Salzman Lab

Official Twitter of the Salzman research group at Stanford Biomedical Data Science

[Salzman Lab @SalzmanLab]

@lpachter We believe UMAPS are taken from aviaries.

[Salzman Lab @SalzmanLab]

presentation by @TBaharav today at @RECOMBconf on the statistics behind SPLASH, a method that can be used as a robust alternative to one of the most common statistical tests

[Salzman Lab @SalzmanLab]

backlogged on posting our new work in genomics! using RNA splicing as a special case of expansive, ultra-efficient and fully statistical analysis of RNA or DNA sequencing data. https://t.co/9Dwco1lmhW

[Tavor Baharav @TBaharav]

Thrilled to share our new publication in PNAS on OASIS, an alternative to Pearson’s X² for analyzing contingency tables.

Made it to the front page! 1/ 7

[Salzman Lab @SalzmanLab]

3. Development of NOMAD2, which enables reference-free biological discovery at unmatched scale and speed. In collaboration with @sdeorowicz and @marekkoki, led by @marekkoki and @roozbehdn, this package is publicly available on github: https://t.co/FFsvwH8siU

[Salzman Lab @SalzmanLab]

Thrilled to announce 3 new papers from our lab, up on bioRxiv this past week.
https://t.co/soTnsDxvUR
https://t.co/GccCGdQQhq
https://t.co/Q0jMrZsHgq

[Salzman Lab @SalzmanLab]

2. Design of a novel statistical test on contingency tables, OASIS, which provides interpretable and finite-sample valid rejection of the null, enabling reference-genome and metadata free strain detection in biological applications. Led by @TBaharav: https://t.co/GccCGdQirS

[Matt Durrant @mgdurrant]

I can confirm that the NOMAD analysis method and the accompanying software is a game-changer for sequence analysis and discovery. Check this paper out! https://t.co/TewYcDIaHM

[Sebastian Deorowicz @sdeorowicz]

NOMAD2 is an unsupervised, reference-free, ultra-fast next generation of NOMAD, revealing new insights into cancer transcriptomes. Joint work with @salzmanlab. Many thanks to all for great collab!
https://t.co/x25YvXXhdh

[Salzman Lab @SalzmanLab]

NOMAD+ is a unified, highly efficient algorithm enabling unbiased discovery of an unprecedented breadth of RNA regulation and diversification in single cells through a new paradigm to analyze the transcriptome.

[Salzman Lab @SalzmanLab]

Excited to share that our paper on reference-free transcriptomic analysis is live. https://t.co/5lA7dd72d8

[Salzman Lab @SalzmanLab]

NOMAD+ discovers broad and new examples of transcript diversification in single cells, bypassing genome alignment and without requiring cell type metadata.

[Tavor Baharav @TBaharav]

Had a blast at #biodata22! Was great to present and get feedback on NOMAD, our recent work for reference-free inference https://t.co/qutAFVlZwB.

[LCG UNAM @lcgunam]

"NOMAD is a single reference-free ‘omic algorithm for biological discovery across the tree of life"
Julia Salzman @SalzmanLab en #FrontiersInGenomics HOY martes 15 nov 5pm #EnVivo #EnLínea https://t.co/xo1aQxP0mr @ccg_unam @ibt_unam @unammorelos

[Salzman Lab @SalzmanLab]

Very excited that ReadZS, a scRNA-seq pipeline for detection of RNA processing, is out in @GenomeBiology! This work was led by @ea_meyer, K Chaung, and @roozbehdn.

[Genome Biology @GenomeBiology]

ReadZS, from @ea_meyer, Chaung, @roozbehdn & Salzman, for identifying RNA processing (splicing, alternative polyadenylation etc) in RNA-seq data, without the need for pre-existing annotations. It works on both 3'-enriched and non-enriched data https://t.co/HIHi2baC6F

[Salzman Lab @SalzmanLab]

Excited to share our new preprint NOMAD! We present a unifying statistical formulation for many fundamental problems in genome science and develop an efficient reference-free algorithm (NOMAD) to solve it. Work by Kaitlin Chaung, @TBaharav, and @RZheludev. https://t.co/2SFbqQ43Ia

[Salzman Lab @SalzmanLab]

Excited to announce that a project spearheaded between our lab and David Tse's has been selected for a @czbiohub Data Insights Award! Follow the link for more information.
https://t.co/BtlIKu0rRl

[Salzman Lab @SalzmanLab]

Excited to share DIVE is live on bioarxiv! DIVE is a novel reference-free approach for de novo discovery of diversity-generating and mobile genetic elements implemented as a publicly available python package (work led by @jordiatjhu).

[bioRxiv @biorxivpreprint]

DIVE: a reference-free statistical approach to diversity-generating and mobile genetic element discovery https://t.co/s7JCodGFJz #bioRxiv