Welcome! Orca is a deep learning sequence model framework for multiscale genome structure prediction. Orca can predict genome interactions from kilobase to whole-chromosome-scales using only genomic sequence as input. Orca allows predicting genome structural impacts of any genomic variants, including very large structural variants, or designing virtual genetic screens to probe the sequence basis of genome 3D organization. You can find the Github repo here and the publication here.

Update log

09/18/2023: Added support for complex variants in Seqstr input format. Example input "[hg38]chr9:94904000-110904000 +; chr7:5280600-21280600 -". See input section for more details.

08/11/2022: We have now made a UCSC genome browser trackHub for genome-wide virtual screen of 10bp disruption results here. You can for example use this to find the key CTCF motif behind your genome interaction of interest. You can also directly download the bigWig files for H1-ESC and HFF. Please refer to the publication for more details.

05/10/2022: Updated HFF and H1-ESC models to v0.2 (Nature Genetics 2022 publication version, with minor improvement over the prior bioRxiv version). Job results prior to this date are no longer accessible from original URLs. If you need to retreive your previous prediction results and still have the job ID, feel free to contact us.

What can I use Orca for?

What is Orca?

Orca is a deep learning sequence modeling framework for multiscale genome interaction prediction. Orca models are trained on high-resolution micro-C datasets for H1-ESC and HFF cell lines (and a cohesin-depleted HCT116 Hi-C model for the analysis of sequence dependencies of chromatin compartments). If you have sufficient computational resources including GPUs, you can also train your own models on Hi-C type data given any cooler format input following our examples (see the training section of the code repository).

This webserver provides an user-friendly interface to many of Orca’s prediction capabilities, including predicting multiscale genome 3D organization effects of structural variants. You can also use Orca with the code provided at our Github repository, which provides the full functionalities such as supporting more complex variants or any input sequence. You can also find more information and resources about Orca from the repository.


In the Orca home page, you can select a prediction mode and provide the corresponding input information, then submit the job to our job queue. An example input is provided as a reference for the input format for any prediction mode that you select. Here we list the required input information for all prediction modes that we currently support in the webserver. All coordinates should be in hg38, 0-based, inclusive for the start coordinate and exclusive for the end coordinate.


As an example output, here we showed visualizations generated for the predictions of a duplication variant. For structural variant prediction, Orca generates one (genomic region prediction) or multiple (structural variant prediction) files that each contains a series of multi-level predictions zooming into a breakpoint of the variant, or the corresponding position(s) of the breakpoint in the reference sequence.

Example reference sequence predictions for duplication variant (breakpoint):

Example alternative sequence predictions for duplication variant (right boundary):

For all prediction modes, predicted interaction matrices at multiple scales (1Mb, 2Mb, 4Mb, … ) are visualized with heatmaps, where each pixel represents the interaction between a pair of genomic positions. The interaction scores are represented by log fold over the distance-based background scores (log being natural logarithm). The distance-based background is the expected contact score based on the genomic distance (available from our code repository). We also visualize the observed micro-C data side-by-side for comparison whenever appropriate.

In addtion to the visualizations in pdf format, the results page also allows downloading the numerical predictions in PyTorch serialization format with extension '.pth'. The .pth file can be loaded with torch.load. Each file contains a python dictionary. If the prediction mode is one of the structural variant prediction modes, the dictionary stores multiple dictionaries each corresponding to an output file as described above. The dictionary includes:

Question and feedback?

Thank you for using Orca. If you have any question or feedback, you can let us know at our user email group [email protected].