Model group Models Authors Contributed by Type Cite as License Tags
Model group Models Authors Contributed by Type Cite as License Tags
APARENT 2 Nicholas Bogard et al. Shabnam Sadegharmaki et al. None https://doi.org/10.1101/300061 MIT
AttentiveChrome 55 Ritambhara Singh et al. Jack Lanchantin et al. pytorch https://doi.org:/10.1101/329334 MIT RNA expression
BPNet-OSKN 1 Ziga Avsec Ziga Avsec None https://doi.org/10.1101/737981 MIT DNA binding
BPNet_Dmel_OreR_2to3hr_ZDTBCG 1 Melanie Weilert and Kaelan Brennan Melanie Weilert and Kaelan Brennan None https://doi.org/10.1101/2022.12.20.520743 MIT
Basenji 1 David R. Kelley Ziga Avsec tensorflow https://doi.org/10.1101/gr.227819.117 Apache License v2 RNA expression, Histone modification, DNA accessibility
Basset 1 David R. Kelley Roman Kreuzhuber pytorch https://doi.org/10.1101/gr.200535.115 MIT DNA accessibility
CleTimer 2 Leohnard Wachutka et al. Leohnard Wachutka et al. None Apache License v2 RNA splicing
CpGenie 51 Haoyang Zeng Roman Kreuzhuber custom, keras https://doi.org/10.1093/nar/gkx177 Apache License v2 DNA methylation
DeepBind 927 Babak Alipanahi et al. Johnny Israeli keras https://doi.org/10.1038/nbt.3300 BSD 3-Clause DNA binding
DeepCpG_DNA 5 Christof Angermueller Roman Kreuzhuber keras https://doi.org/10.1186/s13059-017-1189-z
https://doi.org/10.5281/zenodo.1094823 		MIT DNA methylation
DeepFlyBrain 1 Ibrahim Ihsan Taskiran et al. Ibrahim Ihsan Taskiran et al. None TBA MIT
DeepLiver 3 Carmen Bravo et al. Carmen Bravo et al. None Bravo
González-Blas
Carmen.
(2022).
Enhancer
grammar
of
liver
cell
types
and
hepatocyte
zonation
states.
https://doi.org/10.1101/2022.12.08.519575 		Other / Non-commercial (see LICENSE.txt)
DeepMEL 3 Ibrahim Ihsan Taskiran et al. Ibrahim Ihsan Taskiran et al. None https://doi.org/10.1101/gr.260844.120
https://doi.org/10.1101/2019.12.21.885806 		MIT
DeepSEA 3 Jian Zhou et al. Roman Kreuzhuber None, pytorch https://doi.org/10.1038/s41588-018-0160-6
https://doi.org/10.1038/nmeth.3547 		Non-comercial, CC-BY 3.0 Histone modification, DNA binding, DNA accessibility
DeepSTARR 1 Bernardo P. de Almeida Bernardo P. de Almeida None https://doi.org/10.1101/2021.10.05.463203 MIT
Divergent421 1 Nancy Xu Nancy Xu None MIT DNA accessibility
FactorNet 30 Daniel Quang et al. Ziga Avsec keras https://doi.org/10.1101/151274 MIT DNA binding
Framepool 1 Alexander Karollus Alexander Karollus None MIT Translation
HAL 1 Alexander B. Rosenberg Jun Cheng et al. None https://doi.org/10.1016/j.cell.2015.09.054 MIT RNA splicing
KipoiSplice 2 Ziga Avsec et al. Ziga Avsec et al. sklearn https://doi.org/10.1101/375345 MIT RNA splicing
MMSplice 5 Jun Cheng Jun Cheng custom MIT RNA splicing
MPRA-DragoNN 2 Rajiv Movva, Surag Nair Rajiv Movva, Surag Nair None https://doi.org/10.1101/393926 MIT RNA expression
MaxEntScan 2 Gene Yeo et al. Jun Cheng et al. None https://doi.org/10.1089/1066527041410418 MIT RNA splicing
OptMRL 1 Frederick Korbel, Ekaterina Eroshok, Uwe Ohler Frederick Korbel, Ekaterina Eroshok, Uwe Ohler None https://doi.org/10.1101/2023.06.02.543405 GPL-3
Optimus_5Prime 1 Paul J. Sample Ban Wang None https://doi.org/10.1101/310375 MIT Translation
SeqVec 3 Michael Heinzinger, Ahmed Elnaggar et al. Michael Heinzinger, Ahmed Elnaggar et al. None https://doi.org/10.1101/614313
https://doi.org:/10.1101/614313 		MIT Protein properties, Protein structure
SiSp 1 Lara Urban Lara Urban keras https://doi.org/10.1101/328138 MIT RNA splicing
TREDNet 4 Sanjarbek Hudaiberdiev Sanjarbek Hudaiberdiev None https://medrxiv.org/cgi/content/short/2022.05.13.22275035v1 CC-BY-ND
Xpresso 5 Vikram Agarwal Vikram Agarwal None https://doi.org/10.1016/j.celrep.2020.107663 MIT
a2z-chromatin 2 Travis Wrightsman Travis Wrightsman None https://doi.org/10.1101/2021.11.11.468292 MIT DNA accessibility, DNA methylation, Plants
deepTarget 1 Byunghan Lee et al. Ziga Avsec None https://arxiv.org/pdf/1603.09123.pdf GPL-v3 RNA binding
epidermal_basset 60 Daniel Kim Daniel Kim None https://doi.org:/... MIT
extended_coda 1 Pang Wei Koh et al. Johnny Israeli keras https://doi.org/10.1093/bioinformatics/btx243 MIT Histone modification
labranchor 1 Joseph M. Paggi et al. Jun Cheng keras https://doi.org/10.1101/185868 CC BY-NC 4.0 RNA splicing
lsgkm-SVM 322 Dongwon Lee Roman Kreuzhuber None https://doi.org/10.1093/bioinformatics/btw142 MIT DNA binding
pwm_HOCOMOCO 600 Ivan V. Kulakovskiy Ziga Avsec keras https://doi.org/10.1093/nar/gkv1249 MIT DNA binding
rbp_eclip 102 Ziga Avsec Ziga Avsec keras https://doi.org/10.1093/bioinformatics/btx727 MIT RNA binding
scbasset 1 Han Yuan, David R. Kelley Han Yuan keras https://doi.org/10.1101/gr.200535.115 Apache License 2.0 scATAC accessibility

