BIOINFORMATICS
SERVICES
Bioinformatics is key to making sense of complex omics datasets, and we approach this work with both technical rigor and genuine scientific curiosity. Our team brings experience in advanced analytical methods and careful data processing across multiple omics domains, always striving to apply the most appropriate and reliable approaches for each type of dataset. Rather than relying on a single pipeline, we evaluate the strengths and limitations of each method to ensure results that are scientifically sound, reproducible, and thoughtfully interpreted.
The Value We Bring to Your Project
We provide flexible analyses with customized budgets, fully tailored to your project
Each delivery includes detailed, interactive reports where every figure comes with a clear and accessible interpretation of your data.
You can explore and modify your figures through a simple, interactive tool regarding your analitical objeticves
From start to finish, you’re supported through meetings, emails, and calls, with guidance to help you interpret every result with confidence.
Bioinformatics Services
We offer a wide range of bioinformatics services
Genomics (DNA)
Transcriptomics (Bulk RNA-seq, scRNA-seq)
Proteomics (DDA, DIA)
Metabolomics (Metabolites)
Multi-omics Analysis (Integration)
Transcriptomics
Bulk RNA-seq
Proteomics
Preprocessing
From raw data
From quantified matrices:
- Missing data analysis
- Imputation
- Normalization
Differential abundance analysis
Group comparisons
Plots: volcano plot
Clustering analysis
Algorithms:
- Unsupervised
- Supervised
Functional analysis
Different methods
- ORA, GSEA
Different databases
- GO, Wikipathways
Other Analyses
Co-expression Network Analysis
Identification of biomarkers by feature selection
Clustering for patient subgroup detection
Search for robust biomarkers
Transcriptomics scRNA-seq
Preprocessing
From FASTQ files:
- Alignment
- UMI correction
- Cell calling
- Ambient RNA cleanup
From quantified matrices:
- Quality control and filtering
- Doublet removal
- Scaling and normalization
Clustering and cell type annotation
Clustering:
- Visualization (UMAP)
Annotation:
- Manual (markers)
- Automatic (reference)
Differential expression analysis
Group comparisons
Plots: volcano plot
Functional analysis
Different methods
- ORA, GSEA
Different databases
- GO, Wikipathways
Multi-omics Analysis
Selection of the appropriate analysis method according to objectives, experimental design, and available omics data
Processing and individual exploration of omics data types prior to integration in the analysis
Omics data integration analysis to identify biomarkers, integrated multi-omics signatures, and classification models
Functional characterization to contextualize the integration results, identifying relevant biological pathways and functions
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Some of our clients
If you are a researcher and need support with any statistical task, get in touch and we will find the best solution for your needs