
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.
ID: 983816813094944770
https://jim-shaw-bluenote.github.io/ 10-04-2018 21:19:41
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A new paper from our amazing Ondřej Sladký (jointly supervised with Pavel Veselý) on super space-efficient indexing of arbitrary k-mer sets, introducing the Masked Burrows-Wheeler Transform (MBWT).



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 Sudhanva Kamath "Telomere-to-telomere assembly by preserving contained reads" doi.org/10.1101/gr.279…



GASTON, our method to learn “topographic maps” of gene expression, is out now Nature Methods! 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 👇

NENG HUANG 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!

New life update! 🎆 🎓 This Fall, I will be joining the Department of Computer Science at Johns Hopkins University (JHU Computer Science) as an Assistant Professor, with an affiliation at the new Data Science and AI Institute (Johns Hopkins Data Science and AI Institute).


