FrameD

FrameD predicts coding regions and corrects frameshifts in prokaryotic and matured eukaryotic sequences, enabling gene prediction from incomplete or partially determined genomic and transcriptomic data.

Key Features:

• Gene prediction in prokaryotic and matured eukaryotic sequences: Predicts coding regions in prokaryotic genomes and matured eukaryotic sequences.
• Frameshift Prediction: Predicts genes in the presence of frameshift mutations to enable analysis of sequences with frameshifts.
• Handling Partially Undetermined Sequences: Predicts coding regions in sequences containing gaps or ambiguous bases, including expressed sequence tags (ESTs) and EST cluster sequences.
• Incorporation of Protein Similarity Information: Integrates protein similarity information into its predictive model to refine accuracy.
• Model Learning for New Organisms: Supports training of new models tailored to specific organisms.

Scientific Applications:

• Gene Prediction in Bacterial Genomes: Predicts genes in bacterial genomes, with demonstrated effectiveness in GC-rich bacterial genomes.
• Frameshift Correction: Corrects frameshifts within unfinished sequences to improve gene annotation quality.
• EST Analysis: Facilitates gene prediction in expressed sequence tags (ESTs) and EST cluster sequences for transcriptome studies.

Methodology:

FrameD employs a gene model that identifies coding regions despite frameshifts and incomplete sequences, integrates protein similarity information, and has been evaluated on bacterial genomes including GC-rich species.