Applying NGS and other high-density analytical technologies for efficient oncology biomarker identification requires integration and analysis of genomic, transcriptomic, epigenetic, and phenotypic data within a single high-throughput system.

Cancer as a disease is particularly diverse, as is the data derived from oncology studies. Different ways of acquiring and evaluating DNAseq data have developed with a range of approaches spanning from analyzing whole genomes, to only focusing on relevant, desired information in exomes or specific panel targeted re-sequencing of genes. Technologies also exist for gene regulation investigation with ChIP, and DNA methylation studies with sodium bisulfate. These approaches provide a wide variety of data with differing requirements for integration and analysis. Additionally, RNAseq information from mRNA and miRNA adds further diversity and complexity to the problem of integrating relevant oncology data and deriving knowledge.

Genedata Expressionist for Genomic Profiling allows the integration of oncology data from all sources for analysis to be performed in a single system.

Making sense of data has become the bottleneck in many projects as genomic data generation technologies mature. Primary analysis is typically performed by instruments generating data, such as machine statistics, production of sequence reads and quality scores.

More complex secondary analysis, quality control, filtering raw reads, alignment, assembly of reads, variant calling, and calculation of expression are usually not performed within the instruments technology platform. More sophisticated analysis platforms are required, with many platforms offering only secondary or tertiary analysis.

Tertiary analysis such as, multi sample processing, comparisons, quality control of variant calls, expression values, annotation and filtering of features, data normalization, are higher level analyses helping interpretation and conversion of data into knowledge. Making sense of large amounts of data is best suited to exploratory analysis using a genomic browser.

Genedata Expressionist for Genomic Profiling integrates secondary and tertiary NGS data analysis with clinical information, public and proprietary omics data, annotations, and pathway information to improve clinical study designs, drug efficacy, and drug life cycle management.

Cancers are extremely heterogeneous and are best viewed on an individual case-by-case basis. It is paramount when using a system for mining knowledge to have the flexibility to fluidly manipulate visualizations, to obtain at ease, different perspectives of data depending on your clinical question.

With an innovative and highly flexible genomic browser capable of visualizing terabytes of data in a multitude of ways, NGS and other high-throughput genomic data can be mined with ease in the hands of any scientist. A carefully selected set of statistical tests ranging from a simple t-test to sophisticated algorithms like edgeR and logistic regressions, linked to tight integration with pathway analysis tools provide oncologists and statisticians the right tools to extract the most relevant information from their experiments.

Genedata Expressionist for Genomic Profiling provides an innovative genomic browser to visualize massive amounts of interlinked data for full exploratory analysis to glean knowledge.

Genedata offers more than just a software platform. Our scientific consulting practice has benefited many companies and organizations. Genedata participates in and contributes to the success of many oncology consortia, including:

• EpiFemCare (FP7) is an ongoing oncogenomics project to develop a blood based diagnostic test for early detection of breast and ovarian cancer, based on epigenetic markers. Genedata will analyze NGS and array data to discover and validate tumor specific DNA methylation markers that can be detected in serum.

• carcinoGENOMICS (FP6) has developed in vitro methods for assessing the carcinogenic potential of compounds. Genedata has assessed assay reproducibility and developed cancer prediction models.

• NewGeneris (FP6) investigated the role of prenatal and early life exposure to genotoxic chemicals present in food and the environment in the development of childhood cancer and immune disorders. Genedata used multivariate statistics in order to analyze the effects of such exposure on gene expression.

• INCA (FP6) investigated the role of chronic viral infections in the development of cancer. Genedata performed quality control and pre-processing of omics data, and conducted the overall statistical data analysis.

For inquires about consultation services from Genedata Expressionist please contact us.