Technologies such as gene microarrays, protein microarrays and RNA-seq generate large amounts of expression data. Once generated, doesn’t it make sense to try and derive the maximum value out of the data? Simplistic approaches such as creating gene lists in Microsoft Excel and sorting by uncorrected p-values don’t allow you to gain a overarching sense of the biological patterns in the data. In fact, differentially regulated gene lists are often as confusing and uninterpretable as the raw data.

The Entagen team has decades of experience in gaining deeper insight into large-scale expression data. We know how to assess its quality. We know how to properly normalize it. We look out for experimental variation that can mask the biological effects a researcher is hoping to study in the data. We also know how to integrate expression data with other types of biologically relevant information, such as cellular pathway maps and functional classifications in order to gain a deeper understanding of what the results mean from a biological perspective.

Our computational biology and data analysis services include:

• Microarray data quality assessment and robust normalization using RMA & GC-RMA
  • Functional analysis of gene expression patterns with tools such as GOMiner & GSEA
  • Systems biology modeling & biological pathway perturbation analysis
  • Toxicogenomics pattern recognition and assessments
  • Biomarker identification & monitoring
  • ArrayTrack usage for microarray data submissions
  • Data preparation for Voluntary Genomics Data Submissions (VGDS)
  • Next-gen sequence data management and assembly
  • SNPChip and ChIP-Chip analysis