Hutlab

We are excited to be joining the Informatics Technology for Cancer Research (ITCR) program @ NCI. Our work will develop methods to detect cancer-associated viruses and microbial variation from metagenomes, revealing previously hidden contributors to colorectal carcinogenesis. R21 kicking off! 🎉🎉

A big thanks to The Center for Microbiome Informatics and Therapeutics at @mitpress.bsky.social for hosting Curtis for a talk on the endogenous human virome!

Eric @efranzosa.bsky.social and Kelsey from the lab started teaching BST 281 - Genomic Data Manipulation, a couple weeks ago! It covers genomic sequence analysis, high throughput data analysis and cool topics that are currently of importance in the field 🧑‍🎓

Fun time at Time Out market!

Huge thanks to everyone who took BST272 (Computing Environments for Biology) this year! It’s an intense few weeks as intro to working at the terminal, and we hope folks build on it in courses like BST281. This was our first year integrating AI into the curriculum—thanks for all the great feedback!

Aaaaand this one is from Curtis!

Boston recently had its biggest snow of the season so far. Here are some snow pictures from Hutlab members! ❄️🌨️🐶

Baby-to-baby strain transmission shapes the developing gut microbiome - Nature A metagenomic survey of babies attending the first year of nursery detected extensive baby-to-baby microbial strain transmission, pointing to social interactions in infancy as crucial drivers of infan...

🥁 NEW

Our study on infant gut microbiome and #straintransmission is now published in @nature.com:

doi.org/10.1038/s415...

1/10

MSPCA-Angell This holiday season, Baby Duff received the gift of… poop! Baby Duff recently underwent his third fecal transplant to help manage his chronic intermittent diarrhea, which he has suffered from since....

MSPCA-Angell with FMT for cats!!
www.facebook.com/mspcaangell/...

MaAsLin 3: refining and extending generalized multivariable linear models for meta-omic association discovery - Nature Methods MaAsLin 3 is a comprehensive and flexible framework for microbiome association studies with expanded toolsets of statistical models, tests and types of inference.

MaAsLin 3: a comprehensive, flexible framework for microbiome association studies. @hutlab.bsky.social

www.nature.com/articles/s41...

Congratulations to Yehoon Jo @yehoon.bsky.social who finished his PhD lab rotation in the Hutlab last semester and is joining the lab as a PhD student! 🎉

Congratulations to Sagun Maharjan @sagunmaharjann.bsky.social for graduating with a Masters in Computer Science from Georgia Tech while working at the Hutlab!

Clarivate Highly Cited Researchers 2025 The Highly Cited Researchers 2025 list identifies and celebrates individuals who have demonstrated significant and broad influence in their fields of research. Through rigorous selection criteria and ...

Congratulations to Curtis and Eric Franzosa from the lab for being featured in the Clarivate Highly Cited Researchers of 2025 (in both Biology/Biochemistry and Microbiology categories)! clarivate.com/highly-cited... 🎉

Happy holidays from the Hutlab!! 🎉☃️

A huge thanks to Cancer Grand Challenges @cancergrand.bsky.social , @cam.ac.uk and Dr. Nic Walton for organizing a two-day Data Workshop to address data and computing challenges across the group. Chahat gave a presentation of his work, and Curtis led a session on best practices for use of AI/ML!

Huge thanks to Curtis for spending some time with us here in Happy Valley - if you missed his talk, you can check it out here on our YouTube channel: youtu.be/andhyvSyt6c?...

10/ Special thanks to our collaborators @hillspet.bsky.social for their contributions and feedback throughout the development of Parathaa.

The bioBakery help forum A place to ask for help with bioBakery tools

9/ Any feedback or issues can be directed to our forum at: forum.biobakery.org.

8/ Parathaa is openly available for use by the community and can be used to assign amplicons to both SILVA and GTDB taxonomy. github.com/biobakery/pa...

7/ Moreover, application of Parathaa to real-world data identified cases where taxonomic labels found within databases were likely incorrect or incompatible with sequence phylogeny.

6/ We found that Parathaa performed in F1, precision, and recall just as well or better than commonly used amplicon assignment methods while also allowing for finer species resolution.

5/ We validated Parathaa on seven synthetic Silva datasets and historic holdout data from GTDB using both full‑length 16S rRNA amplicons and amplicons of the V1V2 and V4V5 regions.

4/ Parathaa works in two main steps:
- Build an optimized, primer‑specific map linking phylogenetic placement to taxonomy.
- Apply this map to assign zero or more taxonomic labels to each sequence.

3/ Parathaa aims to address these issues by directly modelling taxonomic sequence ambiguity within sequenced amplicon regions: i.e. allowing for assignments to multiple taxonomic labels when phylogenetically warranted.

2/ Amplicon data often contains unique biases due to primer incompatibilities and the inability to differentiate between certain microbes due to low sequence variability, especially at the species level.

Capturing sequence ambiguity among taxa in a primer-specific manner to improve taxonomic classification of amplicon sequencing Abstract. Amplicon sequencing, a common strategy to taxonomically profile microbial communities, is relatively low cost and high throughput. However, it is

1/ We are excited to announce Parathaa: a new method for improving taxonomic classification of amplicon sequencing data. Parathaa aims to directly model sequence ambiguity to identify amplicons to the finest taxonomic level possible. academic.oup.com/nar/article/...

Eric also teaches graduate courses in Bioinformatics and Python programming at the School alongside the Lab’s own microbiome training workshops. Eric is an avid tabletop gamer outside of work (and inside, where appropriate) and never misses an episode of Saturday Night Live :)

Meet @efranzosa.bsky.social!

Eric was recently reappointed as a Senior Research Scientist at @hsph.harvard.edu (congrats!🎉). Eric’s research focuses on computational methods development for microbiome analysis, with an emphasis on functional characterization.

Thank you to @psumbiome.bsky.social for inviting Curtis recently for a talk at their microbiome seminar series (www.huck.psu.edu/institutes-a...) & for a great visit. Its amazing to see such an institutionally well supported microbiome community, and the seminar series hosts really diverse topics!

Meet Sagun!

Sagun completed his Master's in Computer Science at Georgia Tech, specializing in Machine Learning and Interactive Intelligence. He now works as a Software Developer at the Harvard Chan School and the Hutlab, building and maintaining large-scale data infrastructure and tools.