Levi Waldron

Human mitochondrial DNA in public metagenomes: Opportunity or privacy threat? Human DNA is unavoidably present in metagenomic analyses of human microbiomes. While current protocols remove human DNA before submission to public repositories, mitochondrial DNA (mtDNA) has been ove...

🥁 NEW:

Commentary in @cellcellpress.bsky.social!

by @msabrysarhan.bsky.social Giacomo Antonello @whansi.bsky.social @cengoni.bsky.social @dmascalzoni.bsky.social @leviwaldron1.bsky.social Nicola Segata, Christian Fuchsberger

www.cell.com/cell/fulltex...

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benchdamic: benchmarking of differential abundance methods for microbiome data AbstractSummary. Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome dat

also has an associated Bioconductor package `benchdamic` to facilitate benchmarking against commonly used methods (Calgaro et al, Bioinformatics 2023), which we profited from in this analysis academic.oup.com/bioinformati...

Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data - Genome Biology Background The correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to dea...

A previous broad benchmarking of microbiome methods that I was part of (Calgaro et al, Genome Biology 2020) genomebiology.biomedcentral.com/articles/10....

GitHub - waldronlab/MicrobiomeBenchmarkDataAnalyses: Analyses using the datasets provided by the MicrobiomeBenchmarkData package. Analyses using the datasets provided by the MicrobiomeBenchmarkData package. - waldronlab/MicrobiomeBenchmarkDataAnalyses

I like your idea, and it seems promising! No snub intended, just not yet in our scope of 'commonly used' or of reviewing broad benchmarking-focused studies. FYI our benchmarking is reproducible and hopefully easily extensible w/ code from github.com/waldronlab/M...

I guess I can thank you for breaking my long sojourn from social media :D I made a thread about our preprint and referenced your post - we agree on compositional normalization, but "normalization based methods don't work well" (including for RNAseq) is a big claim! bsky.app/profile/levi...

So we agree that compositional normalization is problematic, but disagree about the simple, widely-used methods. Prove your method outperforms 17 methods in the 3 datasets we've provided, and I'll eat humble pie 🙂

Discussion! @inschool4life.bsky.social says all these methods are bad and that his new alternative to normalization improves rigor in microbiome *and* RNAseq analysis, as demonstrated by a simulation and a real-data study of each. I hope you're right! bsky.app/profile/insc...

Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis Abstract. Motivation : The successful translation of genomic signatures into clinical settings relies on good discrimination between patient subgroups. Man

My history on simple methods: In 2014, I benchmarked a simple prediction method (can be trained in a spreadsheet!) against penalized regression. In 27 independent microarray studies, it performed comparably or better than theoretically superior methods (incl. Lasso) academic.oup.com/bioinformati...

Disclaimer: I favor simple methods for high-dimensional data analysis. They perform well in diverse settings. I'm skeptical of hand-selected benchmarks by researchers with a "horse in the race." It's a good start but far short of showing broad utility.

Implications: Our findings suggest researchers should use widely adopted non-parametric or RNA-seq DA methods. Further development of compositional methods should include benchmarking against datasets with known biological ground truth.

Key findings: we benchmarked 17 DA approaches and found compositional methods often lack sensitivity and show increased variability. Non-parametric and RNA-seq-derived methods performed best, challenging the assumption that compositional methods are superior.

MicrobiomeBenchmarkData includes:
1) Oral microbiomes (supragingival vs. subgingival plaques)
2) Vaginal microbiomes (healthy vs. bacterial vaginosis)
3) Spike-in dataset with known absolute abundances
These datasets cover diverse complexities.

MicrobiomeBenchmarkData The MicrobiomeBenchmarkData package provides functionality to access microbiome datasets suitable for benchmarking. These datasets have some biological truth, which allows to have expected results for...

