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.

Open or Closed

A key aspect of Geospiza’s software development and design strategy is to incorporate open scientific technologies into the GeneSifter products to deliver user friendly access to best-of-breed tools used to manage and analyze genetic data from DNA sequencing, microarray, and other experiments.

Open scientific technologies include open-source and published academic algorithms, programs, databases, and core infrastructure software such as operating systems, web servers, and other components needed to build modern systems for data management. Unlike approaches that rely on proprietary software, Geospiza’s adoption of open platforms and participation in the open-source community benefits our customers in numerous ways.

Geospiza’s Open Source History

When Geospiza began in 1997, the company started building software systems to support DNA sequencing technologies and applications. Our first products focused on web-enabled data management for DNA sequencing-based genomics applications. Foundational infrastructure, such as the web-server, and application layer incorporated Apache and Perl. We were also leaders, in that our first systems operated on Linux, an open-source UNIX-based operating system. In those early days, however, we used proprietary databases such as Solid and Oracle because the open-source alternatives Postgres and MySQL were still lacking features needed to support robust data processing environments. As these products matured, we extended our application support to include Postgres to deliver cost-effective solutions for our customers. By adopting such open platforms we were able to deliver robust, high performing systems, rapidly at a reasonable cost.

In addition to using open-source technology as the foundation of our infrastructure, we also worked with open tools to deliver our scientific applications. Our first product, the Finch Blast-Server, utilized the public domain BLAST from NCBI. Where possible, we sought to include well-adopted tools for other applications such as base calling and sequence assembly and repeat masking, for which the source code was made available. We favored these kinds tools over developing our own proprietary tools, because it was clear that technologies emerging from communities like the genome centers would advance much quicker and be better tuned to the problems people were trying to address. Further, these tools, because of their wide adoption within their community and publication, received higher levels of scrutiny and validation than their proprietary counterparts.

Times Change

In the early days, many of the genome center tools were licensed by universities. As the bioinformatics field matured, open-source models for delivering bioinformatics software have become more popular. Led by NCBI and pioneered by organizations like TIGR (now JCVI) and the Sanger institute, the majority of useful bioinformatics programs are now being delivered open-source either under GPL, BSD like, or Perl Artistic style licenses (www.opensource.org). The authors of these programs have benefited from wider adoption of their programs and continued support from funding agencies like NIH. In some cases other groups are extending best-of-breed technologies into new applications.

A significant benefit of the increasing use of open-source licensing is that a large number of analytical tools are readily available for many kinds of applications. Today we have robust statistical platforms like R and BioConductor and several algorithms for aligning Next Gen Sequencing (NGS) data. Because these platforms and tools are truly open-source, bioinformatics groups can easily access these technologies to understand how they work and compare other approaches to their own. This creates a competitive environment for bioinformatics tool providers that drives improvements in algorithm performance and accuracy and the research community benefits greatly.

Design Choices

Early on, Geospiza recognized value incorporating tools from the academic research community into our user friendly software systems. Such tools were being developed in close collaboration with the data production centers that were trying to solve scientific problems associated with DNA sequence assembly and analysis. Companies developing proprietary tools designed to compete with these efforts were at a disadvantage, because they did not have real time access to conversations between biologists, lab specialists, and mathematicians needed to quickly develop the deep experience of working with biologically complex data. This disadvantage continues today. Further, the closed nature of proprietary software limits the ability to publish work and have critical peer review of the code needed to ensure scientific validation.

Our work could proceed more quickly because we did not have to invest in solving the research problems associated with developing algorithms. Moreover, we did not have to invest in proving the scientific credibility of an algorithm. Instead we could cite published references and keep our focus on solving problems associated delivering the user interfaces needed to work with the data. Our customers benefited by gaining easy access to best-of-breed tools and having the knowledge that they had a community to draw on to understand their scientific basis.

Geospiza continues its practice of adopting open best-of-breed technologies. Our NGS systems utilize multiple tools such as MAQ, BWA, Bowtie, MapReads and others. GeneSifter Analysis Edition utilizes routines from the R and BioConductor package to perform statistical computations to compare datasets from microarray and NGS experiments. In addition, we are addressing issues related to high performance computing through our collaboration with the HDF Group and the BioHDF project. In this case we are not only adopting open-source technology, but also working with leaders in the field to make open-source contributions of our own.

When you use Geospiza’s GeneSifter products you can be assured that you are using the same tools as the leaders in our fields to receive the benefits of reducing data analysis costs combined with the advantages of community support through forums and peer reviewed literature.