Applied bioinformatics and omics data analysis

Bioinformatic analysis of omics data is needed when the count or abundance table the laboratory delivers is not the answer but the starting point. It applies when a study generates gene expression or microbiome data and the research question requires turning that signal into a biological conclusion connected to the rest of the study. The sequencing centre may deliver raw files, count tables or abundance matrices; the service turns that material into a statistical analysis and a biological interpretation tied to the study question. Without that phase, the omics data usually remain a laboratory result that is never analysed, disconnected from the clinical or dietary variables that give the study its meaning. The analysis is what closes the gap between sequence and evidence.

The analysis can be taken over at different points in the process, but the entry point has to be defined according to the omics layer and the material available. For 16S microbiome data, that means starting from the raw FASTQ files, with quality control, filtering, denoising and ASV generation, or from an abundance table that has already been generated. For RNA-seq, the usual entry point is the count table or quantifications already generated; if the work starts from raw FASTQ files, the alignment, quantification and quality control steps included should be scoped explicitly. The data do not need to arrive processed: if they arrive raw, the scoped processing is part of the work; if they arrive as a matrix, the analysis starts from there. What does matter is that each sample links correctly to its clinical metadata, because that link is what later allows the omics layer to be connected to the rest of the study.

RNA-seq differential expression is analysed with Bioconductor, a reference ecosystem widely recognised in biomedical publishing. Starting from the count table, quality control, filtering and normalisation are applied, and differential expression is modelled with tools such as edgeR, DESeq2 or limma-voom, depending on the structure of the experiment. Differentially expressed genes are identified with correction for multiple comparisons, usually by controlling the false discovery rate (FDR), which is essential when thousands of genes are tested at once. The analysis does not end with the gene list: functional enrichment against reference databases places those genes in biological pathways, which makes it possible to formulate interpretable biological hypotheses without turning an omics association into mechanistic proof.

Microbiome analysis covers 16S amplicon sequencing data, from sequence processing through to association with the study variables. It applies when a project characterises the microbial community and seeks to relate it to a clinical condition, an intervention or a dietary pattern. Processing starts from the raw sequences, with quality control, denoising and generation of ASVs (amplicon sequence variants), and continues with the analysis of alpha and beta diversity and of differential abundance between groups. Differential abundance is interpreted taking into account the compositional nature of microbiome data. The part that adds value is the association of the microbial profiles with the study's clinical and dietary variables. 16S sequencing can characterise taxonomic profiles, but it does not always resolve reliably to species level, nor does it demonstrate metabolic function directly; those questions usually require other approaches, such as shotgun metagenomics or metabolomics, which are outside the scope of this service.

No. A list of statistically significant genes or taxa is an intermediate step, not a conclusion. This applies to the common situation in which an omics analysis stops at the table of significant features without connecting it to the biology or to the study question. The work that turns that list into a result has two parts: functional enrichment, which groups the features into biological pathways and shows which processes are altered, and biomedical interpretation, which weighs what biological hypothesis a gene, a taxon, a functional pathway or an associated marker might suggest, grounded in training in clinical biochemistry and immunology. That reading is what separates an interpretable, defensible result from a table of p-values that no one knows how to connect to the clinic.

The omics layer is integrated by linking the molecular signal to the clinical variables, the biomarkers and the questionnaire data within a single analytical framework, rather than treating it as an isolated appendix. This applies whenever a study has, in addition to the sequencing, clinical or phenotype data, which are what give the question its meaning. Multivariate analysis techniques are applied to high-dimensional omics matrices to relate the molecular profile to the other variables: which microbial profile is associated with a clinical course, or which expression pattern relates to a phenotype, adjusting for relevant covariates where appropriate. The integration must take into account relevant clinical covariates, possible batch effects and the exact match between each omics sample and its clinical metadata. This is biostatistics applied to omics data, not just running a pipeline. It is the point at which the study answers its question, instead of piling up two parallel analyses that never meet.

Applied bioinformatics and omics data analysis fits when the starting point is a sequencing experiment (RNA-seq, 16S microbiome) with a very large number of variables and specific needs for processing, normalisation and false discovery control at that scale. Multivariate and biomarker analysis, by contrast, starts from a bounded set of candidate variables, such as clinical or laboratory variables or a targeted panel, to identify and validate a panel with diagnostic or prognostic performance. The two can be combined in the same project, for example with an omics screen that feeds a reduced panel which is then validated. They do not, however, answer the same question: this service explores a high-dimensional omics layer (for example RNA-seq or 16S microbiome) from sequencing data; biomarker analysis validates a candidate panel methodologically for research, publication or preliminary clinical decision-making.

No. The service focuses on differential expression (RNA-seq), 16S microbiome, functional enrichment and multivariate analysis of omics matrices, using the Bioconductor ecosystem and statistical integration with the rest of the study. It does not cover variant calling for clinical, accredited or regulatory purposes, or pipelines that require dedicated infrastructure or regulatory accreditation, which are a different competence with their own framework. Nor does it cover, as a line of its own, mass spectrometry proteomics or metabolomics, or single-cell techniques, which rest on different statistical paradigms. Describing the scope precisely is deliberate: the service delivers what we can do with demonstrable methodological competence, not an expansive list of omics techniques outside its scope. If you need one of those lines, you will find a clear boundary here, not a vague promise.