Human genomicsRare & inherited disease research
Build a genomic prioritisation strategy integrating inheritance model, family structure, phenotype, call quality and available level of evidence.
- Filtering and prioritisation according to frequency, functional effect, inheritance model and phenotypic context
- Segregation analysis and pedigree consistency when family data are available
- Reanalysis using updated references, annotations or hypotheses, including CNV/SV when supported by the data
Clinical interpretation and diagnostic validation belong to a specific framework and are not automatically inferred from bioinformatics analysis.
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OncologyCancer research & molecular oncology
Characterise the molecular landscape of tumour samples and compare genomic or transcriptomic profiles according to the study design.
- Detection and annotation of somatic variants, copy-number alterations and structural variants
- Fusions, differential expression and pathway analyses
- Comparisons incorporating available controls, sample quality and relevant technical factors
Purity, depth, control quality and tumour context directly affect sensitivity and interpretation of results.
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PathogensInfectious diseases & antimicrobial resistance
Combine genomic characterisation, typing and isolate comparison to study diversity, lineages, antimicrobial resistance and virulence factors.
- Quality control, assembly or mapping, annotation and genomic characterisation
- Investigation of genes or mutations associated with antimicrobial resistance and comparison with relevant reference resources
- Typing, lineages, virulence, comparative genomics and contextualisation using available metadata
Genomic resistance predictions should be interpreted alongside available microbiological and phenotypic data.
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Molecular epidemiologyGenomic surveillance
Analyse collections of isolates to describe lineages, genomic diversity and relationships between samples in a temporal, geographic or epidemiological context.
- Lineage assignment and typing using schemes appropriate to the organism
- Genomic distances, SNPs, cgMLST or other appropriate comparative approaches
- Phylogenomics, longitudinal collection analyses and integration of temporal or geographic metadata to contextualise clusters
Genomic proximity alone does not establish a transmission chain; it must be interpreted alongside epidemiological metadata.
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Microbial communitiesMicrobiome research
Study microbial community structure while accounting for the compositional nature of the data, covariates, controls and technical effects.
- Taxonomic profiling, diversity, ordination and overall community structure
- Differential analyses using methods suited to compositional data and relevant covariates
- Functional profiling and integration with other omics or phenotypic variables when supported by the data
Conclusions depend as much on the quality of the design and metadata as on the bioinformatics method itself.
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NGS detectionNGS detection of adventitious agents
Use bioinformatics to support untargeted screening for exogenous sequences in NGS data from cell lines, raw materials, biological products or production systems when justified by the analytical context.
- Quality control and subtraction of expected background: host, vector, construct or other known component sequences
- Taxonomic classification, reference alignment and de novo assembly to confirm and characterise candidate signals
- Assessment of signal strength using coverage, read distribution, controls, homology and technical noise, with database and pipeline traceability
The relevance of a signal depends on data quality, available controls, coverage, distribution and experimental context. A bioinformatics detection should be interpreted in light of these elements before any conclusion.
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