Bioinformatics Group, Institute of Molecular Biology NAS RA

We have considerable experience in systems biology analysis of high-throughput data, including enrichment/overreperesntation analysis for pathways and functional gene sets, as well as advanced pathway analysis, machine learning approaches and others. If none of the more specialized services correspond to the type of systems biology analyses you'd like to undertake, contact us through this one.

We provide a wide range of biostatistics and bioinformatics data analysis pipelines. For details, see the available specialized services. If none are relevant, you may contact us through this service.

Brainstorming/miscellaneous requests

This service is for the cases where:

• You have non-standard data you’d like to analyze
  
• You have standard data you’d like to analyze in a non-standard manner
  
• You don’t really understand what to do with the data
  
• Any other cases requiring non-standard or creative approach
  

• Pathway and network based analysis
  
• NGS data analysis
  
• Computational research of telomere biology
  
• Integrative analysis of transcriptomic, proteomic and metabolomic data
  
• Population genetics studies

Biostatistics/Machine-learning

• Basic and advanced descriptive statistics and visualization
  
• Hypothesis testing analysis
  
• Correlation/Regression analysis
  
• Application of self organizing maps algorithm for high-dimensional gene expression data processing and feature identification
  
• PCA and clustering analysis
  

Input: Any spreadsheet type file with data.

Output: Results are presented in graphical format with clear, comprehensive and aesthetic figures and schemes, as well as in tabular and text formats. All the results are explained in detail, with the option of further discussions and post-feedback modifications.

• RNA-seq data processing
  
• sequencing data quality control
  
• read alignment
  
• gene- and transcript-based quantitation, TPM/RPKM/FPKM-based quantitation
  
• Identification of alternatively spliced variants
  
• Identification of differentially expressed genes
  
• Overrepresentation and gene set enrichment analysis
  
• PCA analysis
  
• Hierarchical clustering/heatmap rendering
  
• Advanced pathway analysis (described in details in a separate service)
  

Input: Fastq or BAM raw data files or already processed RPKM/TPM/FPKM files.

Output: Results are presented in graphical format, with clear, comprehensive and esthetic figures and schemes, as well as in tabular and text formats. All the results are explained in detail, with the option of further discussions and post-feedback modifications.

• Microarray raw data processing (from CEL and TXT files to gene expression matrixes)
  
• Identification of differentially expressed genes
  
• Overrepresentation and gene set enrichment analysis
  
• PCA analysis
  
• Hierarchical clustering/heatmap rendering
  
• Advanced pathway analysis (described in details in a separate service)
  

Input: CEL and TXT formatted raw data files or Gene Expression Omnibus dataset file formats

Output: Results are presented in graphical format, with clear, comprehensive and aesthetic figures and schemes, as well as in tabular and text formats. All the results are explained in detail, with the option of further discussions and post-feedback modifications.

Advanced pathway analysis

• RNA-seq raw data processing
  
• Mircroarray raw data processing
  
• Identification of differentially expressed genes
  
• Overreprestation and gene set enrichment analysis
  
• Pathway signal flow analysis: this is our in-house algorithm for quantitative assessment of pathway activities based on gene expression/metabolite abundance/proteomics data, as well as pathway topology.
  
• Integrative analysis of metabolomic and transcriptomic/proteomic data.
  
• Identification of dysregulated pathways in the studied condition.
  
• Identification of samples with similar profiles of pathway dysregulation.
  
• Construction of custom pathways and further analysis based on particular biological question.
  

Input: Gene expression matrix (or fold-change values) files in a wide variety of formats (.txt, .csv, .xls, .xlsx). Or no additional input is required, if this is a downstream analysis of microarray and RNA-seq data analysis services.

Output: Results are presented in graphical format, with clear, comprehensive and aesthetic figures and schemes, as well as .gif files showing pathway signal flow in certain cases. Additionally, tabular and text formats will also be delivered. All the results are explained in detail, with the option of further discussions and post-feedback modifications.

• Pathway signal flow analysis: this is our in-house algorithm for quantitative assessment of pathway activities based on gene expression/metabolite abundance/proteomics data, as well as pathway topology.
  
• Integrative analysis of metabolomic and transcriptomic/proteomic data.
  
• Identification of dysregulated pathways in the studied condition.
  
• Identification of samples with similar profiles of pathway dysregulation.
  
• Construction of custom pathways and further analysis based on particular biological question.
  

Input: Gene expression matrix (fold-change values) files in a wide variety of formats (.txt, .csv, .xls, .xlsx). Or no additional input is required, if this is a downstream analysis of microarray and RNA-seq data analysis services.

Output: Results are presented in graphical format, with clear, comprehensive and esthetic figures and schemes, as well as .gif files showing pathway signal flow in certain cases. Additionally, tabular and text formats will also be delivered. All the results are explained in detail, with the option of further discussions and post-feedback modifications.

Computational analysis of telomere biology

• Computation of mean telomere length from Illumina whole genome sequencing data
  
• Correlation analysis of mean telomere length with transcriptome and epigenome features
  
• Computational assessment of telomerase and ALT based telomere maintenance/lengthening mechanisms from gene expression data
  

Input: Please contact us for data type and format requirements.

Output: Depending on the input data type, computation of mean telomere length (whole genome) or telomere maintenance mechanisms (gene expression data), or association analysis with -omics data from different sources (epigenomics) is possible. Results are presented in graphical format, with clear, comprehensive and aesthetic figures and schemes, as well as .gif files showing telomere maintenance mechanisms’ activity. Additionally, tabular and text formats will also be delivered. All the results are explained in detail, with the option of further discussions and post-feedback modifications.

• Sequencing data quality control
  
• Read alignment
  
• Variant calling
  
• Genome wide association studies
  

Input: fastq or BAM files and phenotype data (optional).

Output: Results are presented in graphical format with clear, comprehensive and aesthetic figures and schemes, as well as in tabular and text formats. All the results are explained in detail, with the option of further discussions and post-feedback modifications.

• Chip-seq data processing
  
• ChIP-seq quality control
  
• Read mapping
  
• Peak calling
  
• Genomic region annotation
  
• Bisulfite-seq data processing
  
• Bisulfite-seq quality control
  
• Bisulfite mapping
  
• Methylation state identification
  
• Genomic region annotation
  

Input: fastq or BAM files.

Output: For ChIP-seq datasets, the output is details of detected peaks, including genomic position and width of the peak, the summit and the peak score. For BS-seq, the output is positions of all sequenced cytosines (in CpG, CHG and CHH contexts) along with the methylation score. All the results are explained in detail, with the option of further discussions and post-feedback modifications.

• Java based desktop apps
  
• Cytoscape apps
  
• Bash scripts and R Pipelines for NGS data analysis and visualization
  
• R pipelines for transcriptomic, proteomic and metabolomic data analysis