TE-GER binary model: detects presence/absence of Transposable Elements (TE vs Background) in genomic sequences. Architecture: DNABERT-2 + BiLSTM hybrid. Labels: Background, TE.

Model Architecture

Base: DNABERT-2 (DNA language model)
Head: Bidirectional LSTM + Linear Classifier
Input: 512 bp sliding windows over raw FASTA sequences
Task: Sequence classification (token-level TE annotation)

Usage

Use this model via the TE-GER CLI:

python Te_annotator.py genome.fasta output.gff3 --level binary

Labels

0: Background
1: TE

Citation

Developed by Johan S. Piña — 2025

Jspinad/te-ger-binary

TE-GER — Binary Detection

Model Architecture

Usage

Labels

Citation

Jspinad/
te-ger-binary