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IGVF @ CAGI7!
This year’s meeting features several talks from IGVF members, all aligned with our efforts to link genomic variation to function.
Keynote by Douglas Fowler (UW).
Late registration ends Dec 1.
genomeinterpretation.org/cagi7.html
Stoked to share our latest work entitled: “Large-scale discovery of neural enhancers for cis-regulation therapies”
shorturl.at/H3Qww
This is an enormous team effort that I had the honour of spearheading with Nick Page and Florence Chardon.
Bluetorial below.
Incredible work from the IGVF community!
A major step forward for scalable CRT discovery, and a significant contribution to understanding gene regulation in human neurons.
🔍 scE2G is a computational pipeline for predicting genome-wide enhancer–gene regulatory links using single-cell ATAC-seq or paired ATAC + RNA-seq (multiome) data.
Learn more & access the tool here: github.com/EngreitzLab/...
From data to discovery, IGVF is working to understand how genomic variation influences genome function and phenotype.
Learn more about our work at www.igvf.org
Wishing everyone a spook-tacular Halloween! 👻
Whether you’re celebrating or just enjoying a well-earned weekend, we hope it’s a wickedly wonderful one!
RSV remains a global health challenge.
Using CRISPR knockout screens and single-cell RNA-seq, researchers identified key host factors and responses that drive infection.
Read more: pmc.ncbi.nlm.nih.gov
From all fields and corners of the map, IGVF members came together in Durham, NC. United by one mission, to understand how genomic variation influences function.
A huge thank you to the Durham Bulls Athletic Park for welcoming us and hosting a fantastic night of food, fun, and practice home runs! ⚾
We're live at #ASHG2025!
IGVF members are presenting across multiple sessions this week.
Check out where to find us and come say hi in Boston! 👋🏽
#ASHG2025 kicks off next week!
Keep an eye out for IGVF presentations and posters throughout the meeting.
See you in Boston!
How do genetic variants in brain vascular cells shape neurological disease risk?
With MultiVINE-seq, researchers mapped thousands of risk variants, revealing distinct mechanisms for cerebrovascular vs. neurodegenerative disorders—including Alzheimer’s.
www.cell.com/neuron/fullt...
How do cells respond to chemical and genetic perturbations?
A recent study introduces PerturbNet, a deep generative model that predicts how unseen drugs, CRISPR edits, or even missense mutations alter single-cell gene expression.
Read the full publication:
pmc.ncbi.nlm.nih.gov/articles/PMC...
ASHG 2025 is coming up! 🎉
Keep an eye out for IGVF talks and posters throughout the meeting.
We'll see you in Boston!
scE2G is a computational pipeline for predicting genome-wide enhancer–gene regulatory links using single-cell ATAC-seq or multiome data.
Explore the tool here: github.com/EngreitzLab/...
The IGVF Consortium began with one mission: to understand how genomic variation shapes genome function and influences health. Together, we’re building lasting resources for the research community.
igvf.org
Neomer-based classifiers "short DNA sequences from tumor mutations”, show high accuracy in detecting cancer, even at early stages, and distinguishing subtypes.
Uncover the science behind it: pmc.ncbi.nlm.nih.gov/articles/PMC...
IGVF uses state-of-the-art experimental and computational approaches to model, predict, characterize, and map genome function.
Together, these efforts are building a catalog that reveals how genomic variation influences genome function and phenotypes.
Deadline extended! 🏁
The submission deadlines for TCS2 and ATP7B in CAGI7 are now September 15.
A big thanks to our collaborators in the Fowler and Roth groups for their leadership on this effort.
genomeinterpretation.org/cagi7-challe...
Scanpy is a scalable Python toolkit for analyzing single-cell gene expression data. From preprocessing & visualization to clustering, trajectory inference, and differential expression testing, Scanpy can handle datasets with over 1M cells.
Learn more & access the tool:
github.com/scverse/scanpy
See how IGVF comes together 🧩
Our High-Level Consortium Map highlights the experiments, teams & technologies driving discovery on how genomic variation impacts function.
Explore the map: igvf.org/high-level-c...
MPRAnalyze offers a powerful statistical framework for Massively Parallel Reporter Assays (MPRAs) to study enhancer activity.
From quantifying activity to comparing across conditions, it helps researchers uncover how enhancers drive gene regulation.
bioconductor.org/packages/rel...
The IGVF Data Portal serves as the central hub for research data from the consortium, encompassing raw sequences, analyzed effects, software, and predictive models.
Explore how genomic variation impacts function and phenotype.
data.igvf.org
🔍 scE2G is a computational pipeline for predicting genome-wide enhancer–gene regulatory links using single-cell ATAC-seq or paired ATAC + RNA-seq (multiome) data.
Learn more & access the tool here: github.com/EngreitzLab/...
IGVF is bringing together discoveries from across the network into a lasting, openly available catalog of how genomic variants influence genome function and phenotypes.
A resource that will continue to power future research. Explore our goals and the impact ahead: igvf.org
Using the SEUSS Perturb-seq method, researchers mapped 115 RUNX1 mutations in leukemia cells, revealing wild-type-like, loss-of-function, and hypomorphic types.
Uncover the science behind it: pmc.ncbi.nlm.nih.gov/articles/PMC...
Using the SEUSS Perturb-seq method, researchers mapped 115 RUNX1 mutations in leukemia cells, revealing wild-type-like, loss-of-function, and hypomorphic types.
Uncover the science behind it:
pmc.ncbi.nlm.nih.gov/articles/PMC...
Thousands of genomic variants are linked to disease, but how do they actually function?
IGVF is building a catalog of experimental and computational insights to uncover the impact of variants.
🔎 We’re on the case: igvf.org
Process 30M reads in minutes with high performance and efficiency.
Kallisto is a fast, accurate tool for quantifying transcript abundances from RNA-seq data using pseudoalignment; no full alignment needed.
Explore it here: github.com/pachterlab/k...
A recent study demonstrates how biophysical modeling of high-throughput perturbation data can reveal transcriptional dynamics, like bursting and splicing, that shape cell fate and disease response.
Explore the science behind it: pmc.ncbi.nlm.nih.gov/articles/PMC...
Part of the IGVF single-cell analysis workflow - the IGVF-chromap tool offers a command-line interface for indexing and aligning single-cell ATAC-seq data using the Chromap pipeline.
Explore the tool: github.com/IGVF/atomic-...
