Management, Analyses, and Distribution of the MaizeCODE Data on the Cloud

Area of Science:

• Genomics
• Bioinformatics
• Plant Science

Background:

• The MaizeCODE project aims to identify and analyze functional elements within the maize genome.
• Understanding maize functional genomics is crucial for crop improvement and agricultural research.

Purpose of the Study:

• To enhance the SciApps cloud-based portal for accessible analysis and distribution of MaizeCODE data.
• To facilitate reproducible science and provide both human and machine access to maize genomic data.

Main Methods:

• Assayed five tissues from four maize strains (B73, NC350, W22, TIL11) using RNA-Seq, Chip-Seq, RAMPAGE, and small RNA sequencing.
• Developed new components on the SciApps workflow platform for complete MaizeCODE data management.
• Integrated publicly accessible scientific workflows, a RESTful API, a searchable data page, and JBrowse genome browser tracks.

Main Results:

• Generated publicly accessible scientific workflows for reproducible analysis of functional genomic data.
• Implemented a RESTful API for efficient data and metadata batch processing and distribution.
• Created a searchable data page linking MaizeCODE experiments to reproducible workflows and integrated genome browser tracks.

Conclusions:

• The enhanced SciApps portal improves access to MaizeCODE data through metadata and a cloud-based platform.
• The platform supports the integration of new analysis tools, workflows, and genomic data from multiple projects.
• Facilitates maize genomic data analysis and promotes reproducible scientific practices.