The Need for Ground Truth Data: DA method benchmarks usually rely on synthetic data, simulations, or expt'l data without a sequencing-independent biological ground truth. Our BioC package fills this gap with 3 experimental datasets with known ground truths www.bioconductor.org/packages/Mic...

Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truth Previous benchmarking of differential abundance (DA) analysis methods in microbiome studies have employed synthetic data, simulations, and "real data" examples, but to the best of our knowledge, none ...

Excited to share our MicrobiomeBenchmarkData preprint, addressing compositional data analysis for microbiome DA. This Bioconductor package features 3 datasets with biological ground truths. Spoiler: simple methods like LEfSe, Wilcoxon, RNA-seq methods performed well www.biorxiv.org/content/10.1...

outreachy

outreachy

outreachy

outreachy

Today we asked our #outreachy applicants:

What's the best/most fun and least fun part while BugSigDB-ing?

Their answers are beautiful!

My Outreachy Internship with Bioconductor An Open Source Journey

Very kind blog post from my Outreachy mentee Peace Sandy about the journey to closing her first issue on an open-source software project! peacesandy.hashnode.dev/my-outreachy...

Reported a duplicated figure to PubPeer - discovered via bugsigdb.org which re-codes published signatures in an analyzable format. Identical signatures are easy to spot as outliers as typical overlap between independently discovered signatures is nowhere near 100% pubpeer.com/publications...

So impressed by outreachy.org 's reach and competitive applicant screening that connected me with a remarkably talented and hard-working intern from Nigeria for a highly productive 3-month paid internship. Recommend to any open-source/open-science projects blog.bioconductor.org/posts/2024-0...

Microbiome Virtual International Forum is a free, monthly 3-hour mini-conference with a keynote talk, short talks, and 1-minute highlights, repeated for accessibility in all time zones. Mar. 20-22 features Curtis Huttenhower; see www.microbiome-vif.org to register and for timing in your time zone

Love this post!

The everyday chronicles

Read a very first blog by Esther Afuape, a #BugSigDB intern:
kunmiwrites.wordpress.com

Much more to come from Esther and her #BugSigDB curations!

#proudmentor
cc @leviwaldron1.bsky.social

My First Piece… Hi… I almost did not believe this was REAL because it looked too good to be true, and as a Nigerian we've a natural instinct to question everything, especially because of the high rate of fraud and cyber ...

Chioma Onyido, an Outreachy intern, wrote about core values, how she applied for internship & why did she choose the Microbiome Study Curation project.

Stay tuned for more Chioma’s stories, tips & tricks on #BugSigDB-ing!

cc @leviwaldron1.bsky.social

chiomaonyido.wordpress.com/2023/12/18/m...

Outreachy Blog : Introducing Myself Kicking Off Outreachy: Week One Highlights, a Journey of Contribution and Growth

Peace Sandy just started her Outreachy journey (together with Chioma Onyido and Afuape Esther) of contribution and growth while curating microbiome studies

Read Peace's point of view on #BugSigDB-ing!

cc @leviwaldron1.bsky.social

peacesandy.hashnode.dev/kicking-off-...

Two positions available at CUNY SPH working with myself & Nicola Segata to understand roles of the microbiome in Parkinson's Disease, in collab with the Michael J Fox Foundation ASAP network. $75-85K/yr, conference travel, 1 mo/yr in the Segata lab in Italy. In-person or remote work possible.

Machine learning for microbiologists - Nature Reviews Microbiology In this Review, Segata, Waldron and colleagues discuss important key concepts of machine learning that are relevant to microbiologists and provide them with a set of tools essential to apply machine l...

In NEW Nature Reviews Microbiology article,

@fasnicar.bsky.social Andrew Maltez Thomas, Andrea Passerini @leviwaldron1.bsky.social and Nicola Segata cover the aspects that they consider most important to enable microbiologists to use machine learning.

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

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Retro Jane Fonda videos are my favorite

Great hook for a course title. And hi on Bluesky!

Welcome, Svetlana!