Transcriptomics

RNA Sequencing (Oxford Nanopore Technologies®)

RNA sequencing is the best way to explore the whole transcriptome of a pool of cells. At Probiomics, we offer the third generation of sequencing provided by Oxford Nanopore Technologies (ONT). Unlike traditional RNA-seq techniques, ONT long reads sequencing allows accurate quantification and characterisation of full-length transcripts, which is crucial to unambiguously identify splice variants and fusion transcripts (for more information).
Starting from 100k cells or 500ng of total RNA, we offer a complete workflow producing ready-to-use material for publication.

Targeted Transcriptomics (Fluidigm Biomark®)

If you are only interested in evaluating the expression of a restricted set of genes given a limited number of cells, then our solution for you is Fluidigm technology. Using a microfluidic approach, Fluidigm Biomark allows performing over 9000 real-time PCR (qPCR) reactions per run allowing the simultaneous analysis of 96 genes (for more information).

Genomics

We offer complete Whole Genome Sequencing, starting from either DNA or cell samples, to produce raw data and clear Genome-Wide Association Study (GWAS) reports. Whole genome sequencing provides an extensive overview of single nucleotide variant (SNV) and structural variation (SV). The long reads of Oxford Nanopore Technologies resolve areas of the genome that have proven intractable to short-read sequencing, including telomeres, centromeres, and highly variable regions.

Epigenomics

Base modifications are important epigenetic mechanisms that regulate gene expression. Oxford Nanopore sequencing is the only technology that directly identifies base modifications at single nucleotide resolution without any chemical steps, such as bisulfite conversion. This benchmark shows how this technology performs better than the widely used bisulfite strategy. Using just 1ug of DNA or 500K cells, we are able to provide a whole genome DNA methylation profile, investigating differentially methylated sites and their enrichment for DNA-binding proteins. To date, we can detect 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), and N6-methylcytosine (6mA), recently discovered to also be present in the human genome.

Cell frequencies

Through Flow Cytometry we are able to quantify (CytoFLEX) and sort (BD FACSAria IIcs) different types of cells in a biological sample using membrane and intracellular proteins as markers. FACS-based sorting can be coupled with transcriptome analysis performed using a targeted (Fluidigm, Biomark ®) or untargeted (RNA-seq) approach.

Raw data analysis and quality check

Each platform produces raw data that need to be processed, normalised, and checked before any type of downstream analysis. For example, regarding RNA sequencing, this step consists of basecalling, reads quality check and filtering, alignment against reference transcriptome, reads count, and normalisation.

Exploratory data analysis

Dimensionality reduction techniques, such as PCA, T-SNE, UMAP, MOFA, or unsupervised hierarchical clustering strategies are commonly applied to get an overview of large biological datasets. These approaches allow to highlight sample similarities driven by the whole data set(s).

Statistical analysis

The main objective of any experimental project is to summarise, describe, and visualise the data, measure relationships within the data, and test hypotheses using statistical tests. Our pipelines are designed to offer a reproducible workflow and are able to choose the right test depending on the number of comparisons, the normality, and the homogeneity of the data distributions.

Functional analysis

Hypothesis tests applied to omics data generally produce a list of features that are significantly different between conditions. These features can be, for example, transcripts, proteins, and methylation sites. Functional analyses provide an overview of the biological roles of these deregulated features. Depending on the data type and the scientific questions, we perform gene ontology (GO), Gene Set Enrichment Analysis (GSEA), and Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER), for instance, useful to infer the transcription factors' activity starting from RNA-seq data.

Multi-omics data integration

A single omics layer is a wealth of information, but by integrating information from multiple layers, researchers gain an unprecedented depth of insight into the intricate web of interactions within biological systems. Simplifying, this type of analysis consists of building a network where each node is a feature of any omics layer and the nodes are connected using both public information and relationships that came out from the data.

Artificial Intelligence models development

We provide the possibility to build supervised, and semi-supervised Artificial Intelligence models to predict any feature you are interested in, given the dataset you choose to provide to us. One example is using as the predictors metrics from multi-omics profiles, and as the feature to predict the responsiveness or the adverse effects after inoculation of specific therapies. Predictive models can be trained to determine which outcome is expected, and what individual characteristics promote one outcome instead of another. This kind of approach is powerful to discern whether a specific therapeutic strategy will be effective on a specific individual, and also for the discovery of biomarkers that are informative for the discrimination between biological conditions.

Mathematical Modelling

Systems biology has evolved during the last decades to provide progressively more insightful mechanistic models of biological systems. Complex hypotheses on system behaviour can be tested through the development of a mathematical model, merging previously defined knowledge with novel insights given by the most recent experiments. We offer the opportunity to test the rules you think dictate the relationship between the elements of your biological system (hypotheses) to determine to what degree they explain the observed data. This process is subdivided into (i) translation of your hypotheses in mathematical form to build the mathematical model, and (ii) model fitting and validation against your experimental data.