TRAL

TRAL annotates and analyzes tandem repeats in amino acid and nucleotide sequences to integrate heterogeneous outputs from sequence tandem repeat detectors and refine annotations using circular profile Hidden Markov Models (cpHMMs).

Key Features:

• Implementation: Python 3 library implementation for programmatic tandem repeat analysis.
• Integration with Multiple Detection Outputs: Runs and parses outputs from over 40 existing sequence tandem repeat detectors to unify heterogeneous results.
• Clustering of Redundant or Overlapping Annotations: Clusters redundant or overlapping tandem repeat annotations to produce consolidated annotations.
• Statistical Frameworks for False Positive Filtering: Applies multiple statistical frameworks to filter false positive tandem repeat annotations.
• Circular Profile Hidden Markov Models (cpHMMs): Provides a tandem repeat annotation and refinement module based on cpHMMs.
• De novo and Profile HMM Support: Leverages de novo software and sequence profile hidden Markov models within its analysis capabilities.

Scientific Applications:

• Genomic and Proteomic Tandem Repeat Annotation: Identification and annotation of tandem repeats in nucleotide and amino acid sequences for genomic and proteomic research.
• Integration of Complementary Detectors: Combining complementary yet conflicting outputs from multiple tandem repeat detectors to improve annotation consensus.
• Large-scale Sequence Analysis: Refinement and analysis of tandem repeats across large sequence datasets, including collections such as UniProtKB/Swiss-Propot.

Methodology:

Runs and parses outputs from multiple tandem repeat detectors, clusters redundant or overlapping annotations, applies statistical frameworks for false positive filtering, and uses circular profile Hidden Markov Models (cpHMMs) together with de novo software and sequence profile hidden Markov models in a multi-step workflow.