ULTRA

ULTRA detects tandem repeats in biological sequences using a Hidden Markov Model to identify and label degenerate repetitive regions that can obscure annotation and create false homology signals.

Key Features:

• Hidden Markov Model (HMM): A novel HMM is employed and specifically tailored to detect tandem repeats with enhanced sensitivity.
• Repeat types detected: Identifies repetition of short patterns (e.g., 'atg') and repeats that range from tens to hundreds of residues.
• Detection of degenerate repeats: Sensitively detects decayed or obscured tandem repeats that complicate annotation.
• False annotation control: Produces low and reliable false annotation rates across diverse sequence compositions.
• Stable scoring: Generates scores that follow a stable distribution to support consistent decision thresholds.
• Computational efficiency: Demonstrates competitive time and memory requirements compared to TRF.

Scientific Applications:

• Repeat masking for alignments: Masking tandem repeats prior to sequence alignment to reduce spurious homology signals.
• Genome and sequence annotation: Labeling repetitive regions to improve accuracy of gene and feature annotation.
• Comparative genomics: Mitigating false signals of homology caused by similar repetitive patterns across sequences.
• Detection of replication slippage products: Identifying tandem repeats that arise from replication slippage and subsequent decay.
• Integration into analysis pipelines: Use within sequence alignment tools and annotation pipelines to improve downstream analyses.

Methodology:

ULTRA applies a novel Hidden Markov Model to detect and score tandem repeats, produces scores with a stable distribution, and shows competitive time and memory performance relative to TRF.