Understanding the causes of errors in eukaryotic protein-coding gene prediction: a case study of primate proteomes

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Accurate genome annotation is crucial but challenging, especially for eukaryotic protein-coding genes due to complex exon-intron structures.
• Existing gene prediction algorithms can produce significant errors affecting downstream analyses.

Purpose of the Study:

• To investigate the prevalence and causes of gene prediction errors in primate proteomes.
• To develop methods for improving protein sequence quality.

Main Methods:

• Analyzed 176,478 proteins from ten primate proteomes, using human proteins as a reference.
• Characterized gene prediction errors, focusing on mismatched segments.
• Identified potential causes for mispredictions and developed a proof-of-concept improvement method.

Main Results:

• Detected 82,305 potential errors, including deletions, insertions, and mismatched segments.
• Identified causes for approximately half of the mismatched sequence errors.
• Successfully proposed improved sequences for 603 primate proteins.

Conclusions:

• Gene prediction errors affect up to 50% of primate protein sequences.
• Causes include undetermined genome regions, sequencing issues, and model limitations.
• Existing genome data can be leveraged to enhance protein sequence quality.