Tal Korem

Korem Lab - Microbiome Systems Biology @ Columbia | New York, USA We apply systems biology approaches to decipher the metabolic interactions between the microbiome and its human host in diverse clinical settings, aiming towards personalized microbiome-based therapeu...

We are hiring a postdoc - come work with us (www.koremlab.science) at the intersection of #microbiome, data science, and women's health!
Message or email me if interested. 🖥️ 🧬

Korem Lab - Microbiome Systems Biology @ Columbia | New York, USA We apply systems biology approaches to decipher the metabolic interactions between the microbiome and its human host in diverse clinical settings, aiming towards personalized microbiome-based therapeu...

We are hiring a postdoc - come work with us (www.koremlab.science) at the intersection of #microbiome, data science, and women's health!
Message or email me if interested. 🖥️ 🧬

I am seeking a postdoc for my group at UCLA. We work at the intersection of population genetics x microbiome (garud.eeb.ucla.edu). If interested, please message me!

This will also likely reduce the number of study sections, firing SROs and making them less specialized.

Guidelines for preventing and reporting contamination in low-biomass microbiome studies Nature Microbiology - In this Consensus Statement, the authors outline strategies for processing, analysing and interpreting low-biomass microbiome samples, and provide recommendations to minimize...

Hope this is useful - consensus statement "Guidelines for preventing and reporting contamination in low-biomass microbiome studies" rdcu.be/er3Io

The trolley problem graphic where one side is several people tied to the train tracks except the other side has no one on the tracks. A man is standing holding a lever trying to decide which track the trolley should take. The text reads “You can pull the lever, but to do so you must wear a mask at the grocery store.”

Not getting much attention, except in a recent NYTimes story, is a provision to increase the tax on university endowments and those of other nonprofits.

Incorporating scale uncertainty in microbiome and gene expression analysis as an extension of normalization - Genome Biology Statistical normalizations are used in differential analyses to address sample-to-sample variation in sequencing depth. Yet normalizations make strong, implicit assumptions about the scale of biologic...

New paper in Genome Biology!

genomebiology.biomedcentral.com/articles/10....

We introduce scale models, a generalization of normalizations that explciitly account for uncertainty in biological system scale (e.g., microbial load).

as a resident of syracuse, ny, a rust belt town that used to be an economic epicenter for the nation: syracuse university is our largest local employer now and if it goes under, so does my town, which has the largest concentration of child poverty in the nation.

Antibiotic research at Harvard lab threatened by federal funding cuts Microbiologist Michael Baym studies antibiotic resistance at Harvard Medical School. He lost millions in federal funding this week.

"I'm still in shock. I know that there have been political issues around Harvard in recent weeks, but antibiotic resistance isn't one of them." My conversation with Harvard microbiologist @baym.lol, one of many researchers there who just lost millions in fed. grants. www.wbur.org/news/2025/05...

Our paper explaining why Gihawi et al. failed to prove an error in the normalization used by the 2020 cancer #microbiome analysis now out as a Matters Arising in @asm.org #mSystems (w/ @george-austin.bsky.social) 🖥️ 🧬

Thread explaining the key points below.

journals.asm.org/doi/10.1128/...

Our paper explaining why Gihawi et al. failed to prove an error in the normalization used by the 2020 cancer #microbiome analysis now out as a Matters Arising in @asm.org #mSystems (w/ @george-austin.bsky.social) 🖥️ 🧬

Thread explaining the key points below.

journals.asm.org/doi/10.1128/...

Come and work with me!

The Nature Micro team is expanding and we're looking for someone to champion microbial ecology, plant micro & related areas for the journal

Knowledge of microbial ecology/plant micro is desirable but we're open to applications from all microbiologists

Link below 👇

Congrats!

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models - Nature Microbiology DEBIAS-M corrects technical variability in microbiome data in a manner both interpretable and suitable for machine learning. In extensive benchmarks, DEBIAS-M facilitates robust analyses that generali...

OUT NOW: Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

@tkorem.bsky.social & co

#microsky #microbiomesky 🧪

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

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models Nature Microbiology - DEBIAS-M corrects technical variability in microbiome data in a manner both interpretable and suitable for machine learning. In extensive benchmarks, DEBIAS-M facilitates...

