A tiny fraction of all species forms most of nature: Rarity as a sticky state

https://www.pnas.org/doi/10.1073/pnas.2221791120

#microbiome #ecology

Identifying keystone species in microbial communities using deep learning

"Study proposes new framework to identify keystone microbial species"

Lay summary
https://phys.org/news/2023-11-framework-keystone-microbial-species.html

Source
https://www.nature.com/articles/s41559-023-02250-2

#KeystoneSpecies #DeepLearningModel #ecology #microbiome

Study proposes new framework to identify keystone microbial species

Microbial communities are thought to contain keystone species, which can disproportionately affect the stability of the communities, even if only present in low abundances.

^{Science X (Phys.org)}

Very interesting preprint by Ran Blekhman's lab (@blekhman): https://www.biorxiv.org/content/10.1101/2023.10.11.560955v1 on https://microbiomap.org/ , a resource integrating 168k human gut 16S samples for a global overview. Seems similar to https://microbeatlas.org/ , which combines >2 million samples (both 16S and WGS across all habitats)

#microbiome #microbiology

Integration of 168,000 samples reveals global patterns of the human gut microbiome

Understanding the factors that shape variation in the human microbiome is a major goal of research in biology.

^bioRxiv

Preprint from Salzberg team questioning a 2020 Nature paper from Rob Knight 😮

"the raw read counts were vastly over-estimated for nearly every bacterial species, often by a factor of 1000 or more."

"Our conclusion after re-analysis is that the near-perfect association between microbes and cancer types reported in the study is, simply put, a fiction."

Major data analysis errors invalidate cancer microbiome findings

https://www.biorxiv.org/content/10.1101/2023.07.28.550993v1

#microbiome #genomics #research #science

Major data analysis errors invalidate cancer microbiome findings

We re-analyzed the data from a recent large-scale study that reported strong correlations between microbial organisms and 33 different cancer types, and that created machine learning predictors with near-perfect accuracy at distinguishing among cance…

^bioRxiv

Our new Perspective is out in Nature Microbiology today. It covers some common/emerging myths and misconceptions in the human #microbiome field, and explains why they are likely incorrect: https://www.nature.com/articles/s41564-023-01426-7

A hopefully helpful primer against uncritical repetition.

Thanks to @fragilismatters for providing me and Alan with the original inspiration for this piece.

Human microbiome myths and misconceptions - Nature Microbiology

Walker and Hoyles highlight selected myths and misconceptions in the human microbiota literature.

^Nature

Interesting: *Vaginal Microbiota Transplantation (VMT)*

We're probably going to start seeing a lot more of this as we appreciate how microbiomes of different organs impact health and disease.

Personally the key is to have clinical measures of success, as here, not just measuring % of different species changing.
#IDMastodon #MedMastodon #AMR #antibiotics #microbiology #microbiome #transplant

https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(23)00247-X/fulltext?dgcid=raven_jbs_aip_email

Accounting for 16S rRNA copy number prediction uncertainty and its implications in bacterial diversity analyses

https://www.nature.com/articles/s43705-023-00266-0

#microbiology #bioinformatics #microbiome

Accounting for 16S rRNA copy number prediction uncertainty and its implications in bacterial diversity analyses - ISME Communications

ISME Communications - Accounting for 16S rRNA copy number prediction uncertainty and its implications in bacterial diversity analyses

^Nature