FEPS is a comprehensive web-based bioinformatics tool designed to extract the most widely used sequence-derived features from protein sequences.
Pioneering the field of automated protein feature extraction, FEPS was first released in 2016 and has since evolved into a powerful platform supporting both traditional machine learning and modern deep learning approaches.
- 48 distinct feature extraction methods
- Organized into 7 major feature groups
- Computes a total of 2765 descriptors
- Supports multiple input groups and class labels
- Direct integration with ML/DL pipelines (SVM, RF, KNN, CNN, etc.)
- Designed for scalability, reproducibility, and usability
📖 Ismail et al., 2022 — FEPS: A Tool for Feature Extraction from Protein Sequence
FEPS-generated features can be used for a wide range of classification and prediction tasks in computational biology, such as:
- 🧠 Protein function prediction
- 🧬 Protein classification
- 🧱 Protein structure prediction
- 📍 Subcellular localization prediction
- ➕ And many more!
👉 Launch the FEPS tool: https://hdismail.com/feps/
FEPS accepts fasta-formatted protein sequence files. The recommended format for classification scenarios:
- ✅ Sequences must be valid protein sequences
- ✅ Files must be in FASTA format
- ✅ Sequences of the same class/group should be combined into one file
- ✅ The filename will be used to label the group
💡 Replace
your-username/your-repo-namewith the actual GitHub repo path.
The extracted features are organized into 7 major groups:
| Group | Feature Category |
|---|---|
| 1 | Amino Acid Composition |
| 2 | Composition, Transition, and Distribution |
| 3 | Autocorrelation Descriptors |
| 4 | Pseudo Amino Acid Composition |
| 5 | Quasi & Sequence-Order-Coupling |
| 6 | Shannon Entropy Descriptors |
| 7 | Conjoint Triad |
Users can interactively select any group and corresponding methods using the web interface.
👉 Visit the FEPS Webserver: https://hdismail.com/feps/
👉 Read the full article: Methods in Molecular Biology, 2022
👉 Download example dataset
If you use FEPS in your research, please cite:
Ismail H, White C, Al-Barakati H, Newman RH, Kc DB. (2022). FEPS: A Tool for Feature Extraction from Protein Sequence. Methods Mol Biol. 2499:65–104. https://doi.org/10.1007/978-1-0716-2317-6_3
- Dr. Hamid D. Ismail
- Collaborators: White C, Al-Barakati H, Newman RH, Kc DB
For questions or feature requests, please contact:
📧 hamid@hamiddi.com
This project is licensed under the MIT License.