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Targeted DNA Methylation Analysis by Next-generation Sequencing
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A reference-free approach to analyse RADseq data using standard next generation sequencing toolkits.

Rasmus Heller1, Casia Nursyifa1, Genís Garcia-Erill1

  • 1Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen N, Denmark.

Molecular Ecology Resources
|January 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a reference-free pipeline for RADseq data processing, blending de novo assembly with next-generation sequencing tools. Allelic dropout significantly biases RADseq data, impacting heterozygosity estimates.

Keywords:
RADseqallelic dropoutgenetic diversitygenotype callinggenotype likelihoodsite frequency spectrum

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Genotyping-by-sequencing (GBS) methods like RADseq are vital for large-scale genomic studies, especially when reference genomes are unavailable.
  • Existing RADseq software has limitations compared to general next-generation sequencing (NGS) toolkits.
  • Processing RADseq data without a reference genome necessitates specialized approaches.

Purpose of the Study:

  • To develop and evaluate a novel, reference-free pipeline for RADseq data processing.
  • To integrate de novo assembly methods with standard NGS tools for enhanced data analysis.
  • To assess the impact of allelic dropout (AD) on RADseq data and heterozygosity estimates.

Main Methods:

  • A pipeline was created using de novo RADseq assembly from STACKS to generate a RAD loci catalog for read mapping and variant calling.
  • RADseq data from 28 zebras (~8x depth) were analyzed and compared with alternative pipelines.
  • Site frequency spectra (SFS) were compared between the new pipeline, other methods, and medium-depth shotgun sequencing data (~13x).

Main Results:

  • The developed pipeline produced comparable SFS to other methods at 8x sequencing depth.
  • A genotype likelihood-based pipeline showed similar performance to ours at low sequencing depths (2-4x).
  • RADseq SFS were slightly skewed towards rare/invariant alleles compared to shotgun sequencing, attributed to allelic dropout (AD).
  • AD caused a ~16% heterozygosity deficit, reduced to ~5% after filtering, identifying it as a major bias source.

Conclusions:

  • The developed reference-free pipeline offers a viable alternative for RADseq data processing, especially when reference genomes are absent.
  • Allelic dropout is a significant source of bias in RADseq data, leading to heterozygosity underestimation.
  • Filtering for allelic dropout is crucial for accurate population genetic inferences from RADseq data.