Data deluge dam
GenoLogics and SAS team up to create end-to-end analysis solution for large genomic data sets
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VICTORIA, British Columbia—As genomic research centers continue to struggle with the dreaded DNA data deluge, GenoLogics and SAS have constructed a deal to bridge the gap between raw sequence reads and data analysis.
According to an announcement made in late May, the deal involves the integration of GenoLogics' Geneus lab and data management solution with the statistical discovery application of SAS' business division, JMP Genomics. The integration is intended to enable research organizations generate high-quality genomic data sets and apply specialized statistical analysis tools to identify crucial nuggets of information hidden in long lists of candidate genes or biomarkers. Financial terms of the deal were not disclosed.
Counting instrument vendors such as Illumina and ABI as its partners, GenoLogics found JMP Genomics as a natural fit to enable downstream analysis for its clients, says Sal Sanci, vice president of products for GenoLogics.
"The informatics ecosystem out there is large, and no one does everything," Sanci says. "The whole goal of this integration is to provide more of an end-to-end solution for our customers. The complementary fit of our products is really what drove this partnership. Geneus is about making sure that the data researchers are generating is available and accessible. Once you have all that data, we can help you manage it, but you also need to analyze it. That is where JMP Genomics comes in, because they are very good at dealing with huge amounts of data."
Dr. Shannon Conners, JMP Genomics product manager, says the partnership will provide a valuable entrée into the next-generation data market, as JMP Genomics links advanced statistics with graphics, whether the data comes from traditional microarray studies or from summarized results produced by next-generation technologies.
"Next-generation sequencing is an important market for us, and our core competency is downstream statistical analysis. To get users to the point of needing those analytics, we work closely with partners who can provide solutions for sequence data," Conners says. "We first connected with GenoLogics last year at a conference, and realized our products and users shared complimentary goals with respect to generating, managing and analyzing next-generation data."
Geneus, a configurable lab and data management system, supports sample and workflow management, automates pipelines and consolidates data enabling analysis. The system is part of a broader suite of informatics solutions for research labs that enables integrated data analysis for experiments across multiple sciences and systems biology initiatives.
Conners says the latest release of JMP Genomics' desktop software package, JMP Genomics 4, incorporates even more functionality for researchers, including new processes for examining paired data types, support genotype data sets of up to 15,000 individuals and 1.5 million SNPs for selected processes, new and enhanced workflows and graphical features and grid-enablement of selected processes.
"The researcher will have a protocol he wants to run on a number of samples," Sanci explains. "He will run them through Geneus, which will take him through the lab workflow so he is able to track what he does. If there is any QC processing that needs to be done, that will also be done through Geneus. At some point, we will get a set of the files from the instrument that the researcher will want to take to analysis. JMP Genomics will pick them up from there and allow the researcher to do whatever kind of analysis he is looking for."
The integration of both systems can help accelerate scientific discovery by bridging the gap between raw sequence reads and downstream statistical analysis, Conners says.
"We're hearing feedback from many different people in the field that next-generation sequencing, while powerful, is creating a huge amount of data that people are struggling to interpret and summarize in a way they can make sense of," she says. "There is also a challenge with costs. People want to make it straightforward to get from raw sequence data to summarizations. This integration will allow people to create a workflow to manage and store the data, and then plug in algorithms to allow the sequence data to be summarized and analyzed."
According to an announcement made in late May, the deal involves the integration of GenoLogics' Geneus lab and data management solution with the statistical discovery application of SAS' business division, JMP Genomics. The integration is intended to enable research organizations generate high-quality genomic data sets and apply specialized statistical analysis tools to identify crucial nuggets of information hidden in long lists of candidate genes or biomarkers. Financial terms of the deal were not disclosed.
Counting instrument vendors such as Illumina and ABI as its partners, GenoLogics found JMP Genomics as a natural fit to enable downstream analysis for its clients, says Sal Sanci, vice president of products for GenoLogics.
"The informatics ecosystem out there is large, and no one does everything," Sanci says. "The whole goal of this integration is to provide more of an end-to-end solution for our customers. The complementary fit of our products is really what drove this partnership. Geneus is about making sure that the data researchers are generating is available and accessible. Once you have all that data, we can help you manage it, but you also need to analyze it. That is where JMP Genomics comes in, because they are very good at dealing with huge amounts of data."
Dr. Shannon Conners, JMP Genomics product manager, says the partnership will provide a valuable entrée into the next-generation data market, as JMP Genomics links advanced statistics with graphics, whether the data comes from traditional microarray studies or from summarized results produced by next-generation technologies.
"Next-generation sequencing is an important market for us, and our core competency is downstream statistical analysis. To get users to the point of needing those analytics, we work closely with partners who can provide solutions for sequence data," Conners says. "We first connected with GenoLogics last year at a conference, and realized our products and users shared complimentary goals with respect to generating, managing and analyzing next-generation data."
Geneus, a configurable lab and data management system, supports sample and workflow management, automates pipelines and consolidates data enabling analysis. The system is part of a broader suite of informatics solutions for research labs that enables integrated data analysis for experiments across multiple sciences and systems biology initiatives.
Conners says the latest release of JMP Genomics' desktop software package, JMP Genomics 4, incorporates even more functionality for researchers, including new processes for examining paired data types, support genotype data sets of up to 15,000 individuals and 1.5 million SNPs for selected processes, new and enhanced workflows and graphical features and grid-enablement of selected processes.
"The researcher will have a protocol he wants to run on a number of samples," Sanci explains. "He will run them through Geneus, which will take him through the lab workflow so he is able to track what he does. If there is any QC processing that needs to be done, that will also be done through Geneus. At some point, we will get a set of the files from the instrument that the researcher will want to take to analysis. JMP Genomics will pick them up from there and allow the researcher to do whatever kind of analysis he is looking for."
The integration of both systems can help accelerate scientific discovery by bridging the gap between raw sequence reads and downstream statistical analysis, Conners says.
"We're hearing feedback from many different people in the field that next-generation sequencing, while powerful, is creating a huge amount of data that people are struggling to interpret and summarize in a way they can make sense of," she says. "There is also a challenge with costs. People want to make it straightforward to get from raw sequence data to summarizations. This integration will allow people to create a workflow to manage and store the data, and then plug in algorithms to allow the sequence data to be summarized and analyzed."
