AFST

AFST filters and trims abnormal regions of expressed sequence tags (ESTs) to improve the quality of Sanger-derived cDNA sequencing datasets.

Key Features:

• Pattern Analysis: Employs pattern recognition on terminal cDNA sequences to detect sequence anomalies.
• Error Detection: Identifies wet-lab errors such as abnormalities from restriction enzyme cutting and chimeric EST sequences resulting from sequence fusion.
• Data Quality Improvement: Detects and filters abnormal sequences to improve cDNA insert identification and extraction from raw EST data.

Scientific Applications:

• Downstream EST analyses: Enhances reliability of downstream EST-based analyses by providing higher-quality input data.
• Sanger EST dataset processing: Applied to Sanger-derived EST datasets to mitigate sequencing errors and improve sequence integrity.

Methodology:

Pattern recognition analysis of cDNA terminal sequences to detect and filter abnormalities such as restriction-enzyme cutting artifacts and chimeric ESTs.