Data Analysis

Our team offers various options for information and advisory cooperation in the field of biomedical data analysis. We provide assistance in the analysis of big biological and medical data, their visualization and interpretation. We work with data of any complexity (including unstructured data, gaped and noisied data), an arsenal of analytical tools – from standard statistical methods to machine learning algorithms, visualization – in all its diversity.

We perform the following types of analysis:

• Standard statistical data analysis
• Genomics
  • Identification of polymorphic sites by targeted exome sequencing
  • Identification of polymorphic sites by whole exome sequencing
  • Identification of polymorphic sites based on whole genome sequencing data
• Transcriptomics
  • Identification and analysis of small capped RNAs
  • Mammalian RNA polymerase II rate evaluation
  • Identification and annotation of expressed genes
  • Differential gene expression analysis
  • Identification of RNA-binding protein sites in different RNA classes
  • De novo transcriptome assembly
  • Assembling a transcriptome based on reference annotations
  • Identification and annotation of retained introns
  • Differential RNA splicing analysis
  • Identification and annotation of open reading frames in full-length RNA molecules
  • Reconstruction, topological and functional analysis of gene regulatory networks
  • Univariate analysis of patient survival based on transcriptome data
  • Multivariate analysis of patient survival based on transcriptome data
  • Building classification and predictive models based on meta-classifiers and transcriptome data
• Epigenomics
  • Identification of open chromatin regions
  • Identification of post-translational modifications of histone proteins
• Proteomics
  • Protein differential expression analysis
  • Proteomic data functional annotation
• Graphical data visualization