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

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).

SUBMITTER: Lang OW

PROVIDER: S-EPMC10711449 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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

Lang Olivia W OW Srivastava Divyanshi D Pugh B Franklin BF Lai William K M WKM

Nucleic acids research 20231201 22

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. ...[more]

PMID: 37933851

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Project description:BackgroundGenome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants - typically single-nucleotide polymorphisms (SNPs) - in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions.ResultsTo practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals.ConclusionsAs a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.

Project description:ContextPseudohypoparathyroidism type I (PHP-I) is divided into PHP-Ia with Albright hereditary osteodystrophy and PHP-Ib, which usually shows no Albright hereditary osteodystrophy features. Although PHP-Ia and PHP-Ib are typically caused by genetic defects involving α subunit of the stimulatory G protein (Gsα)-coding GNAS exons and methylation defects of the GNAS differentially methylated regions (DMRs) on the maternal allele, respectively, detailed phenotypic characteristics still remains to be examined.ObjectiveTo clarify phenotypic characteristics according to underlying (epi)genetic causes.Patients and methodsWe performed (epi)genotype-phenotype analysis in 69 Japanese patients with PHP-I; that is, 28 patients with genetic defects involving Gsα-coding GNAS exons (group 1) consisting of 12 patients with missense variants (subgroup A) and 16 patients with null variants (subgroup B), as well as 41 patients with methylation defects (group 2) consisting of 21 patients with broad methylation defects of the GNAS-DMRs (subgroup C) and 20 patients with an isolated A/B-DMR methylation defect accompanied by the common STX16 microdeletion (subgroup D).ResultsAlthough (epi)genotype-phenotype findings were grossly similar to those reported previously, several important findings were identified, including younger age at hypocalcemic symptoms and higher frequencies of hyperphosphatemia in subgroup C than in subgroup D, development of brachydactyly in four patients of subgroup C, predominant manifestation of subcutaneous ossification in subgroup B, higher frequency of thyrotropin resistance in group 1 than in group 2, and relatively low thyrotropin values in four patients with low T4 values and relatively low luteinizing hormone/follicle-stimulating hormone values in five adult females with ovarian dysfunction.ConclusionThe results imply the presence of clinical findings characteristic of each underlying cause and provide useful information on the imprinting status of Gsα.

Dataset Information

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

Publications

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

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