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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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ddradseqtools: a software package for in silico simulation and testing of double-digest RADseq experiments.

F Mora-Márquez¹, V García-Olivares², B C Emerson^2,3

¹Forest Genetics and Physiology Research Group, Technical University of Madrid (UPM), Ciudad Universitaria s/n, Madrid, Spain.

Molecular Ecology Resources

|June 12, 2016

Summary

This study introduces ddradseqtools, a software package designed to optimize the experimental design for double-digested RADseq (ddRADseq) library preparation. It aids in simulating in silico fragments, designing libraries, and preprocessing reads to improve data quality and reduce errors.

Keywords:

PCR duplicates allele dropout coverage double-digested RADseq in silico simulation

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Double-digested RADseq (ddRADseq) is a next-generation sequencing (NGS) method for analyzing genetic variation across thousands of loci.
Effective ddRADseq experimental design requires careful consideration of enzyme selection, genome characteristics, library protocols, and error sources.
Optimizing ddRADseq design is crucial for statistically sound and economically efficient genomic studies.

Purpose of the Study:

To present ddradseqtools, a novel software package for facilitating ddRADseq experimental design.
To assist researchers in simulating in silico ddRADseq fragments and designing modified libraries.
To provide tools for initial bioinformatics preprocessing and error simulation in ddRADseq data.

Main Methods:

In silico generation of double-digested fragments based on user-defined parameters.
Simulation of modified ddRADseq library construction, including indexed adapters and degenerate base regions (DBRs) for PCR duplicate quantification.
Generation of simulated single-end (SE) or paired-end (PE) reads, incorporating potential SNPs and indels.
Simulation of allele dropout and PCR duplicate effects on sequencing coverage.

Main Results:

ddradseqtools successfully generates in silico ddRADseq fragments and simulates library construction.
The software quantifies potential PCR duplicates and simulates allele dropout, providing insights into data quality.
Validated correspondence between in silico simulations and in vitro ddRADseq experiments, offering practical guidelines.

Conclusions:

ddradseqtools is a valuable, cost-efficient software for optimizing ddRADseq experimental design.
The tool aids in minimizing missing data and mitigating sources of error, leading to more reliable genomic analyses.
It supports fine-tuning of alignment and variant calling parameters through simulated read outputs.