Innovative statistical methods and software to better exploit genomic technologies
Recent developments in next-generation sequencing (NGS) or DNA sequencing applications have driven statistical research in obtaining optimal ratios for specific data types. However, a gap exists between current statistical analysis tools for single data types and multiple approaches used by biomedical scientists to investigate several omics data types. Novel bioinformatics resources are needed to manage and integrate various data. To address the issue, the EU-funded STATEGRA (User-driven development of statistical methods for experimental planning, data gathering, and integrative analysis of next generation sequencing, proteomics and metabolomics data) project developed statistical approaches and tools to collect and integrate varied NGS and omics data. Project partners created a benchmarking dataset where seven omics data types were obtained through controlled experiments. This data was used to assess and validate STATEGRA methods. They designed integrative methods using various analysis strategies, and developed user-friendly web-based tools where methods are implemented and made available to various stakeholders. The STATEGRA team disseminated the developed tools to the wider genomics and life sciences community, and presented project outcomes at six international conferences. Research resulted in about 50 papers, half of which have already been published in leading biomedical and biostatistical journals. Three workshops, several courses and a summer school were held. In addition, a conference that brought together omics data analysis researchers was launched in 2014 and held again the following year. In the future, there are plans to convert the event into a dedicated meeting for omics data analysis research. STATEGRA provided a set of bioinformatics resources that the genomics community can use to better integrate and understand experiments that involve various omics measurements. Ultimately, these user-friendly software packages that support cutting-edge genomics research in data integration will improve biomedical research and bridge the gap between data production and knowledge.
Keywords
Genomics, next-generation sequencing, omics data, STATEGRA, statistical methods
