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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

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BigWig and BigBed: enabling browsing of large distributed datasets.

W J Kent¹, A S Zweig, G Barber

¹Center for Biomolecular Science and Engineering, School of Engineering, University of California, Santa Cruz (UCSC), Santa Cruz, CA 95064, USA.

Bioinformatics (Oxford, England)

|July 20, 2010

Summary

BigWig and BigBed files enable efficient visualization of next-generation sequencing data in the UCSC Genome Browser. This approach transmits only necessary data for fast remote access to large datasets.

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Area of Science:

Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) experiments generate large datasets.
Efficient visualization and remote access to this data are crucial for analysis.

Purpose of the Study:

To describe the BigWig and BigBed file formats for high-performance display of NGS data.
To detail the software implementation enabling efficient data transmission and visualization.

Main Methods:

Utilized compressed binary indexed files (BigWig and BigBed) at multiple resolutions.
Implemented a multi-layered software approach leveraging web protocols, Linux/UNIX systems, R-trees, indexing, and compression.
Developed utilities for BigWig and BigBed creation and parsing.

Main Results:

BigWig and BigBed files facilitate high-performance display of NGS experiment results.
The software approach ensures only required data is transmitted, enabling fast remote access.
Data is accessible at various resolutions for detailed or overview views.

Conclusions:

BigWig and BigBed formats provide an efficient solution for managing and visualizing large-scale NGS data.
The implemented software architecture optimizes data transfer, enhancing usability of the UCSC Genome Browser.
Freely available source code and binaries support non-commercial use and further development.