Bioinformatics data analyses are divided into 3 tiers:
- Standard analyses are based on pipelines the group has written and actively maintains
- Advanced analyses are based on existent workflows which can be adapted to the needs of collaborators
- Custom analyses are specifically developed for the collaborator and do not fall in the above categories
We mostly use generalised linear models, based on negative binomial distributions. We manage experimental designs with different levels of complexity, including nested, paired, factorial and continuous variable models.
We provide support in defining the most appropriate experimental design to study the biological question of interest.
Data files will be delivered to collaborators via courier, hard disk or file sharing system. Human data will be encrypted and description keys will be sent through certified mail (PEC).
Standard bioinformatics analyses are executed through standard pipelines, and are managed by an experienced bioinformatician. The analysis is concluded with a report, including metrics on data quality, and a list of results, depending on the type of data:
- Genomic analysis: lists of variants and their annotation using the most common databases (e.g. dbSNP, ExAC, ClinVar)
- Transcriptomic analysis: gene expression levels, lists of differentially expressed genes across different conditions and corresponding enriched pathways and ontologies
- Single-cell transcriptomic analysis: single cell clustering, determination of cluster-specific markers, canonic correlation analysis across samples
- Epigenomic analysis: lists of enriched regions and their annotation, determination of differentially enriched regions
- Microbiome analysis via 16S DNA sequencing: determination of taxonomic abundances (levels L2-L7), differential analysis, computation of alpha and beta diversity
The pipeline will be modified to run analyses tailored on specific experimental design.
Genomic data (WGS and WES)
- Segregation analysis based on pedigrees and hereditariness models
- Prediction of the effects of mutations in coding regions
- Phenotype-driven prioritisation of genes in specific diseases
- In the case of tumour samples:
- Analysis of clonal populations and clonal evolution;
- Analysis of tumoural profiles;
- Tumour Mutational Burden calculation
- Data integration from multiple data sources (e.g. ChIP-seq and RNA-seq), based on evidence of biological network or inferred from statistical models and quantitative data
- Alternative splicing analysis
- Detection and characterisation of circular RNAs
- Detection of fusion transcripts Single-cell transcriptomic data
- RNA velocity
- Diffusion maps
- Motif analysis on sequences deriving from enriched regions
Any kind of bioinformatics analysis not included above will be discussed in detail with the collaborator. A formal offer will follow.
- Standard: €1050.00
- Advanced: €2100.00
- Custom: €210.00 per working day