based assay to detect the then-new B.1.1.7 variant. We provided the samples & analysis to validate their method on real wastewater samples. This was before we knew that variants would be a thing, and before we had any idea that sequencing wastewater would work. Biobot actually launched this assay as a paid product for a hot sec, for which I served as the technical lead.

Jill S McClary-Gutierrez … Claire Duvallet … Alexandria B Boehm, Rolf U Halden, Kyle Bibby, Jeseth Delgado Vela. “Standardizing data reporting in the research community to enhance the utility of open data for SARS-CoV-2 wastewater surveillance.” Environmental Science: Water Research & Technology (2021). doi: 10.1039/D1EW00235J

I was invited to join a large workshop hosted by the NSF Research Coordination Network (RCN) on Wastewater Surveillance for SARS-CoV-2 and Emerging Public Health Threats to figure out what metadata was actually crucial to report on in wastewater-based epidemiology. I enjoyed providing my perspective from Biobot’s experience working at scale, and had fun meeting folks in the WBE space.

Claire Duvallet. “Data detectives, self-love, and humility: a research parasite’s perspective.” GigaScience (2020). doi: 10.1093/gigascience/giz148.

As a winner of the Research Parasite award, I got to write this perspective, stepping on my soapbox and sharing lessons learned from my meta-analysis PhD paper.

Mariana Matus, Claire Duvallet, Newsha Ghaeli, Melissa Kido Soule, Krista Longnecker, Ilana Brito, Carlo Ratti, Elizabeth B. Kujawinski, Eric Alm. “24-hour multi-omics analysis of residential sewage reflects human activity and informs public health.” bioRxiv (2019) doi: 10.1101/728022.

I worked closely with Mariana on this paper during grad school and at the beginning of my time at Biobot. This was her PhD work, which laid the foundations for Biobot. I did the majority of the metabolomics work on this paper, which sparked my love and admiration for this non-genomics data type.

Claire Duvallet, Caroline Zellmer, Pratik Panchal, Shrish Budree, Madji Osman, and Eric Alm. “Framework for rational donor selection in fecal microbiota transplant clinical trials.” PLoS ONE (2019) doi: 10.1371/journal.pone.0222881. Associated github: cduvallet/donor-selection

This was the final chapter of my thesis, in which I put together everything I’d learned from doing clinically-motivated microbiome research into a theoretical framework for how you would think about approaching donor selection in FMT trials.

Chengzhen Dai, Claire Duvallet, An Ni Zhang, Mariana Matus, Newsha Ghaeli, Shinkyu Park, Noriko Endo, Siavash Isazadeh, Kazi Jamil, Carlo Ratti, and Eric Alm. “Multi-site sampling and risk prioritization reveals the public health relevance of antibiotic resistance genes found in sewage environments.” bioRxiv (2019). doi: 10.1101/562496.

I mentored an excellent master’s student on this paper. We applied a cool framework developed by a postdoc in the lab, An-ni, to get past just counting genes and move toward a “so what?” behind antimicrobial resistance genes in wastewater.

Claire Duvallet, Kara Larson, Scott Snapper, Sonia Iosim, Ann Lee, Katherine Freer, Kara May, Eric Alm, and Rachel Rosen. “Aerodigestive sampling reveals altered microbial exchange between lung, oropharyngeal, and gastric microbiomes in children with impaired swallow function.” PLOS ONE (2019). doi: 10.1371/journal.pone.0216453. Associated github: cduvallet/aspiration-analysis-public

This was my first thesis chapter. I worked independently with a clinical collaborator to analyze a 16S dataset she’d generated from pediatric patients’ lung, throat, and nose samples. It was a tough project because of the niche field, few previous studies, and really wacko microbiome data, but I’m super proud of having pulled out some interesting findings that made clinical sense.

Keegan Korthauer, Patrick K Kimes, Claire Duvallet, Alejandro Reyes, Ayshwarya Subramanian, Mingxiang Teng, Chinmay Shukla, Eric J Alm, and Stephanie C Hicks. “A practical guide to methods controlling false discoveries in computational biology.” Genome Biology (2019). doi: 10.1186/s13059-019-1716-1. Associated github: pkimes/benchmark-fdr

I hang out with statisticians sometimes! I was thrilled to collaborate with a bunch of folks from Rafa Irizarry’s lab applying multiple hypothesis correction methods on a variety of biological data types. I contributed the microbiome datasets, and struggled through learning R to keep up with the cool kids!

Sean M. Gibbons, Claire Duvallet, and Eric J. Alm. “Correcting for batch effects in case-control microbiome studies.” PLoS Computational Biology (2018) 14(4): e1006102. doi: 10.1371/journal.pone.0176335

Early in my PhD, I worked with Sean to develop a super simple statistical method to correct for batch effects. We used the datasets I’d gathered for my meta-analysis, and I also coded up the method into a Python package and also put it in QIIME2.

Non-peer reviewed professional content

[White paper] Claire Duvallet, Matthew Boyce. “Normalizing wastewater data.” Biobot Analytics. (2023)

[Blog post] Claire Duvallet. “Building a new data visualization: from basic to brilliant!.” Biobot Analytics. (2023)

[White paper] Scott Olesen, Cristin Young, Claire Duvallet. “The Effect of Septic Systems on Wastewater-Based Epidemiology.” Biobot Analytics. (2022)

[Tutorial] Claire Duvallet. “Updating your qiime2 plugin.” QIIME 2 developer docs. (2019).

[Blog post] Claire Duvallet. “Scientific discovery from a clinical study: surprises from the lung and stomach microbiomes.” Nature Microbiology Community Forum. May 2019.

[Tutorial] Claire Duvallet and Mehrbod Estaki. “QIIME 2 for Experienced Microbiome Researchers.” QIIME 2 docs. (2018).

[Tutorial] Claire Duvallet. “Developing a plug-in for dummies.” QIIME 2 developer docs. (2018).

[Tutorial] Claire Duvallet. “Publishing your plugin on conda.” QIIME 2 developer docs. (2018).

[Blog post] Claire Duvallet. “Fuzzy zeros in percentile normalization method to correct for batch effects.” microBEnet: the microbiology of the Built Environment network. June 2018.

[Blog post] Claire Duvallet. “Beyond dysbiosis: disease-specific and shared microbiome responses to disease.” Nature Microbiology Community Forum. December 2017.

[Dataset] Claire Duvallet, Sean M. Gibbons, Thomas Gurry, Rafael A. Irizarry, and Eric J. Alm. (2017). MicrobiomeHD: the human gut microbiome in health and disease. Zenodo. doi: 10.5281/zenodo.797943

Software

Percentile normalization
Correcting batch effects in case-control microbiome studies. (Gibbons et al., 2018)

Distribution-based OTU calling
New implementation of Preheim et al.’s distribution-based OTU clustering algorithm. (Preheim et al., 2013; Olesen et al., 2017).

Amplicon sequencing pipeline
End-to-end pipeline to process 16S data.

Match HMDB
Script to match mz’s to the HMDB database.

Other

[Blog post] Claire Duvallet. “A Well-Kept Secret for Finding a Job post-PhD.” MIT Graduate Admissions blog (2020).

[Blog post] Claire Duvallet. “Learning to Engage in Deep Conversations.” MIT Graduate Admissions blog (2018).

[Resource] Claire Duvallet and Monika Avello. “Graduate student support resources flowchart.” I and another student on iREFS created this flowchart to help graduate students find support at MIT.