LONGO: an R package for interactive gene length dependent analysis for neuronal identity

Area of Science:

• Neuroscience
• Bioinformatics
• Genomics

Background:

• Neuronal reprogramming is promising for disease modeling but requires accurate validation of cell identity.
• Current methods for assessing neuronal identity can be variable and lack systematic approaches.
• Long genes (>100 kb) are known to be highly expressed in neurons, offering a potential marker.

Purpose of the Study:

• To develop a systematic and unbiased method for estimating neuronal identity.
• To introduce a novel metric and software package for analyzing long gene expression (LGE).
• To facilitate the validation and improvement of neuronal differentiation and reprogramming techniques.

Main Methods:

• Development of the R package LONGO for analyzing gene expression based on gene length.
• Introduction of the long gene quotient (LQ) metric to quantify LGE.
• Application of LQ to RNA-seq and microarray data for single-cell and population analysis.

Main Results:

• The LONGO package provides a versatile tool for analyzing LGE.
• The LQ metric effectively quantifies LGE, enabling the distinction between neuronal and non-neuronal cells.
• The software facilitates quick and easy validation of neuronal identity.

Conclusions:

• LONGO offers a user-friendly approach to leverage LGE for neuronal identification.
• This method aids in validating neuronal differentiation and reprogramming efficiency.
• The package encourages further research into the role of LGE in neuroscience.