In silico approach to calculate the transcript capacity.

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• The functional capacity of transcripts post-translation remains an area requiring novel quantification methods.
• Understanding transcript function is crucial for deciphering cellular mechanisms and disease etiology.

Purpose of the Study:

• To introduce and analyze the novel concept of transcript capacity (TC).
• To develop an in silico method for estimating TC using existing genomic and transcriptomic data.
• To explore the biological functions associated with varying levels of TC.

Main Methods:

• Developed an in silico approach to estimate transcript capacity (TC).
• Utilized genome-wide association study (GWAS) beta effects and RNA-sequencing transcription levels.
• Integrated data from the human protein atlas for body fat percent trait analysis.

Main Results:

• Identified distinct Gene Ontology (GO) terms associated with high and low TC.
• Highest TC genes were predominantly linked to neuronal functions and cell projection organization.
• Lowest TC genes were frequently associated with wound healing and embryonic development.

Conclusions:

• The developed in silico method provides a novel way to estimate transcript capacity.
• TC estimation reveals functional divergence, with high TC linked to neuronal activity and low TC to developmental processes.
• This approach offers potential for broader applications in comparative genomics and bioinformatics across species.