Peter Koo
@pkoo562.bsky.social
AI4Science researcher. Associate Professor @CSHL. My lab advances AI for genomics and healthcare!
http://koo-lab.github.io
http://koo-lab.github.io
Reposted by Peter Koo
Yijie Kang (CSHL, Stony Brook) from @pkoo562.bsky.social Lab presented "Decoding the sequence basis of Pol II elongation with deep learning"
November 7, 2025 at 3:05 PM
Yijie Kang (CSHL, Stony Brook) from @pkoo562.bsky.social Lab presented "Decoding the sequence basis of Pol II elongation with deep learning"
This was led by @EESeitz
, a former postdoc who was jointly advised by me and Justin Kinney at CSHL, and also in collaboration with David McCandlish @TheDMMcC
It's a beautiful followup to SQUID, our surrogate modeling approach to interpret genomic DNNs!
www.nature.com/articles/s42...
, a former postdoc who was jointly advised by me and Justin Kinney at CSHL, and also in collaboration with David McCandlish @TheDMMcC
It's a beautiful followup to SQUID, our surrogate modeling approach to interpret genomic DNNs!
www.nature.com/articles/s42...
Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models - Nature Machine Intelligence
The intersection of genomics and deep learning shows promise for real impact on healthcare and biological research, but the lack of interpretability in terms of biological mechanisms is limiting utility and further development. As a potential solution, Koo et al. present SQUID, an interpretability framework built using domain-specific genomic surrogate models.
www.nature.com
October 9, 2025 at 12:08 PM
This was led by @EESeitz
, a former postdoc who was jointly advised by me and Justin Kinney at CSHL, and also in collaboration with David McCandlish @TheDMMcC
It's a beautiful followup to SQUID, our surrogate modeling approach to interpret genomic DNNs!
www.nature.com/articles/s42...
, a former postdoc who was jointly advised by me and Justin Kinney at CSHL, and also in collaboration with David McCandlish @TheDMMcC
It's a beautiful followup to SQUID, our surrogate modeling approach to interpret genomic DNNs!
www.nature.com/articles/s42...
Check out the paper to find out more!
Code: github.com/evanseitz/se...
Paper: www.biorxiv.org/content/10.1...
Code: github.com/evanseitz/se...
Paper: www.biorxiv.org/content/10.1...
Uncovering the Mechanistic Landscape of Regulatory DNA with Deep Learning
The regulatory genome encodes the logic that governs gene expression, enabling cells to respond to developmental, environmental, and evolutionary cues. This logic arises from complex cis-regulatory me...
www.biorxiv.org
October 9, 2025 at 12:08 PM
Check out the paper to find out more!
Code: github.com/evanseitz/se...
Paper: www.biorxiv.org/content/10.1...
Code: github.com/evanseitz/se...
Paper: www.biorxiv.org/content/10.1...
We find regulatory DNA is readily reprogrammable with a few key mutations! We observed similar phenomenon across all genomic DNNs we tested! 12/N
October 9, 2025 at 12:08 PM
We find regulatory DNA is readily reprogrammable with a few key mutations! We observed similar phenomenon across all genomic DNNs we tested! 12/N
SEAM is a powerful tool that helps to: 1) explore the high evolvability landscape of regulatory sequences; 2) identifies mutations that drive mechanistic changes; and 3) dissect motif syntax and context dependencies. 11/N
October 9, 2025 at 12:08 PM
SEAM is a powerful tool that helps to: 1) explore the high evolvability landscape of regulatory sequences; 2) identifies mutations that drive mechanistic changes; and 3) dissect motif syntax and context dependencies. 11/N
And we tested the backgrounds across different ChromBPNet models independently trained on DNase-seq and ATAC-seq and we observe similar backgrounds! This suggests these mutagenesis-robust patterns are important context that reflects properties of the local sequence space. 10/N
October 9, 2025 at 12:08 PM
And we tested the backgrounds across different ChromBPNet models independently trained on DNase-seq and ATAC-seq and we observe similar backgrounds! This suggests these mutagenesis-robust patterns are important context that reflects properties of the local sequence space. 10/N
While previous analyses focused on differences in attr maps across clusters, a surprising observation was that there were also shared patterns. We disentangled the attribution signals that are sensitive versus robust to mutagenesis – we call them foreground and background. 9/N
October 9, 2025 at 12:08 PM
While previous analyses focused on differences in attr maps across clusters, a surprising observation was that there were also shared patterns. We disentangled the attribution signals that are sensitive versus robust to mutagenesis – we call them foreground and background. 9/N
This analysis flagged 2 key mutations at positions 170 & 174 that created a new CAAT box. To test necessity & sufficiency, we mutated each individually and together, then examined attr maps+ predictions
- single mutations -> no change
- double mutation -> CAAT box + new Inr
8/N
- single mutations -> no change
- double mutation -> CAAT box + new Inr
8/N