Kipoi models

[!WARNING]

Kipoi Project - Sunset Announcement

After several impactful years, we have made the decision to archive the Kipoi repositories and end active maintenance of the project.

This is a bittersweet moment. While it’s always a little sad to sunset a project, the field of machine learning in genomics has evolved rapidly, with new technologies and platforms emerging that better meet current needs. Kipoi played an important role in its time, helping researchers share, reuse, and benchmark trained models in regulatory genomics. We’re proud of what it accomplished and grateful for the strong community support that made it possible.

Kipoi’s impact continues, however:

  • The Kipoi webinar series will carry on, supporting discussions around model reuse and interpretability.
  • Kipoiseq, our standard set of data-loaders for sequence-based modeling, also remains active and relevant.

Thanks to everyone who contributed, used, or supported Kipoi. It’s been a fantastic journey, and we're glad the project helped shape how models are shared in the field.

- The Kipoi Team

CircleCI DOI

This repository hosts predictive models for genomics and serves as a model source for Kipoi. Each folder containing model.yaml is considered to be a single model.

Contributing models

  1. Install kipoi: 

    pip install kipoi

  2. Run kipoi ls. This will checkout the kipoi/models repo to ~/.kipoi/models)

  3. Follow the instructions on contributing/Getting started.

Using models (to predict, score variants, build new models)

To explore available models, visit http://kipoi.org/models. See kipoi/README.md and docs/using getting started for more information on how to programatically access the models from this repository using CLI, python or R.

Configuring local storage location

This model source (https://github.com/kipoi/models) is included in the Kipoi config file (~/.kipoi/config.yaml) by default:

# ~/.kipoi/config.yaml
model_sources:
    kipoi:
        type: git-lfs
        remote_url: [email protected]:kipoi/models.git
        local_path: ~/.kipoi/models/
        auto_update: True

If you wish to keep the models stored elsewhere, edit the local_path accordingly.