Unleash the full potential of your transcriptomics data

Transcriptome analysis allows characterization of all transcriptional activity, without prior assumptions. However, interpreting transcriptomics data to obtain useful biological insights can be challenging.

We strive to analyse your dataset holistically, starting from a broad perspective to very deep analytics to decipher the biology behind your transcriptomics datasets.

We employ a broad range of bioinformatic tools and software for data analysis:

• Data Preprocessing by Partek Genomics Suite
• PCA Plot
• Stacked Bar Charts for DEG analysis and Identification
• Manhattan Plot
• Volcano Plot
• Heatmap and Clustergrams
• Correlation Heatmap and Correlation Matrices for comparing categorical variables
• EDGE and SOM analysis for time-course studies
• WGCNA to evaluate gene modules that correlate with biology phenotypes

Uncover the gene network and pathways involved in your data

Pathway Enrichment Analysis with Enrichr and GSEA preranked analysis.
• WikiPathways
• GO-MF
• GO-CC
• GO-BP
• Reactome
• Gene set libraries curated in Enrichr
• Customised gene sets (eg BTMs)
• In-house databases related to infectious diseases

Gene interaction and networks
• Pathway and gene association networks by Ingenuity Pathway Analysis
• Gene-gene interaction analysis by Cytoscape
• Gene interaction networks by STRING