Next Generation Sequencing technologies are powerful tools for rapidly sequencing genomes and studying functional genomics. However, the lack of scalable data analysis capabilities limits their potential. Future bioinformatics applications need to be developed on common standard infrastructures that can reduce overall data storage, increase data processing performance, integrate information from multiple sources and are self-describing. HDF technologies meet all of these requirements, have a long history, and are widely used in data-intensive science communities. They consist of general data file formats, software libraries and tools for manipulating the data. Compared to emerging standards such as the SAM/BAM formats, HDF5-based systems demonstrate improved I/O performance and methods to reduce data storage. HDF5 is also more extensible and can support multiple data indexes and store multiple data types. For these reasons, HDF5 and its BioHDF implementation are well qualified as standards for implementing data models in binary formats to support the next generation of bioinformatics applications.
In the poster we will present:
- An overview of NGS data analysis and workflows
- A prototype data model for working with NGS data
- Practical examples of data analysis and viewing information using the underlying framework
- Performance benchmarks comparing HDF5 to other file formats