Tiberius 1.1.7-foss-2023a

Tiberius is a deep learning-based ab initio gene structure prediction tool that end-to-end integrates convolutional and long short-term memory layers with a differentiable HMM layer. It can be used to predict gene structures from genomic sequences only, while matching the accuracy of tool that use extrinsic evidence.

Accessing Tiberius 1.1.7-foss-2023a

To load the module for Tiberius 1.1.7-foss-2023a please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load bear-apps/2023a
module load Tiberius/1.1.7-foss-2023a

There is a GPU enabled version of this module: Tiberius 1.1.7-foss-2023a-CUDA-12.1.1

BEAR Apps Version

2023a

Architectures

EL8-emeraldrapids — EL8-icelake — EL8-sapphirerapids

The listed architectures consist of two parts: OS-CPU. The OS used is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.

More Information

For more information visit the Tiberius website.

Dependencies

This version of Tiberius has a direct dependency on: foss/2023a learnMSA/2.0.10-foss-2023a Python/3.11.3-GCCcore-12.3.0 SciPy-bundle/2023.07-gfbf-2023a

Other Versions

These versions of Tiberius are available on the BEAR systems (BlueBEAR and BEAR Cloud VMs). These will be retained in accordance with our Applications Support and Retention Policy.

Last modified on 9th January 2026