plsRT: Looking for a motivated postdoc! Join us at @BCM_HGSC to explore the mosaic & somatic landscape of the human genome: structural variants, methylation, and all things @SMaHTnetwork If you like long reads, complex variants & methylation come talk to me! #Genomics #postdoc

Trio-barcoded @nanopre Adaptive Sampling (TBAS) to improve #RareDisease diagnostic at less than 1/2 the $$ & high cov: 76% solve rate across 13 trios inc. two corrections from prev. diagnosis. https://t.co/HbjvhTjTPn @fuyvei96 @GREGoR_research @BCM_HGSC #Genetics #bioinformatics

We introduce trio-barcoded @nanopore adaptive sampling (TBAS), which allows targeted sequencing of one #RareDisease trio in one flowcell. We tested it on HG002-4+13 trios from @GREGoR_research. TBAS and pipeline accurately report causative SNV, SV&TRs. https://t.co/p1q4GWEjmt

We’re bringing SMaHT science to #ASHG2025! Check out the list of network presentations below. See you in Boston!

Today is our 7th #bioinformatics Hackathon @BCM_HGSC ! 120+ people from around the world attending!! Super excited about all the projects that are coming! Thanks to @DCGenomics @dnanexus @GREGoR_research @SMaHTnetwork ! Stay tuned !

Also a new topic added: long-read methylation! Some interesting topics (already in my mind) to explore for long-read DNA methylation analysis!

🧬 The SMaHT marker paper is now live in @Nature This landmark study characterizes somatic variation across 19 tissue types from 150 nondiseased donors, laying the groundwork for future discoveries in health, aging, and disease. Read the full paper: https://t.co/uXArDHEtHn

Toward clinical long-read genome sequencing for rare diseases

Fun event!!

Come join us on our SV, graph genome hackathon August 27–29, 2025: https://t.co/QDdqGvHRYY Seats are filling up! It will be great opportunity to work together on some prototypes. We will publish them together in F1000. @RiceUniversity @bcmhouston #bioinformatics #openscience

Always love the name we made up: SMaHT + TR =SMaHTTR

🎉 Congratulations to our Early Career Researcher Pilot and Feasibility Awardees! 👏 Yilei Fu – @sedlazeck 👏 Cheuk-Ting Law – @KathleenHBurns 👏 Yang "Young" Zhang – @Zonglab 👏 Elliot Swanson – @stergachislab 👏 Andy Russell – @insitubiology 👏 Hang Su – @broadinstitute

🚨 Calling all structural variant wranglers! Join us August 27–29 at Baylor College of Medicine with Rice University for the 7th Pan-Structural Variation Hackathon in the Cloud. 🗓️ Save the date: August 27–29 📍 Baylor College of Medicine + Cloud 🔗 https://t.co/M6VkZaWHFW

Slides from my talk (with Kamil Jaron) on an history of k-mers in bioinformatics:

Initial release of Pomfret for methylation assisted phase block joining for more contiguous phased assemblies without any additional library prep from @nanopore data. Such amazing work from @0xfxfxf ! #NanoporeConf https://t.co/7viZhUwt2a

1/ 🧬 A Hitchhiker’s Guide to Long-Read Genomic Analysis is out now @genomeresearch! This mini-review walks through the latest advances in long-read DNA sequencing — from assemblies to variant calling to epigenetics. Link 🔗 https://t.co/tXioydaJBl 🧵👇

Come join us at our 7th #bioinformatics #hackathon @BCM_HGSC @BCMFromtheLabs around Structural Variants, Graph genomes & many related topics: https://t.co/QDdqGvHRYY Groups will work on interesting topics that will be published in F1000. Registration closes 10th of Aug!

How to accurately genotype SV is important & we discovered a huge bias in other methods! Many improvements across population wide SV calling! Out now Kanpig: https://t.co/eots87Czkq @NatureComms Great work from @BCM_HGSC @broadinstitute #Bioinformatics

Long reads @nanopore @PacBio can provide insights into epigenetics. We (@fuyilei96 @timp0) summarize the latest #Bioinformatics approaches to analyze methylation & why this matters! Out now @NatureRevGenet https://t.co/rbjJcDQOrP @BCM_HGSC @bcmgenetics @RiceCompSci @JohnsHopkins

We summarized state-of-the-art computational methods for DNA methylation analysis using long-read sequencing, covering everything from base calling to sample-level, cell-type-level, and even population-scale analysis. Huge Thanks to @sedlazeck and @timp0 for this great work!