GenoPipe: identifying the genotype of origin within (epi)genomic datasets

• Published in: Nucleic acids research Vol. 51; no. 22; pp. 12054 - 12068
• Main Authors: Lang, Olivia W, Srivastava, Divyanshi, Pugh, B Franklin, Lai, William K M
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 11.12.2023
• Subjects: Computational Biology; Datasets as Topic; Genome; Genomics - methods; Genotype; High-Throughput Nucleotide Sequencing - methods; Polymorphism, Single Nucleotide; Sequence Analysis, DNA - methods
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkad950

Abstract Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of many laboratories. Technical mistakes can and do occur at nearly every stage of a genomics assay (i.e. cell line contamination, reagent swapping, tube mislabelling, etc.) and are often difficult to identify post-execution. However, the DNA sequenced in genomic experiments contains certain markers (e.g. indels) encoded within and can often be ascertained forensically from experimental datasets. We developed the Genotype validation Pipeline (GenoPipe), a suite of heuristic tools that operate together directly on raw and aligned sequencing data from individual high-throughput sequencing experiments to characterize the underlying genome of the source material. We demonstrate how GenoPipe validates and rescues erroneously annotated experiments by identifying unique markers inherent to an organism's genome (i.e. epitope insertions, gene deletions and SNPs). Graphical Abstract Graphical Abstract