Integrating multi-omics data to investigate pseudogene expression in Mycolicibacterium smegmatis

Area of Science:

• Microbiology
• Genomics
• Molecular Biology

Background:

• Large-scale omics data enables genome-wide functional element investigation.
• Pseudogenes are DNA sequences similar to genes but lacking protein-coding ability.
• Accurate annotation of bacterial pseudogenes is crucial for understanding genome function.

Purpose of the Study:

• To assess the expression and translation of pseudogenes in the M. smegmatis genome.
• To identify potential sequencing errors affecting pseudogene annotation.
• To explore the utility of multi-dimensional omics data for bacterial pseudogene analysis.

Main Methods:

• Utilized RNA-Sequencing (RNA-Seq) data to analyze gene expression.
• Focused on 134 pseudogenes lacking duplicated protein-coding genes in M. smegmatis.
• Examined RNA-Seq reads to detect potential sequencing errors.

Main Results:

• Observed significant expression and translation in 28 out of 134 pseudogenes.
• Identified potential sequencing errors in numerous pseudogenes, impacting their annotation.
• Highlighted that functionally relevant genes like recN and manB may be affected by these errors.

Conclusions:

• Bacterial pseudogene annotation may be influenced by sequencing errors.
• Multi-dimensional omics data analysis is valuable for re-evaluating pseudogene annotations.
• Further investigation is needed to clarify the functional role of identified pseudogenes.