SeqEngine: Automated Feature Engineering & Selection for Protein and DNA Sequences

SeqEngine is a fully automated, end‑to‑end pipeline for feature extraction and feature selection from biological sequences (protein and DNA). It extracts a comprehensive set of 610 features for proteins and 400 features for DNA, implements seven feature selection methods (univariate filters, wrapper, and embedded), and outputs multiple curated feature subsets along with a detailed text report containing sequence statistics, feature rankings, and performance metrics. Designed for researchers who need a reproducible, interpretable, and efficient tool to pre‑process sequence data for machine learning.

📊 Pipeline Overview

The figure below shows the complete workflow of SeqEngine:

✨ Key Features

• Comprehensive Feature Extraction:
  • Protein: 610 features including AAC, dipeptide, PAAC, CTD, Moran autocorrelation, BLOSUM62, and physicochemical properties.
  • DNA: 400 features including NAC, DNC, TNC, k‑mer (k=4), PseDNC, Moran autocorrelation, and ENAC.
• Multiple Feature Selection Strategies:
  • Univariate Filters: f_classif, mutual_info, chi2
  • Wrapper: Recursive Feature Elimination (RFE) with Random Forest
  • Embedded: Random Forest importance, Lasso (L1), Gradient Boosting importance
• Human‑Readable Output: Feature names like AAC_Alanine, CTD_Comp_Hydrophobicity_G1, Kmer_AAAA—no more cryptic F1, F2.
• Batch Processing with tqdm progress bars for large datasets.
• Parallel Execution using multiple CPU cores.
• Comprehensive Report: Sequence statistics, feature statistics, method‑by‑method performance (accuracy, precision, recall, F1, MCC), top‑20 ranked features, and execution times.
• Multiple Output Formats: Consolidated feature CSV, per‑method selected feature CSVs, aggregate ranking summary, and a full text report.

📦 Installation

pip install seqengine