Jim Shaw @jim_elevator
Postdoc with Heng Li at Dana-Farber/Harvard Med School. Math PhD from @UofT with @YunWilliamYu. Working on methods for analyzing (metagenomic) sequencing data. jim-shaw-bluenote.github.io Joined April 2018-
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Stunning feat of applied sequencing bioinformatics. Congrats to the authors!!
🌎👩🔬 For 15+ years biology has accumulated petabytes (million gigabytes) of🧬DNA sequencing data🧬 from the far reaches of our planet.🦠🍄🌵 Logan now democratizes efficient access to the world’s most comprehensive genetics dataset. Free and open. doi.org/10.1101/2024.0…
skani v0.3.0 is released. github.com/bluenote-1577/… * 30-40% potential reduction in memory * Breaking changes to indexing and searching databases Calculate ANI for contigs, genomes. Search vs > 140k genomes: pre-indexed GTDB-R226 available for download.
I'm excited to share that I will soon join UCLA's Bioengineering department as an Assistant Professor. I am incredibly fortunate to have landed my dream job in this current atmosphere, and I would like to thank my mentors, colleagues, and friends for their support.
Preprint on "Improving spliced alignment by modeling splice sites with deep learning". It describes minisplice for modeling splice signals. Minimap2 and miniprot now optionally use the predicted scores to improve spliced alignment. arxiv.org/abs/2506.12986
New life update! 🎆 🎓 This Fall, I will be joining the Department of Computer Science at Johns Hopkins University (@JHUCompSci) as an Assistant Professor, with an affiliation at the new Data Science and AI Institute (@HopkinsDSAI).
@csuhuangneng developed longcallR for joint SNP calling and phasing from long RNA-seq reads, AND for identifying allele-specific splicing/junctions (ASJ). Although ASJs of statistical significance are rare, a large fraction involve unannotated junctions. In Rust!
@csuhuangneng developed longcallR for joint SNP calling and phasing from long RNA-seq reads, AND for identifying allele-specific splicing/junctions (ASJ). Although ASJs of statistical significance are rare, a large fraction involve unannotated junctions. In Rust!
GASTON, our method to learn “topographic maps” of gene expression, is out now @naturemethods! IMO the coolest part is a new model of *spatial gradients in sparse data*. As is typical for bio papers, it’s buried in Methods, but see below for a quick outline on the math 👇
GASTON, our method to learn “topographic maps” of gene expression, is out now @naturemethods! IMO the coolest part is a new model of *spatial gradients in sparse data*. As is typical for bio papers, it’s buried in Methods, but see below for a quick outline on the math 👇
Tech Alert! 🚀🧬 We can now determine the sequence of DNA with non-canonical bases in a direct and high-throughput manner with Nanopore sequencing. Check out our preprint for details: biorxiv.org/content/10.110…
Hifiasm 0.21.0 has been released. It now has a beta module for direct assembly of ONT R10 simplex reads. Initial tests with regular simplex reads show very promising results! github.com/chhylp123/hifi…
Happy to see our method for T2T genome assembly published! It addresses an important limitation of string graph, that is, the contained reads. Led by @skamath5e "Telomere-to-telomere assembly by preserving contained reads" doi.org/10.1101/gr.279…
Happy to see our method for T2T genome assembly published! It addresses an important limitation of string graph, that is, the contained reads. Led by @skamath5e "Telomere-to-telomere assembly by preserving contained reads" doi.org/10.1101/gr.279…
Just tried Sylph github.com/bluenote-1577/… by @jim_elevator. Processed >2000 samples in a few hours on google cloud powered by @TerraBioApp. Roughly 15min per sample. Briefly checked the abundances and they match pretty well with what we got using an independent workflow.
I gave a talk recently at Ben Langmead's group on my post on fast computation of random minimizers. Was super fun! Blogpost: curiouscoding.nl/posts/fast-min… Recording: curiouscoding.nl/talks/minimize…
A new paper from our amazing @sladky_on (jointly supervised with @VeselyPavel_mff) on super space-efficient indexing of arbitrary k-mer sets, introducing the Masked Burrows-Wheeler Transform (MBWT).
A new paper from our amazing @sladky_on (jointly supervised with @VeselyPavel_mff) on super space-efficient indexing of arbitrary k-mer sets, introducing the Masked Burrows-Wheeler Transform (MBWT).

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