An automated method for detecting alternatively spliced protein domains.

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Alternative splicing (AS) generates eukaryotic gene diversity at the transcriptome level.
• The precise impact of AS on the proteome remains controversial and technically challenging to study.
• Current methods for analyzing AS at the proteomic level face difficulties in coordinate mapping and suffer from redundant computations.

Purpose of the Study:

• To develop a systematic and efficient pipeline for identifying protein domains affected by alternative splicing.
• To assess the global impact of alternative splicing on the proteome.
• To pinpoint specific instances where AS significantly alters protein domain function.

Main Methods:

• Development of the AstaFunk pipeline utilizing Viterbi dynamic programming.
• Implementation of optimizations to avoid redundant calculations and ensure score-optimal domain hit detection.
• Qualitative and quantitative evaluation using RNA sequencing data on well-studied genes and entire transcriptomes.

Main Results:

• The AstaFunk pipeline efficiently computes AS-altered domains across transcriptomes.
• The study confirms that most AS events have a limited effect on the proteome.
• Several cases were identified where AS significantly impacts protein domain function.

Conclusions:

• The AstaFunk pipeline provides an efficient method for analyzing AS at the proteomic level.
• While generally limited, AS can critically influence protein function in specific contexts.
• The findings contribute to understanding the functional consequences of alternative splicing in eukaryotes.