Where raw reads are provided, quality control steps can be performed followed by read alignment and  quantification. Differential gene expression analysis is typically performed using DESeq2 (although other methods can be used if preferred).

Ribosomal profiling (or Ribo-seq) is used to capture mRNA footprints protected by translating ribosomes and identify actively translated open reading frames (ORFs). Analysis encompasses QC steps (adaptor sequence removal, trimming), alignment, detection of actively translated ORFs using e.g RiboTaper, ORF quantification, differential expression analysis, translational efficiency estimation and enrichment analysis.

Here analysis can encompass pre-processing steps (alignment, QC, normalisation, data correction & integration, feature selection, dimensionality reduction & visualisation) and downstream analysis (clustering, cluster annotation, trajectory analysis, differential expression analysis, gene set analysis etc). 

Both germline and somatic variant analysis can be performed, the latter being dependent on the availability of both tumour and a reference tissue from the same individual being available (e.g. blood). Analysis can start at the alignment and pre-processing stage, proceeding to variant calling (SNPs, SNVs, indels, CNVs, gene fusions), annotation and filtering. Where SNPs occur in a protein coding region, tools to assess the impact on the protein structure or function can be applied.

Visium spatial gene expression enables the visualisation of gene expression on tissue sections. Analysis can include pre-processing steps (demultiplexing, alignment, filtering, barcode & UMI counting & visualisation) and downstream analysis (cluster analysis and marker identification, integration with scRNA-seq data).

Both homology modelling and de novo techniques can be used to model the 3D structure of proteins. This can be useful when trying to ascertain the impact of a mutation of protein structure or when designing drugs to interact with proteins.