Improved selection of canonical proteins for reference proteomes
Giuseppe Insana1, Maria J Martin1, William R Pearson2
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK.
NAR Genomics and Bioinformatics
|June 12, 2024
Summary
UniProt canonical protein sequences can be inconsistent in higher eukaryotes. The ortho2tree pipeline improves canonical assignment by identifying biologically relevant isoforms, enhancing protein annotation accuracy.
Area of Science:
- Bioinformatics
- Proteomics
- Genomics
Background:
- UniProt canonical protein sequences are crucial for research but can be inconsistent in higher eukaryotes due to isoform selection.
- The longest sequence is often chosen as canonical in unreviewed protein databases, leading to biologically unlikely length variations in highly similar orthologs.
Purpose of the Study:
- To develop and validate the ortho2tree pipeline for improving canonical protein sequence assignment.
- To address inconsistencies in canonical protein selection arising from alternative splicing in higher eukaryotes.
Main Methods:
- The ortho2tree pipeline analyzes orthologous protein sequences, builds multiple alignments, and constructs phylogenetic trees to identify isoforms with similar lengths.
- It examines canonical and isoform sequences from UniProt Reference Proteomes, focusing on mammals.
Main Results:
- ortho2tree proposed 7804 canonical changes and confirmed 53,434 canonicals in UniProtKB release 2023_01.
- The pipeline's isoform selection resulted in gap distributions comparable to those in bacteria and yeast, suggesting improved biological accuracy.
- ortho2tree showed high agreement with the MANE (Matched Annotation للنظام Eukaryotes) standard, with 82% agreement for proposed changes and 92% for confirmed canonicals.
Conclusions:
- The ortho2tree pipeline offers a more accurate method for assigning canonical protein sequences, particularly in vertebrates and plants.
- Improved canonical assignment enhances the reliability of protein similarity searching, functional annotation, and structural analysis.
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