October 9, 2025 at 12:08 PM
This analysis flagged 2 key mutations at positions 170 & 174 that created a new CAAT box. To test necessity & sufficiency, we mutated each individually and together, then examined attr maps+ predictions
- single mutations -> no change
- double mutation -> CAAT box + new Inr
8/N
- single mutations -> no change
- double mutation -> CAAT box + new Inr
8/N
Applying SEAM to CLIPNET, which predicts transcriptional activity measured via PRO-cap, we find that many SNVs lead to new clusters in the PIK3R3 promoter. A few specific mutations can quantitatively tune gene expression and SEAM can find them! 7/N
October 9, 2025 at 12:08 PM
Applying SEAM to CLIPNET, which predicts transcriptional activity measured via PRO-cap, we find that many SNVs lead to new clusters in the PIK3R3 promoter. A few specific mutations can quantitatively tune gene expression and SEAM can find them! 7/N
Now, if we plot the percent mismatch of the nucleotides with respect to WT for each cluster, you can see yellow bars that reflect all sequences in the cluster share the same single nucleotide mutation. This analysis pinpoints the exact mutation that led to the new mechanism! 6/N
October 9, 2025 at 12:08 PM
Now, if we plot the percent mismatch of the nucleotides with respect to WT for each cluster, you can see yellow bars that reflect all sequences in the cluster share the same single nucleotide mutation. This analysis pinpoints the exact mutation that led to the new mechanism! 6/N
Low entropy reflects all the sequences share the same nucleotides, while high entropy reflects different mutations destroyed the motif. Sometimes, we see motif preserving signature outside the vertical bands. This represents a de novo motif that appeared within that cluster. 5/N
October 9, 2025 at 12:08 PM
Low entropy reflects all the sequences share the same nucleotides, while high entropy reflects different mutations destroyed the motif. Sometimes, we see motif preserving signature outside the vertical bands. This represents a de novo motif that appeared within that cluster. 5/N
If we calculate the positional entropy of the sequences within each cluster, we get a cluster summary matrix. The vertical bands highlight the locations of the motifs in WT seq and entropy levels indicate whether the motif is present or not in the attr maps in each cluster. 4/N
October 9, 2025 at 12:08 PM
If we calculate the positional entropy of the sequences within each cluster, we get a cluster summary matrix. The vertical bands highlight the locations of the motifs in WT seq and entropy levels indicate whether the motif is present or not in the attr maps in each cluster. 4/N
Attr maps can sometimes be easy to interpret, and sometimes they're complex. SEAM's clustered attr maps are cleaner (think SmoothGrad) and they decompose complex mechanisms via partial random mutagenesis, which occasionally disrupts key binding sites. 3/N
October 9, 2025 at 12:08 PM
Attr maps can sometimes be easy to interpret, and sometimes they're complex. SEAM's clustered attr maps are cleaner (think SmoothGrad) and they decompose complex mechanisms via partial random mutagenesis, which occasionally disrupts key binding sites. 3/N
SEAM is conceptually simple. Starting from a reference sequence:
1) sample in a local region of sequence space via partial random mutagenesis
2) calculate attr maps to unveil the mechanisms
3) cluster attr maps based on shared mechanisms
4) cluster-based sequence analysis
2/N
1) sample in a local region of sequence space via partial random mutagenesis
2) calculate attr maps to unveil the mechanisms
3) cluster attr maps based on shared mechanisms
4) cluster-based sequence analysis
2/N
October 9, 2025 at 12:08 PM
SEAM is conceptually simple. Starting from a reference sequence:
1) sample in a local region of sequence space via partial random mutagenesis
2) calculate attr maps to unveil the mechanisms
3) cluster attr maps based on shared mechanisms
4) cluster-based sequence analysis
2/N
1) sample in a local region of sequence space via partial random mutagenesis
2) calculate attr maps to unveil the mechanisms
3) cluster attr maps based on shared mechanisms
4) cluster-based sequence analysis
2/N
First talk a (surprise) keynote by Jacob Schreiber from UMass Medical talking about fruit-themed AI tools for understanding and designing regulatory DNA
September 11, 2025 at 1:44 PM
First talk a (surprise) keynote by Jacob Schreiber from UMass Medical talking about fruit-themed AI tools for understanding and designing regulatory DNA
Now Barbara Engelhardt giving a keynote on characterizing behaviors of modified T cells in live cell imaging data using machine learning!
September 10, 2025 at 5:58 PM
Now Barbara Engelhardt giving a keynote on characterizing behaviors of modified T cells in live cell imaging data using machine learning!
Next talk by Courtney Shearer who is talking about genomic language models for zero shot promoter indel effects!
September 10, 2025 at 3:16 PM
Next talk by Courtney Shearer who is talking about genomic language models for zero shot promoter indel effects!