GeNemo: a search engine for web-based functional genomic data.

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Big Data Science

Background:

• Functional genomic assays (e.g., ChIP-seq, DNase-seq) generate large datasets like WIG/bigWig and peak/BED files.
• Analyzing these big data types presents significant challenges for researchers.
• Existing search engines are primarily text-based, limiting the analysis of functional genomic patterns.

Purpose of the Study:

• To introduce GeNemo, a web-based search engine specifically designed for functional genomic data.
• To enable pattern matching of functional genomic regions for comparative analysis.
• To facilitate the exploration of large online functional genomic datasets, including ENCODE and mouse ENCODE.

Main Methods:

• GeNemo utilizes a pattern-matching approach for functional genomic regions, distinguishing it from text or sequence searches.
• Users can input complete or partial functional genomic datasets (e.g., bigWig, BED files).
• A Markov Chain Monte Carlo-based maximization process, leveraging up to 24 parallel computing threads, powers the search.

Main Results:

• GeNemo successfully searches user-input data against extensive online functional genomic datasets.
• The engine identifies genomic regions with similar functional patterns (binding, modification, accessibility).
• Search results allow users to visually compare their data and navigate identified genomic regions.

Conclusions:

• GeNemo offers a powerful new tool for analyzing and discovering patterns within functional genomic data.
• The engine simplifies the comparison of user-generated data with large public repositories like ENCODE.
• GeNemo enhances biological discovery by enabling efficient exploration of functional genomic datasets.