Pergola: Boosting Visualization and Analysis of Longitudinal Data by Unlocking Genomic Analysis Tools

Area of Science:

• Computational neuroscience
• Genomics data standards
• Behavioral data analysis

Background:

• Behavioral neuroscience requires automated data production.
• Current computational standards lack interoperability, reproducibility, and shareability.
• Need for novel computational approaches in behavioral data handling.

Purpose of the Study:

• To address the need for improved computational standards in behavioral neuroscience.
• To demonstrate repurposing genomic data formats for behavioral time-series data.
• To enhance interoperability, reproducibility, and shareability of behavioral neuroscience data.

Main Methods:

• Repurposing existing genomic data formats for time-series behavioral data.
• Implementing data conversion procedures using the open-source software Pergola.
• Applying established genomics analysis and visualization tools to reformatted behavioral data.

Main Results:

• Successfully adapted genomic formats to handle behavioral time-series data.
• Reproducible analysis of behavioral data from mouse, fly, and worm models using genomics tools.
• Demonstrated application of principal component analysis, hidden Markov modeling, and volcano plots to behavioral data.

Conclusions:

• Genomic data formats offer a robust solution for managing and analyzing behavioral neuroscience data.
• Repurposing genomic tools enhances the reproducibility and shareability of behavioral neuroscience research.
• Integration with workflow managers like Nextflow further solidifies reproducible research practices.