EMPathways2: Estimation of Enzyme Expression and Metabolic Pathway Activity Using RNA-Seq Reads.

Area of Science:

• Bioinformatics
• Metatranscriptomics
• Systems Biology

Background:

• Metatranscriptomic data analysis is crucial for understanding microbial community functions.
• Existing tools may lack comprehensive capabilities for assessing enzyme expression and metabolic pathways.
• Accurate analysis is key to deciphering complex regulatory mechanisms in biological systems.

Purpose of the Study:

• To present a novel bioinformatics software tool, EMPathways2, for metatranscriptomic data analysis.
• To outline a comprehensive analysis pipeline for assessing differential enzyme expression and metabolic pathway activities.
• To facilitate deeper understanding of regulatory mechanisms in living systems through advanced transcriptome analysis.

Main Methods:

• Utilized RNA-Seq data and bioinformatics pipelines including Bowtie2 and IsoEM2 for transcriptome assembly and gene expression quantification.
• Implemented a quality assessment and preprocessing stage for accurate enzyme estimation.
• Employed the EMPathways2 software for deciphering gene-enzyme-pathway relationships.

Main Results:

• Developed a complete analysis pipeline from raw sequencer data to enzyme expression and metabolic pathway activity estimates.
• EMPathways2 effectively deciphers complex relationships between genes, enzymes, and metabolic pathways.
• Provided an online repository with sample data and custom Python scripts for downstream analyses.

Conclusions:

• EMPathways2 offers a robust approach to metatranscriptomic data analysis, enhancing the assessment of enzyme expression and metabolic activities.
• The tool and associated pipeline advance transcriptome data analysis, contributing to a deeper understanding of biological regulatory mechanisms.
• Availability of sample data and scripts supports diverse downstream applications and further research.