Fantastic talk by @vikramshivakumar.bsky.social Mumemto—Scalable multi-MUM finding for pangenomes
Papers biorxiv.org/content/10.1101/2025.05.20.654611 & doi.org/10.1186/s13059-025-03644-0
Code: github.com/vikshiv/mume...
Very efficient pangenome visualization tool, revealing synteny and variations!

I just cannot see academic institutions, medical facilities and so many other employers stumping up $100K.

It’s disheartening, both to see this happen and to see how few people who rely on and employ individuals on H-1B visas are willing to speak up and address this. Your silence is deafening.

Make no mistake, this remains catastrophic.

It makes it near impossible for people to stay and work in the US, no matter how qualified they are, unless they work for a handful of extremely wealthy companies.

For so many of us, it’s time to make other plans.

The concessions made by Brown endanger so many members of our community on campus, limit the access to higher education for individuals from underrepresented backgrounds, and undermine the serious conversations about major issues on campus.

Just so disappointing to see them go down without a fight.

Brown have agreed to do all this without any sort of legal challenge, and have agreed to these terms without consulting their staff, students or alumni.

Here's a link to the article that should bypass the paywall, if you wanted to read it for yourself. www.nytimes.com/2025/07/30/u...

In short, all funding is restored and active cases are dismissed in exchange for a $50M commitment to state work force development, new compliance with the administration's discriminatory policies on transgender individuals, a slew of "anti-DEI" admissions policies. No admission of any wrongdoing.

Thank you! It was awesome to talk to you too, and to learn about all the cool data and insights from your project!

The homepage for this work is here: github.com/arun96/South...

This analysis can be replicated on any population of your choosing, and the WDL scripts used to run the various stages (as well as any other analysis details) can all be found on that page and in our pre-print.

Finally, we compare our placed contigs to loci associated with biomarker traits in the UK Biobank and East London Genes & Health Dataset, and find a number of positions where a placed contig is close to a significant locus.

We are also able to align existing RNA-Seq data from 140 SAS individuals from MAGE directly to these contigs, allowing us to identify 200 contigs with a high density of RNA-Seq alignments.

BLAST shows that these contigs are highly similar to non-reference human and primate sequences.

We show that the majority of the placements we make are missed by traditional insertion calling tools, but in line with specific large non-reference sequence detection ones.

For the unplaced contigs, BLAST shows that the majority have high similarity to non-reference human and primate sequences.

We are able to place ~20K contigs against CHM13 through a combination of alignment, mate pair read information and LD.

We find >8,000 instances of a placed contig intersecting one of 106 protein coding genes, and >6,000 placements within 1 Kb of a known GWAS site.