A hopefully paywall-free link:
www.nature.com/articles/s41...

DEBIAS-M is available as a Python package (korem-lab.github.io/DEBIAS-M/ or just pip install debias-m). It works with any microbiome read count or relative abundance matrices, and any paired metadata. 7/7

Boxplots showing performance on metabolite prediction (each point is a different metabolite). Y-axis is Spearman correlation, x-axis are different methods. Prediction using raw data is nearly random (median correlation of ~0). MelonnPan improves substantially to a median of ~.25. DEBIAS-M and multi-task DEBIAS-M improve this further, with a median Spearman of ~.3.

Its multi-task version allows DEBIAS-M to learn models for multiple tasks at the same time, further increasing its performance. This is particularly useful for tasks such as metabolite level predictions, where we want to predict multiple metabolite levels using the same microbiome data. 6/7

Finally, DEBIAS-M is designed for machine learning pipelines, allowing to not just hold-out labels for a test set, but actually has an online learning mode that can handle completely new data on the fly (to our knowledge - the only method that allows that for microbiome data). 5/7

An analysis of 17 studies of the gut microbiome in the context of HIV. On the top is an Adonis analysis, showing that 43% of the variance in inferred experimental biases is explained by DNA extraction kit, 27% by the 16S gene region, and 14% by the type of swab used for sample collection. On the bottom is a PCA of inferred biases, where every dot is a study. There is apparent clustering by extraction kit type and 16S gene region.

Next, the changes DEBIAS-M makes to the data are interpretable and explained by differences in experimental protocols. Analyzing the biases inferred for these 17 gut microbiome studies in HIV, we found that 84% of the variance can be explained by just three experimental factors. 4/7

Boxplots with auROC on the y-axis and different methods on the x-axis/ Title reads :"DEBIAS-M improves cross-study HIV prediction". Raw data has a median auROC of ~0.5; ComBat, ConQuR, percnorm, Voom-SNM, MMMUPHin and PLSDA-batch have median auROCs between ~0.5 and ~0.6. The median auROC of DEBIAS-M is close to 0.7 and is significantly higher than all the rest.

This results in several benefits. First, in diverse benchmarks - using metagenomics and 16S sequencing, vaginal and gut microbiomes, and phenotypic and metabolite predictions - DEBIAS-M outperforms alternative methods. Here is an example for a gut 16S-based HIV classification across 17 studies. 3/7

DEBIAS-M is based on the multiplicative bias model of McLaren et al. (elifesciences.org/articles/46923). Under this model, every experimental protocol has different biases for each microbe. We infer the biases that maximize cross-batch association with phenotypes and minimize batch effects. 2/7

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models - Nature Microbiology DEBIAS-M corrects technical variability in microbiome data in a manner both interpretable and suitable for machine learning. In extensive benchmarks, DEBIAS-M facilitates robust analyses that generali...

Happy to share DEBIAS-M, our new method for domain adaptation and bias correction in #microbiome data.🧬🖥️

Microbiome data is very variable, with substantial study- and batch-effects. DEBIAS-M corrects these, enabling robust and generalizable analyses.
A quick thread:
www.nature.com/articles/s41...

Just let me try and cure cancer for fucks sake.

Congratulations!

Whomst among ass profs is not tired of Unprecedented Times? I want a precedent of faculty position straight from PhD and uninterrupted federal science funding for a 50 year career pls.

My next study section was canceled well beyond Feb 2. (20-21)

A foundation model of transcription across human cell types - Nature A foundation model learns transcriptional regulatory syntax from chromatin accessibility and sequence data across a range of cell types to predict gene expression and transcription factor interactions...

GET is finally published!
- Paper: t.ly/iQct_ (new validations, dry and wet)
- Model: t.ly/4jnUI (new tutorial on PBMC 10x Multiome data, and yes you can even fine-tune it on a Macbook)
- Analysis package: t.ly/OqLAL
- Demo: t.ly/rbFQB
- Docker: t.ly/86n_i

It's really well done

A thread by Megan explaining the work

bsky.app/profile/mega...