Bakta generally, but not tried with funkier genomes

Thanks for the mention 😁 The paper is out now in ISME Journal: academic.oup.com/ismej/advanc...

MiMiC2 is available on GitHub and there are premade functional profiles for multiple collections of publicly accessible isolates: github.com/thh32/MiMiC2 7/7

Next we wanted to create SynComs that allow mechanistic studies of a disease, so we chose IBD. By comparing healthy and IBD metaGs we selected a SynCom for each. In germ-free mice we showed the IBD SynCom caused increased inflammation. 6/7

Using MiMiC2 we were able to create SynComs for a range of ecosystems, including the rumen of cows! We wondered our function-based approach would select a methanogen, since they are sub-dominant, but critical members. It did, along with diverse other members! 5/7

This comparison is done against ALL input metaGs, not a single metaG, allowing the SynComs to capture the variability within an ecosystem. Strains are also selected together, reducing functional redundancy between each other. 4/7

MiMiC2 addresses this by reducing both metaGs and isolate genomes down to the protein families they encode. Next, we can select strains that functional match our metaGs of interest. 3/7

SynComs are usually put together based on expert curation, but microbiota are functionally complex, and capturing that complexity in only a few strains is hard. Hence the idea "what if we used metagenomes (metaGs) to select the correct strains?" 2/7

It’s also timely to present about SynCom design considering the recent work from @tcahitch.bsky.social @tclavel.bsky.social. I highly recommend reading!

academic.oup.com/ismej/advanc...

Thanks for the shout-out 😁

Really cool dataset!

Congratulations on getting this out, can't wait to read the updated work in full!