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Home » Bioinformatic Analysis

Bioinformatic Analysis

Bioinformatic Analysis
Analyzing and interpreting genomics data

Gene Logic’s Bioinformatics team has helped many clients to uncover biological insights from their data. We have extensive experience in analyzing genomics data, whether for pre clinical research & development or data generated from clinical trials.

We offer a wide range of statistical and bioinformatic analyses of microarray data, ranging from basic quality control and differential gene expression analysis to extended pathway and mechanistic studies.

We provide three levels of analysis: basic, advanced and custom.

Basic Expression Array Analysis is included with microarray processing and consists of the following:

  • RNA and Microarray Quality Control data
  • MAS 5.0 output of the GCOS Server
  • Functional Gene Annotations
  • Summary statistics for each treatment group
  • Pair-wise comparisons of principal interest using fold change and t-test derived p-values
Advanced Expression Array Analysis is optional on a per study basis. The analysis is performed such that an experienced biostatistician specializing in microarray data analysis actively interrogates the data and provides robust and valid measures of gene regulation, principal data trends, and outlier analysis. In addition to MAS 5.0, alternative methods for data normalization and summarization, including RMA and GCRMA are available. The standard Gene Logic RNA and Microarray Quality Control analysis is included. Additional components might include:

  • Clustering Analysis (Hierarchical, K-means, Self Organized Maps, etc.)
  • Gene Ontology and Pathway Enrichment
  • Additional sample and comparison visualizations (scatter plots, histograms, Volcano plots, Principal Component Analysis and more)
Custom Expression Array Analysis is designed in consultation with our client partner. The focus is on enhanced understanding of the biological implications of their study. Custom analysis may include:

  • Advanced Biological Interpretation
  • Comparisons of study results with Gene Logic’s Reference Databases
  • Predictive Modeling