Gene structure prediction and alternative splicing analysis using genomically aligned ESTs

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The human genome sequence enables gene prediction via expressed sequence tag (EST) alignment.
• Accurate gene structure elucidation requires identifying splice junctions, boundaries, and alternative splicing.

Purpose of the Study:

• To develop a computational tool for delineating gene structures from EST alignments.
• To analyze alternative splicing patterns and their evolutionary conservation.

Main Methods:

• Developed the Transcript Assembly Program (TAP) using a novel algorithm for EST assembly.
• Integrated polyadenylation site scan (PASS) for gene boundary identification.
• Analyzed alternative splicing in human and mouse EST data.

Main Results:

• TAP achieved 60% sensitivity and 92% specificity at the exon level for known transcripts.
• Gene boundary identification accuracy was 78%.
• Over 50% of human genes show alternative splicing, predominantly affecting coding regions.

Conclusions:

• TAP is an effective tool for gene structure prediction and alternative splicing analysis.
• Alternative splicing is widespread in humans and significantly impacts coding sequences.
• Comparative analysis provides insights into the evolution of alternative splicing.