TagDigger: user-friendly extraction of read counts from GBS and RAD-seq data

Clark, Lindsay V.; Sacks, Erik J.

Permalink

https://hdl.handle.net/2142/95132 Copy

Description

Title

TagDigger: user-friendly extraction of read counts from GBS and RAD-seq data

Author(s)

• Clark, Lindsay V.
• Sacks, Erik J.

Issue Date

2016-07-11

Keyword(s)

• Genotyping-by-sequencing
• Meta-analysis
• Read depth
• Restriction site-associated DNA sequencing
• Single nucleotide polymorphism (SNP)
• Tag counts

Date of Ingest

2017-02-07T22:00:45Z

Abstract

Background: In genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), read depth is important for assessing the quality of genotype calls and estimating allele dosage in polyploids. However, existing pipelines for GBS and RAD-seq do not provide read counts in formats that are both accurate and easy to access. Additionally, although existing pipelines allow previously-mined SNPs to be genotyped on new samples, they do not allow the user to manually specify a subset of loci to examine. Pipelines that do not use a reference genome assign arbitrary names to SNPs, making meta-analysis across projects difficult. Results: We created the software TagDigger, which includes three programs for analyzing GBS and RAD-seq data. The first script, tagdigger_interactive.py, rapidly extracts read counts and genotypes from FASTQ files using user-supplied sets of barcodes and tags. Input and output is in CSV format so that it can be opened by spreadsheet software. Tag sequences can also be imported from the Stacks, TASSEL-GBSv2, TASSEL-UNEAK, or pyRAD pipelines, and a separate file can be imported listing the names of markers to retain. A second script, tag_manager.py, consolidates marker names and sequences across multiple projects. A third script, barcode_splitter.py, assists with preparing FASTQ data for deposit in a public archive by splitting FASTQ files by barcode and generating MD5 checksums for the resulting files. Conclusions: TagDigger is open-source and freely available software written in Python 3. It uses a scalable, rapid search algorithm that can process over 100 million FASTQ reads per hour. TagDigger will run on a laptop with any operating system, does not consume hard drive space with intermediate files, and does not require programming skill to use.

Publisher

BioMed Central

Type of Resource

text

Genre of Resource

Article

Language

eng

Permalink

http://hdl.handle.net/2142/95132

DOI

https://doi.org/10.1186/s13029-016-0057-7

Sponsor(s)/Grant Number(s)

DOE Office of Science, Office of Biological and Environmental Research (grant number DE-SC0012379)

Owning Collections