TIBCO Software Inc. has announced that Biogemma, a European leader in plant biotechnologies for the agricultural sector, has selected TIBCO Spotfire® 3.1 to move beyond transcriptomic analyses and embark on new types of integrative research based on genetic maps.
“Before, genetic analyses were mainly conducted with the help of software developed inside a laboratory, but the advantage of using Spotfire to process data positioned on genetic maps quickly became apparent. In addition, with the new version of Spotfire, certain features seemed interesting and prompted us to consider extending its use throughout the company,” said Oliver Dugas, Upstream Genomics Coordinator and Bioinformatics Manager at Biogemma.
As part of its overall plan to develop a comprehensive solution to search all the data produced by the company's various platforms, Biogemma’s first objective is to centralize data from outside projects, projects in collaboration with public or private partners, or data available in the public domain generally published in scientific articles.
This data must be integrated and formatted before it can be used; however, with the flexibility of Spotfire®, Biogemma’s researchers easily get around this problem and are able to work with heterogeneous data. They quickly create links between the data produced internally and all other data from various sources and can produce an accurate and efficient analytical view of their research.
TIBCO Spotfire used for multiple analysis projects
The principal users of Spotfire are the members of the GeneDiscovery team, engineers and PhDs who conduct “in silico” analyses by integrating and comparing data from the services platforms in order to discover the most effective genes for improving plants in an agricultural context. In addition to this team, other researchers and project managers use Spotfire, for instance to visualize analyses of research into similarities between DNA sequences or when they wish to add more of a reporting dimension to the analyses.
These Biogemma researchers work in several areas of research and on four main types of crops: corn, wheat, sunflower, and oilseed rape. Their approach aims to improve these species in order to better meet farmers’ requirements.
Their principal research entails identifying the elements (genes) that encourage the growth of plants adapted to the various growing locations based on the climate constraints (cold, drought), biological constraints (presence of insects, mushrooms, parasites), regulatory constraints (decrease in pesticide inputs) and environmental constraints (reduction of fertilizer and water inputs).
Spotfire allows users to quickly identify favorable genes among the natural biodiversity of these plants. This identification occurs by using genetic maps, by association genetics and by using all the genomic or expressed sequences and, of course, the genomic sequence of the species of interest or of related species when they are available.
Although the process of genetic map creation has now been fully mastered, the various statistics modules available in the Spotfire software make it possible to manipulate the data positioned on the genetic maps and to add information and essential research elements.
“The analysis of the various levels of expression of tens of thousands of genes at the same time will be able to be cross-checked with genetic information to provide even more certainty that a genetic region actually contains an overexpressed gene under conditions of stress. That is where the full power of Spotfire lies,” confirms Dugas.
TIBCO Spotfire for the benefit of all users
Spotfire very quickly became the must-have software at Biogemma. Researchers confirm that its ease of use makes it a tool that is very intuitive to use and very versatile in the functions that it can perform. It also provides the capability to perform different visualizations in both genetics and genomics, and its ability to connect to very diverse data sources such as databases and flat files is a plus. In addition, Biogemma's engineers are keenly aware of the explosion in new technologies, since one of the key aspects in the field of life sciences is sequencing activities which require the retrieval of massive amounts of data - nearly 1 terabyte every 2 months - from different sites in order to carry out more complex analyses. Spotfire has the capability to create simple visualizations, but also to launch analyses with complex statistical analysis scripts, particularly by using the R language. The data and the analyses can therefore be handled by remote computation servers, with the final results processed using the Spotfire software.
Numerous benefits for researchers
The choices made by Biogemma in the work methods used at the company demonstrate a strong desire to make available a wide range of analysis methodologies, combined with a relatively high concentration of resources, proximity of users and collaboration with outside teams which have developed their own methods. As a result, Biogemma’s teams must handle data which come from very different sources and which have been obtained using different approaches, different methods, different visions and different specialities.
Biogemma works with researchers who analyze the contents of proteins or amino acids in seeds and with others who measure soil characteristics under conditions of drought or plant height. Spotfire gives Biogemma’s researchers a data mining tool which is flexible enough to tolerate heterogeneity in terms of data types; added to that are all the very advanced visualization aspects, which offers them quite exceptional statistics report performance.
“Using Spotfire gives us an advantage in terms of relevance and vision. It allows us to analyze our results and visualize unexpected behaviors in the biological data which can have an impact on our research. In these cases, we obtain results that we would not have seen if we had simply conducted standard and relatively fixed analyses,” added Dugas.
Biogemma’s researchers now have access to a new representation of data analysis, and more specifically, in relation to other research, can visualize the results in many ways. For example, they have seen that by analyzing DNA sequences, large regions on chromosomes were organized in a particular manner. They have long had in their possession all the data needed to realize this, but this data, which was too detailed and too abundant, would have needed to be analyzed and dissected by creating an entire analysis chain designed with an underlying hypothesis related to significant genomic movements.
“In this case, we did not establish an underlying hypothesis - the result was just blindingly obvious,” stressed Dugas.