MasterPATH: network analysis of functional genomics screening data.

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Functional genomics screens generate gene lists (hit lists) for biological process investigation.
• Existing computational methods focus on enriched pathways or subnetworks.
• Novel approaches are needed to uncover specific molecular pathways from screening data.

Purpose of the Study:

• To develop and present a novel network analysis method for discovering molecular pathway members.
• To identify genes and pathways associated with specific phenotypes using functional genomics data.
• To provide a computational tool for interpreting gene lists from high-throughput screens.

Main Methods:

• Developed a network analysis method utilizing shortest path and centrality measures.
• Integrated multiple interactome databases (HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase).
• Applied the method to miRNA loss-of-function screens, transcriptome profiling of muscle differentiation, and oxidative DNA damage recognition screens.

Main Results:

• Identified key myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets in muscle differentiation.
• Linked MYOD and SMAD3 to known and novel muscle-related targets, including C-KIT.
• Revealed the role of the H3-SETDB1 interaction in oxidative DNA damage recognition.

Conclusions:

• The study presents a systematic methodology for discovering molecular pathways from integrated networks and functional genomics data.
• This approach offers a valuable tool for explaining unexpected gene associations in screening hit lists.
• The findings provide new insights into muscle differentiation and DNA damage recognition pathways.