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panushri25 committed Oct 28, 2023
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- This repo contains code for the paper `Bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants` (technical report coming soon) by Anusri Pampari*, Anna Shcherbina*, Anshul Kundaje. (*authors contributed equally)
- Please contact [Anusri Pampari] (\<first-name\>@stanford.edu) for suggestions and comments.
- Here is a link to the [slides](https://docs.google.com/presentation/d/1Ow6K8TYN40u7T3ODdo-JRCLuv5fUUacA/edit?usp=sharing&ouid=104820480456877027097&rtpof=true&sd=true), [ISMB talk](https://www.youtube.com/watch?v=3W3JeJvvjLc) and a comprehensive [tutorial](https://github.com/kundajelab/chrombpnet/wiki). Please see the [FAQ](https://github.com/kundajelab/chrombpnet/wiki/FAQ) and file a github [issue](https://github.com/kundajelab/chrombpnet/issues) if you have questions.
- If you are using chrombpnet <= v0.1.3 please refer to the note here - https://github.com/kundajelab/chrombpnet/wiki/Denovo-motif-discovery

Chromatin profiles (DNASE-seq and ATAC-seq) exhibit multi-resolution shapes and spans regulated by co-operative binding of transcription factors (TFs). This complexity is further difficult to mine because of confounding bias from enzymes (DNASE-I/Tn5) used in these assays. Existing methods do not account for this complexity at base-resolution and do not account for enzyme bias correctly, thus missing the high-resolution architecture of these profiles. Here we introduce ChromBPNet to address both these aspects.

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## How to Cite

If github citations are not allowed in your submissions please reach out to Anusri Pampari and Anshul Kundaje.
If you're using ChromBPNet in your work, please cite as follows:


```
@software{Pampari_Bias_factorized_base-resolution_2023,
author = {Pampari, Anusri and Shcherbina, Anna and Nair, Surag and Schreiber, Jacob and Patel, Aman and Wang, Austin and Kundu, Soumya and Shrikumar, Avanti and Kundaje, Anshul},
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