Bioinformatics Unit

The Bioinformatics Unit provides computational support for the analysis and interpretation of high-throughput biological data. The unit specializes in transcriptomics, single-cell omics, proteomics, and network biology, combining established analytical methodologies with the development of novel computational approaches.

Its activities support research projects across the life sciences by enabling the extraction of biologically meaningful insights from complex omics datasets.

Beyond conventional bioinformatics analyses, the unit develops specialized computational tools for:

Literature-based knowledge discovery
Biological network analysis
Biomarker prioritization
Comparative genomics

Core Analytical Capabilities

1. Transcriptomics Analysis (RNA-seq)

The unit provides comprehensive analysis of bulk RNA sequencing (RNA-seq) datasets, including:

Sequencing data quality control and preprocessing
Gene and transcript quantification
Differential gene expression analysis
Functional enrichment and pathway analysis
Biological interpretation of transcriptomic profiles

These analyses enable the identification of gene expression changes associated with biological processes, disease states, or experimental interventions.

2. Single-Cell Transcriptomics (scRNA-seq)

The unit provides computational analysis of single-cell RNA sequencing (scRNA-seq) data, including:

Quality control and filtering of single-cell datasets
Data normalization and exploratory analysis
Cell clustering and identification of cell types and populations
Gene correlation analysis and marker discovery
Functional enrichment analysis of associated genes

Advanced exploratory analysis is supported through scRNA-Explorer, a web-based platform developed by the research team for interactive exploration of single-cell datasets, as well as through customized analytical pipelines.

3. Proteomics Data Analysis

The unit supports the analysis of mass spectrometry-based proteomics datasets, including:

Preprocessing and statistical analysis of protein abundance data
Differential protein expression analysis
Data visualization and presentation
Functional enrichment and pathway analysis

These analyses are supported by ProteoSign v2, a web-based platform developed by the research team for differential expression analysis of proteomics datasets.

4. Knowledge-Based Bioinformatics and Literature Mining

The unit develops and applies computational approaches that integrate experimental data with knowledge extracted from the biomedical literature.

Protein–Protein Interaction Discovery

Using the UniReD platform, the unit identifies potential functional associations between proteins based on evidence extracted from the biomedical literature and clustering of related proteins.

This approach enables the discovery of functional network relationships beyond curated protein interaction databases.

5. Comparative Genomics and Evolutionary Bioinformatics

The unit supports comparative genomic and proteomic analyses from an evolutionary perspective.

These analyses aim to:

Identify conserved genes
Infer functional relationships
Investigate the evolutionary mechanisms shaping biological systems across species

6. Emerging Analytical Capabilities

The research team is expanding its analytical expertise into spatial omics, with an initial focus on:

Analysis of spatially resolved transcriptomics data
Spatial differential expression analysis
Cell neighborhood and cell–cell interaction analysis
Integration of spatial omics with single-cell datasets

These capabilities will complement the unit’s existing expertise in single-cell transcriptomics and integrated biological network analysis.