pseudoseq

Pseudoseq is a term used in computational biology and related fields to denote a synthetic or surrogate sequence dataset created to mimic real sequencing data without requiring biological samples. In practice, pseudoseq data are generated in silico by simulating reads from a reference genome, transcriptome, or constructed sequence, applying controlled error profiles, coverage, and read-length distributions. The purpose is to facilitate testing, benchmarking, and education of sequencing analyses and pipelines.

The concept arises from the need to evaluate software for read alignment, assembly, variant calling, and expression quantification under known ground truth. Pseudoseq allows researchers to introduce specific complexities such as sequence repeats, GC bias, or structural variants in a repeatable, reproducible manner. Because the data are generated rather than measured, they provide a known baseline to assess accuracy and sensitivity of tools.

In practice, pseudoseq may also refer to surrogate sequences used to illustrate algorithmic ideas or to demonstrate data-processing workflows without handling real data. In some contexts, the term highlights the distinction between observed sequencing data and synthetic data produced for methodological development.

Limitations include the fact that pseudoseq cannot fully capture the stochastic nature and biological variability of real samples. Model choice, error profiles, and parameter settings heavily influence outcomes, so simulations should be validated against real data when possible. See also simulated data, read simulation, sequencing benchmarks, and in silico experiments.