Betty Liu (@bettieliu) profile picture

Betty Liu

@bettieliu

BioE PhD candidate @Stanford | SGF Fellow | Flagship Pioneering Fellow | @UofT EngSci alum

Joined September 2014
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Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)Photo by Betty Liu (@bettieliu)
Delighted to share our latest work deciphering the landscape of chromatin accessibility and modeling the DNA sequence syntax rules underlying gene regulation during human development! biorxiv.org/content/10.1101/…. Read on for more 🧵 [1/16]
Dissecting regulatory syntax in human development with scalable multiomics and deep learning

Dissecting regulatory syntax in human development with scalable multiomics and deep learning

Transcription factors (TFs) establish cell identity during development by binding regulatory DNA in a sequence-specific manner, often promoting local chromatin accessibility, and regulating gene...

biorxiv.org
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We're selecting 10 early-stage techbio companies from China and the West to pitch at The Lantern Summit. Meet investors and operators from Greater China, Europe and the US. Shanghai · October 27. Apply by October 15th: thelanternsummit.com/pitch-t…
The Lantern Summit: Accelerating Life Sciences Beyond Borders

The Lantern Summit: Accelerating Life Sciences Beyond Borders

An invitation-only gathering connecting tech bio leaders across China, Europe, and the United States. Shanghai · 26-27 October 2026.

thelanternsummit.com
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Betty Liu retweeted
Jason Tan (@yxjtan) profile picture
Jason Tan@yxjtanJun 30
Does every enhancer work with every promoter? With @jengreitz and @WJGreenleaf, we revisit this long-debated question and resolve an outstanding contradiction in the field. A tour 🧵👇 biorxiv.org/content/10.64898…
Intrinsic promoter responsiveness dictates sensitivity to transcriptional activation by enhancers

Intrinsic promoter responsiveness dictates sensitivity to transcriptional activation by enhancers

Enhancers activate specific target promoters, but whether intrinsic enhancer-promoter compatibility contributes to this specificity is debated. Recent studies using different reporter assays have...

biorxiv.org
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Special shoutout to my advisor @WJGreenleaf — this is my first PhD manuscript and I’m incredibly grateful for his guidance and support throughout the journey. It’s been an amazing experience learning from him while working on this first big milestone! 🧬📄
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Our single-cell atlas, trained models, accessibility tracks, per-basepair contrib scores, and annotated motif instances (and more) are available here: zenodo.org/communities/hdma/. Analysis code & tutorials are available here: greenleaflab.github.io/HDMA/ [16/16]
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Big shoutout to pkgs that were essential: ChromBPNet, the seq-to-accessibility model (@panushri25); Fi-NeMo, to identify predictive motif instances (@austintwang); tangermeme, for in silico experiments (@jmschreiber91); & BPCells, for efficient genomic plots (@bn_parks) [15/16]
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Last, we found that putative causal noncoding variants for various traits were enriched in CREs in a cell type specific manner, and we used our models to predict how SNVs alter motifs and thus alter accessibility, providing mechanistic interpretation of variant effect: [14/16]
Image from Betty Liu (@bettieliu)
Image from Betty Liu (@bettieliu)
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Deep learning models can identify sequences predictive of accessibility, but also which *negatively* impact accessibility. We found a small set of negative motifs (in peaks!) that were extremely abundant in every cell type, enriched near nucleosome dyads and peak flanks [13/16]
Image from Betty Liu (@bettieliu)
Image from Betty Liu (@bettieliu)
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We also found examples of "soft" syntax, where motifs synergize across longer distances (<150 bp), potentially reflecting active or passive competiton of TFs with nucleosomes mediating cooperativity: [12/16]
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Our models were able to predict synergistic effects at exactly the motif syntax described for the Coordinator motif by @seungsookim10&Wysocka lab, where X-ray crystallography showed DNA stabilizes weak contacts between TWIST1&ALX4, which directs mesenchymal gene programs: [11/16]
Image from Betty Liu (@bettieliu)
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We found 100s of composite motifs, so we used our models to systematically define effects of motif syntax (spacing/orientation) on synergy in silico. We found dozens of "hard" syntax cases, where synergy relies on strict motif position, likely due to direct interactions: [10/16]
Image from Betty Liu (@bettieliu)
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We used this map of motif instances in every cell type to identify ubiquitous and cell type specific motifs, and found that a small set of ubituitous, CG-rich motifs tended to occur in promoters, while cell type specific motifs were predominant at distal&intronic regions: [9/16]
Image from Betty Liu (@bettieliu)
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Clustering these motifs, we assembled a lexicon of 508 unique motifs which influence accessibility during development, and mapped back to peaks to automatically annotate predictive motif instances in open chromatin in every cell type, representing putative TF binding sites [8/16]
Image from Betty Liu (@bettieliu)
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We used model interpretation techniques (DeepLIFT & TF-MoDISco) to score the contribution of every nucloetide to accessibility, and find recurrent patterns of sequences predictive of local chromatin accessibility - and these patterns turn out to resemble TF binding motifs! [7/16]
Image from Betty Liu (@bettieliu)
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What are the DNA sequences that drive accessibility in each cell type? We trained ChromBPNet models - deep learning models tasked with predicting chromatin accessibility at basepair resolution from sequence alone, accounting for Tn5 bias. These models work remarkably well: [6/16]
Image from Betty Liu (@bettieliu)
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Do these CREs drive activity in vivo? We inspected the VISTA enhancers (validated in reporter mice), and our data suggested previously unappreciated activity of some enhancers in the liver! Our data resolved one enhancer to be erythroblast-specific, confirmed w/ histology: [5/16]
Image from Betty Liu (@bettieliu)
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We generated a single-cell multiome, multi-organ atlas using SHARE-seq, profiling gene expr & accessibility in 818k cells from 12 fetal organs: the Human Development Multiomic Atlas (HDMA). We labelled 203 cell types & defined >1M candidate cis-regulatory elements: [4/16]
Image from Betty Liu (@bettieliu)
Image from Betty Liu (@bettieliu)
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Transcription factors (TFs) are causal regulators of gene expression in cell specification, yet we lack a comprehensive map of their activities across cell types. How do TFs shape accessibility during human development, and how does motif syntax mediate TF synergy? [3/16]
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This is a huge joint effort with Selin Jessa, Sam Kim, and Yan Ting Ng; led by Will Greenleaf and @anshulkundaje labs at Stanford and Kyle Farh’s team at @illumina’s AI lab. A wonderful collab w/ Anca Pasca (@BrainTherapies), Serena Tan, and @LenPennacchio [2/16]
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Super excited to work with the Roth Lab this summer! Check out their latest publication on the human protein interactome project biorxiv.org/content/10.1101/…
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