Expeditiously Exponential: Data Sharing and Standardization

We can all agree that our ability to produce genomics and other kinds of data is increasing at exponential rates. Less clear, is understanding the consequences for how these data will be shared and ultimately used. These topics were explored in last month's (Oct. 9, 2009) policy forum feature in the journal Science.

The first article, listed under the category "megascience," dealt with issues about sharing 'omics data. The challenge being that systems biology research demands that data from many kinds of instrument platforms (DNA sequencing, mass spectrometry, flow cytometry, microscopy, and others) be combined in different ways to produce a complete picture of a biological system. Today, each platform generates its own kind of "big" data that, to be useful, must be computationally processed and transformed into standard outputs. Moreover, the data are often collected by different research groups focused on particular aspects of a common problem. Hence, the full utility of the data being produced can only be realized when the data are made open and shared throughout the scientific community. The article listed past efforts in developing sharing policies and the central table included 12 data sharing policies that are already in effect.

Sharing data solves half of the problem, the other aspect is being able to use the data once shared. This requires that data be structured and annotated in ways that make it understandable by a wide range of research groups. Such standards typically include minimum information check lists that define specific annotations, and which data should be kept from different platforms. The data and metadata are stored in structured documents that reflect a community's view about what is important to know with respect to how data were collected and the samples the data were collected from. The problem is that annotation standards are developed by diverse groups and, like the data, are expanding. This expansion creates new challenges with making data interoperable; the very problem standards try to address.

The article closed with high-level recommendations for enforcing policy through funding and publication requirements and acknowledged that full compliance requires that general concerns with pre-publication data use and patient information be addressed. More importantly, the article acknowledged that meeting data sharing and formatting standards has economic implications. That is, researches need time-efficient data management systems, the right kinds of tools and informatics expertise to meet standards. We also need to develop the right kind of global infrastructure to support data sharing.

Fortunately complying with data standards is an area where Geospiza can help. First, our software systems rely on open, scientifically valid tools and technologies. In DNA sequencing we support community developed alignment algorithms. The statistical analysis tools in GeneSifter Analysis Edition utilize R and BioConductor to compare gene expression data from both microarrays and DNA sequencing. Further, we participate in the community by contributing additional open-source tools and standards through efforts like the BioHDF project. Second, the GeneSifter Analysis and Laboratory platforms provide the time-effiecient data management solutions needed to move data through its complete life cycle from collection, to intermediate analysis, to publishing files in standard formats.

GeneSifter lowers researcher's economic barriers of meeting data sharing and annotation standards keep the focus on doing good science with the data.