Nukleotidskning

Nukleotidskning is a term used in genomics to describe the systematic analysis of nucleotide sequences to identify features of functional or biological interest. The concept covers both computational and experimental approaches to examining DNA or RNA sequences for patterns, motifs, variants, or structural signals.

In computational nucleotidskning, sequence data are scanned with algorithms to locate known or novel motifs such as transcription factor binding sites, splice sites, promoters, or repetitive elements. Techniques include motif scanning with position weight matrices, regular-expression searches, k-mer counting, and machine learning classifiers. Experimental nucleotidskning uses high-throughput assays to map nucleotide-related features across genomes or transcriptomes, for example by probing accessibility, binding, or activity in a tiling or saturation strategy and then sequencing the results to infer functional regions.

Applications include genome annotation, regulation discovery, interpretation of genetic variation, and design of synthetic sequences with desired properties. In research and clinical contexts, nucleotidskning supports prioritizing variants for further study and predicting off-target effects for genome editing tools.

Limitations include dependence on data quality and annotation, context specificity of regulatory elements, and the need for experimental validation to confirm predictions. The term is used in Scandinavian-language literature as a generic descriptor of scanning nucleotides for meaningful patterns, rather than a single unified technology. Related concepts include nucleotide sequencing, motif discovery, and genome annotation.