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Quality control of large genome datasets.

Max Robinson1, Arpita Joshi1, Ansh Vidyarthi1

  • 1Institute for Systems Biology, 401 Terry Avenue N, Seattle, WA 98109, USA.

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|July 5, 2022
PubMed
Summary

The 1000 Genomes Project (TGP) has multiple versions with noted discrepancies. A cross-comparison revealed issues in cohort membership, variation levels, and genome quality, recommending rapid genome comparisons for quality assessment.

Keywords:
comparative analysisgold-standard quality benchmarkinghuman genetic variationquality assessment strategiesreference genome sequence

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Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetic Variation

Background:

  • The 1000 Genomes Project (TGP) is a key resource for human genetic variation.
  • Seven public versions of TGP genomes exist, based on different reference sequences (GRCh37, GRCh38) and processing pipelines.
  • Independent resequencing efforts and new pipelines have generated additional TGP data versions.

Purpose of the Study:

  • To perform a cross-comparison evaluation of all seven public versions of the 1000 Genomes Project data.
  • To identify discrepancies and quality issues across different TGP versions.
  • To provide recommendations for quality assessment of large genome datasets.

Main Methods:

  • Genome fingerprinting was used for ultrafast comparison of TGP genome versions.
  • Cross-comparison evaluated cohort membership, variation levels, pipeline performance, individual relationships, phasing, and annotation.
  • Benchmarking against the Genome In A Bottle Consortium's "platinum quality" genome (NA12878) was used for validation.

Main Results:

  • Multiple issues were identified across TGP versions, including cohort membership discrepancies and disagreement on variation levels.
  • Substandard pipeline performance was observed in specific genomes and genomic regions.
  • Cryptic relationships, inconsistent phasing, and reference genome history-induced annotation distortions were detected.

Conclusions:

  • Rapid genome comparisons are recommended for global quality assessment of large genome datasets.
  • Benchmarking remains a crucial part of best-practice quality assessment.
  • Understanding version-specific issues is essential for researchers choosing a TGP dataset for their analyses.