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Bioinformatics Services

Bioinformatics Services

We offer bioinformatics solutions for a wide range of research needs, from standardized pipelines to highly customized approaches. Our services include:

  • Standard workflows, which are well-established pipelines requiring well-defined input data formats and data quality.
  • Project-specific services, which are designed to provide solutions to specialized or complex research questions, and include custom script development, data visualization, and statistical analysis.

Standard workflows of omics datasets usually includes two main steps:

  1. Pre-processing, in which the raw data are subjected to quality control, filtering, normalization, and cleaning to ensure data integrity.
  2. Application-specific analyses, in which the pre-processed data are analyzed to address application-specific research questions.

Results are returned as user-friendly html reports.

Standard Workflows

We have standard workflows available for:

  • (bulk) RNA-seq
  • ChIP-seq
  • ATAC-seq
  • CUT & RUN
  • 10X single-cell RNA-seq
  • Whole-genome sequencing (WGS) and exome sequencing variant calling
  • Shotgun metagenomics analysis

If the analysis you are interested in is not listed, please contact us and we will try our best to accommodate your request.

Examples of the services we provide are listed below.

Pre-processing: 

  • Quality control
  • Quality-based read trimming and filtering
  • Mapping to a reference genome
  • Generation of a coverage track (bigWig file for visualization in genome browsers like IGV or UCSC Genome Browser)
  • Gene expression quantification. Library normalization

     

Application-specific analyses (as applicable): 

  • Sample correlation analysis
  • Principal component analysis (PCA)
  • Differential expression analysis (one simple comparison; replicates required)
  • Functional and Pathway enrichment analysis

     

Standard output includes:

  • QC report
  • Raw gene count table
  • Table with differentially expressed genes, expression fold changes, and P-values
  • Table with over-represented gene ontology (GO) terms and KEGG pathways
  • Visualizations: principal component analysis (PCA), expression heatmap, volcano plots
  • Bigwig for trace visualization on UCSC or IGV browsers (BAM files available upon request)rhältlich)

Pre-processing: 

  • Quality control
  • Mapping to a reference genome
  • Generation of a coverage track (bigWig file for visualization in genome browsers like IGV or UCSC Genome Browser)
  • Peak calling
  • Peak annotation
  • Library normalization

 

Application-specific analyses (as applicable): 

  • Sample correlation analysis
  • Principal component analysis (PCA)
  • Differential peak calling (one simple comparison; replicates required)
  • Motif analysis for differentially called peaks
  • Functional and Pathway enrichment analysis

 

Standard output includes:

  • QC report
  • Peak coordinates and annotation
  • Table with differential peaks, expression fold changes, and P-values (if applicable)
  • Table with over-represented gene ontology (GO) terms and KEGG pathways
  • Motif enrichment results
  • Visualizations: Variability between samples based on their peak profiles (PCA). Heatmaps showing protein binding intensity/accessibility at different genomic regions. Volcano plots.
  • Bigwig for trace visualization on UCSC or IGV browsers (BAM files available upon request)

Pre-processing: 

  • Read quality control
  • Quality-based read trimming and filtering
  • Demultiplexing
  • Mapping to a reference genome
  • Gene expression quantification in each cell
  • Cell quality control
  • Removal of cells classified as doublets or negative
  • Gene quality control
  • Filtering of genes that are only expressed in a small number of cells
  • Correction for ambient gene expression
  • Normalization
  • If necessary, correction of technical (e.g., depth) or biological covariates (e.g., cell cycle)
  • If necessary, correction of batch effects
  • Data integration (if applicable)
  • Expression recovery (denoising, imputation)

 

Application-specific analyses (as applicable): 

  • Feature selection, dimensionality reduction, and visualization
  • Cluster analysis
  • Cluster annotation
  • Compositional analysis

 

Standard output includes:

  • QC report
  • Violin and Scatter plots of features (genes), UMIs (transcripts), and mitochondrial genes counts
  • Transcript/cell count table
  • Dimensional reduction (UMAP or tSNE)
  • Clustering evaluation

Pre-processing: 

  • Read quality control
  • Quality-based read trimming and filtering
  • Mapping to a reference genome. Duplicate removal
  • Base quality score recalibration (BQSR)
  • Local realignment around indels

 

Application-specific analyses (as applicable): 

  • Single nucleotide polymorphism (SNP) and short insertion and deletion (indel) calling
  • Variant annotation (functional impact, population frequency, clinical significance, if relevant)
  • Variant filtering and prioritization (based on quality scores, frequency, and predicted impact)

 

Standard output includes:

  • QC report
  • Variant call statistics (number of SNPs and indels, number of transitions and transversions, etc)
  • VCF file with all called variants
  • VCF file with filtered variants
  • Variant annotation reports

Pre-processing: 

  • Read quality control
  • Quality-based read trimming and filtering
  • Metagenome assembly

 

Application-specific analyses (as applicable): 

  • Binning
  • Taxonomic assignment
  • Annotation

 

Standard output includes:

  • QC report
  • Community profile
  • Gene families abundance
  • Pathways abundance
  • Pathways coverage

Project-Specific Services

We are happy to work with you to provide analyses not included above. This includes constructing custom workflows, but also:

  • Adaptation and application of a variety of more custom or emerging analysis techniques. For example, for RNA-seq: Clustering on genes based on expression profiles; Weighted Gene Co-expression Network Analysis (WGCNA); visualization and analysis of gene-gene interactions using Cytoscape; motif analysis to identify transcription factors that may regulate differentially expressed genes; detection of alternative splicing events; quantification of splice isoforms; analysis of splicing regulatory motifs; gene ontology (GO) and pathway enrichment analysis with specific tools; visualization of gene expression on pathway maps; drug target identification.
  • Customized publication-ready figures and tables.
  • Design and implementation of custom pre-processing and application-specific analysis pipelines.
  • Integration of multi-omics data.
  • Statistical analysis to identify significant patterns and trends.
  • Machine learning for predictive modeling and pattern recognition.
  • Experimental design review and consulting.
  • NCBI GEO data cataloging and upload.
  • Grant proposal development; grant review and advisory services; grant application support.
  • Manuscript preparation support.
  • Doctoral, Master’s, and Bachelor’s students support and co-supervision.
  • Data documentation support.

Bioinformatics Data Processing and Analysis Costs

Pre-processing and application-specific analyses costs are based on the required human and computational resources, which in turn depend on the type of data, number of samples, and sequencing depth.

Each service includes:

  • A one-hour initial consultation and project planning meeting so we can understand specific project needs, outline a project schedule and estimate costs.
  • A one-hour follow-up meeting to discuss results and answer any follow-up questions.
  • A methods write-up for publication.
  • Snipets of the code used to generate the results for reproducibility.

Raw data generation

Note that our services do not include raw data collection, as well as sequencing. Please, contact the Core Unit Next Generation Sequencing if needed.

Data Management and Storage

Please note that routine data archiving and backup are not included within our standard bioinformatics services. Data uploaded to our servers and project-related data will be typically deleted three months after the completion of the project or the final consultation meeting, whichever comes later.

Example of costs

A typical RNA-seq project involving 12 RNA-seq libraries generated from human or mouse cells and tissues might be billed as follows:

  • Initial consultation and project planning meeting (Included)
  • 200 EUR project setup fee (data transfer and preparation, QC review, pipeline setup)
  • 960 EUR Pre-processing and application-specific analyses
  • Generation of 2 custom, publication ready figures (50 EUR per hour)
  • Follow up consultation meeting (Included)
  • 200 EUR for NCBI GEO data cataloging and upload; GEO access management (optional)

For a project-specific quote please get in touch with us.