The exponential increase in the amount of data generated since the development of microarrays and high-throughput sequencing has led to a new discipline for the analysis of genomic and genetic data, namely bioinformatics. We now require special skills and hardware to be able to extract the most pertinent and biologically relevant information. DNAVision has therefore set up an interdisciplinary team of biologists, bioinformaticians and engineers to read, manipulate, analyze and visualize this kind of data.
We have servers dedicated to our analysis pipeline and also a 2000 processing unit cluster.
Bioinformatics is defined as the application of techniques used in informatics and statistics to biological data. At DNAVision, we focus our attention on four aspects of this discipline.
- Quality Analysis: This step is crucial as it means we can have confidence in the results obtained in subsequent analyses, excluding the risk that final conclusions result from poor quality biases in the data.
- Biostatisticsis the application of computational and statistical techniques to biology, in order to give statistical credibility to data: It shows to what extent an observation is the result of experimental design and not a consequence of chance.
- Data Mining aims to further analyze the biostatistical results (for example, differentially expressed genes, single nucleotide polymorphisms, portions of chromosomes showing copy number variations), in order to extract the most relevant biological information (inference of pathways, gene ontology, functional annotation,...).
- Genome Analysis and Annotation: Through strong partnerships, we provide tailored automated genome annotation tools for prokaryotic and eukaryotic genomes.
In addition, we build on our skills to develop algorithms and tools for other areas such as custom chip design. We are also able to set up customized analysis algorithms to meet our customers' specific issues. Finally, the DNAVision bioinformatics team is also available to discuss any matter related to experimental design, including randomization of sample processing.