Services

A fragmentomics study is only as good as its design. We go through your clinical question, sample types, pre-analytical handling and group sizes, and recommend controls and sequencing depth so the data can answer the question you are asking.

Cell-free RNA is short and partly degraded, so standard RNA-seq QC misses things. We align and deduplicate reads, check library complexity, contamination and batch effects, and flag samples that should not go into the analysis.

We describe each sample by how its RNA is fragmented: the distribution of fragment lengths, where fragments start and end along each transcript, and the sequence motifs at the fragment ends – alongside the conventional quantitative expression values.

We look for fragment features that separate your groups – responders and non-responders, disease and control, before and after treatment – and build classifiers that combine them with clinical variables. Models are validated with resampling and, where available, an independent cohort.

You receive the results in a form you can use: figures and tables for publication or regulatory documentation, a methods description, and, if you want to run future studies in-house, the analysis pipeline with documentation and training for your team.

We evaluate data integrity, detect outliers and batch effects, and perform read alignment, gene quantification, normalisation and batch-effect correction, so your data is reliable and ready for downstream analysis.

We help you prepare data for Scarfweb from Nygen Analytics, get your team started, and offer training so you can explore single-cell data on your own.

We support you in using the curated single-cell database Cellhub, hosted by Nygen Analytics: curating your data and comparing it with deposited data sets.

Using dimensionality reduction such as t-SNE, UMAP and PCA, we visualise and cluster cells by their expression profiles. Interactive scatter plots and heat maps let you explore the clusters and the biology behind them.

We annotate cell types using established marker genes, reference databases such as CellMarker and automated methods such as CellTypist, giving you a clear picture of the cellular composition of each sample.

We identify genes uniquely up- or down-regulated in specific cell clusters or conditions, pinpointing regulatory genes and pathways involved in cell function, disease mechanisms and treatment response.

With Gene Set Enrichment Analysis and pathway analysis we identify biological pathways and gene sets associated with specific cell populations or conditions, and explain what the differentially expressed genes mean functionally.

We link differentially expressed genes to biological functions and pathways using databases such as KEGG, Reactome and GO, and interpret your data in the context of known biology.

Using CellPhoneDB and ligand–receptor correlation analysis we identify likely signalling interactions between cell types, showing how cells coordinate within the tissue.

We identify ligand–receptor pairs and combine them with expression data and known databases to infer the communication pathways that regulate tissue homeostasis and cellular processes.

With algorithms such as PAGA and scFates we reconstruct cellular trajectories and identify branching points, revealing the regulatory events that guide cell fate decisions.

By analysing the ratio of spliced to unspliced mRNA we infer where individual cells are heading, predicting differentiation paths and dynamic changes in gene expression.

We integrate scRNA-seq data from multiple samples or conditions, for example with the scArches pipeline, to find shared and distinct cell types and condition-specific expression patterns.

We combine spatial information with expression data to show how gene expression varies across tissue regions and cell types, highlighting spatially regulated genes and interactions in the tissue microenvironment.

We map molecular data onto tissue sections while preserving spatial relationships, and combine several omics layers to understand molecular interactions in place – tailored to cancer research, neuroscience, developmental biology or other fields.

We analyse data from several omics disciplines together to reveal interactions and regulatory mechanisms that no single data type shows on its own.

Using DrugBank, DGIdb and methods such as Drug2cell we relate drug–target interactions to expression profiles, helping you find new targets and understand how existing drugs act on cellular pathways.

With generative models such as the Compositional Perturbation Autoencoder (CPA) and GEARS we predict how cells will respond to perturbations and conditions that were not measured.

We assess assay performance – limit of blank, detection and quantification, PCR efficiency, dynamic range and precision – with confidence intervals, following current recommendations for qPCR and dPCR validation, including in support of cell and gene therapy.

We select and validate the most stable reference genes for your samples using NormFinder and geNorm, so your normalisation holds up to review.

We take your raw data through preprocessing and statistical analysis – t-tests, ANOVA, PCA, clustering, classification and time-course analysis – and report results that meet the MIQE guidelines.

We help design and analyse single-cell RT-qPCR studies, from handling missing data and technical noise to identifying subpopulations.

For labs working under GLP or GMP we set up analysis workflows with traceability and secure data handling, built on GenEx GxP, and help you document them for audits.

Free technical support for our software, including installation, licence activation and licence transfer.

Get help applying the methods to your own data, confidentially and online. A typical case takes between 30 minutes and 2 hours and is invoiced per started 30-minute interval.

We put together a course that fits your team – from qPCR data analysis and GenEx to multivariate statistics and single-cell methods – held on site or online.