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Computational framework for scalable acceleration of database peptide search on supercomputers

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HiCOPS is a software framework for accelerating database peptide search workflows on supercomputers. HiCOPS provided algorithm-independent parallelizations and optimizations can be extended into new HPC database search algorithms or scalably accelerate the existing ones.

HiCOPS has been implemented using C++17, Python 3.7 and Bash. A high-level graphical abstract of our parallel software framework is shown in the following figure.

Graphical Abstract


Learn about our research work in detail in the paper given as follows. You can access the paper via SharedIt.

Muhammad Haseeb, and Fahad Saeed. “High performance computing framework for tera-scale database search of mass spectrometry data.” Nature Computational Science, Volume no. 1, Issue no. 8 (2021): pp no. 550-561.

Please cite our paper if you use our work. Thank you.


Computational Proteomics researchers and developers can integrate their algorithms within the HiCOPS framework. Integration is as simple as implementing conventional (shared-memory) versions of database indexing, filtering, peptide-to-spectrum scoring, post-processing etc. algorithms within HiCOPS.

Supported Environments

HiCOPS can seamlessly run on any Linux based workstation, however we recommend running on symmetric multinode (the most common) HPC systems. Sufficient amount of memory resources must be allocated to HiCOPS to handle the database index.


HiCOPS is under development at the Parallel Computing and Data Science Laboratory at Florida International University.

Name Affiliation GitHub
Muhammad Haseeb FIU mhaseeb123
Fahad Saeed FIU pcdslab


We welcome contributions via GitHub pull requests. For more information, please refer to Contributing document.


HiCOPS is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Both commercial and academic users can collaborate, contribute, and use the software for research or development by acquiring an appropriate license from: