Qlucore, a world leader in the development of bioinformatics software, has today announced that the Genomics Centre, a core research facility at King’s College London, has chosen to use the Qlucore Omics Explorer for advanced data analysis on a wide range of research projects. As a core facility at King’s College the Genomics Centre acts as a central resource for researchers, and also provides access to essential technology and genomics equipment for scientists based at King’s College and beyond.
King’s College selected Qlucore’s highly intuitive bioinformatics application following an in-depth demonstration of the product’s data analysis capabilities, followed by a rigourous evaluation period. With the Qlucore software now in place researchers at the Genomics Centre can expect to shorten analysis time and improve research quality, thanks to the software’s ability to provide instant results.
“Qlucore Omics Explorer analyses data more quickly than any product we have used before,” says Dr Matthew Arno, manager of The Genome Centre at King’s College. “The data analysis is actually performed in real time, with the researchers sitting in front of the PC. As a result, researchers can easily form and evaluate a number of different scenarios and hypotheses in rapid succession, in a very short space of time.”
The King’s College Genomics Centre was set-up in recognition of the profound effect that genome sequencing has on biology and medicine, and to allow scientists to perform many functional genomics studies including gene expression analysis by microarray screening and quantitative PCR, and genetic analysis (sequencing and genotyping). The centre has been designated as a small research facility, and is currently under the jurisdiction of the College’s health schools.
Researchers at the Genomics Centre are currently using the Qlucore software to analyse data produced by a wide variety of research projects, including a study on the effects of metal exposure and ageing in worms (C.elegans). Most recently, scientists have used the software with researchers from Nottingham University’s Queen’s Medical Centre to study the molecular characteristics of different parts of the human eye, based on important data taken from laser capture microdissected samples.
Direct and interactive data analysis
Because the software allows the user to manipulate and filter all of the data at the same time – while also instantly visualising it in 3D – the researchers working at King’s College now can analyse their data directly and interactively on the computer screen. As such, the software is already proving invaluable for unveiling important new discoveries, as it allows actual researchers – the people with the most biological insight – to study the data and to look for patterns and structures, without needing to be a statistics or computer expert.
“By using the Qlucore Omics Explorer researchers at the Genomics Centre can unleash the full potential of their data,” says Carl-Johan Ivarsson, Qlucore’s president. “In fact, scientists working at King’s College can now perform advanced statistical analysis with just a few clicks of the mouse. With the unique combination of speed, power and flexibility that our software provides, we can help these researchers to examine more data, test exciting new theories and consider a wide range of possibilities.”
Qlucore Omics Explorer enables researchers to analyse and explore extremely large data sets (containing up to more than 100 million data samples) on a regular PC. The software can also take full advantage of all annotations and other links that are connected with the data being studied, as well as a number of powerful statistical functions such as false discovery rates (FDR) and p-values.
Qlucore’s software can be used to explore many different types of data, including gene expression micro array data, protein array data, miRNA data and RT-PCR data. The software can also be used to analyse protein data from 2-D gels, image analysis data, and in fact with any data set of multivariate data of sizes up to 1,000 samples and 100,000 variables, or 1,000 variables and 100,000 samples.