TagDigger: user-friendly extraction of read counts from GBS and RAD-seq data
© The Author(s). 2016
Received: 12 November 2015
Accepted: 6 July 2016
Published: 11 July 2016
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.
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.
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.
KeywordsGenotyping-by-sequencing Meta-analysis Read depth Restriction site-associated DNA sequencing Single nucleotide polymorphism (SNP) Tag counts
Genotyping-by-sequencing (GBS), and closely-related techniques such as restriction-site associated DNA sequencing (RAD-seq) and double-digest restriction-site associated DNA sequencing (ddRAD), have revolutionized the use of molecular markers in model and non-model organisms by providing inexpensive methods to simultaneously mine and genotype thousands of single nucleotide polymorphism (SNP) markers without the need for a reference genome [1–4]. However, data quality remains a major issue with all of these techniques [4, 5]. Because the DNA that is sequenced is a random sample of all sites adjacent to a particular restriction enzyme cut site, for any given individual many loci will be missing simply due to under-sampling [1, 6]. Moreover, in an individual that is heterozygous at a given locus, it is possible that only one of two alleles will be sequenced, making the individual erroneously appear homozygous [6, 7]. A crucial piece of information for evaluating GBS data quality is therefore the number of sequencing reads per allele in each individual. For example, an individual with 20 reads for one allele and zero reads for the other is likely to be a true homozygote, whereas an individual with only one read for one allele and zero reads for the other might be an under-sampled heterozygote. Where genotype accuracy is important, it is practical to simply remove all homozygous genotype calls below a certain read depth . Methods also exist that take read depth into account when estimating allele frequencies and probabilities that genotype calls are correct  and for estimating relatedness coefficients . On the other hand, tags with unusually high read depth may represent repetitive sequence and should therefore be excluded from analysis . Knowledge of the number of reads per allele is also important for assigning allelic configurations and estimating allele frequencies in polyploids [12, 13].
Given the importance of read depth in evaluating genotype quality and performing downstream analysis of GBS data, one would expect read counts to be accurately exported from all SNP calling pipelines in an easily-accessible format, but this is not the case. TASSEL’s UNEAK pipeline  (for species without a reference genome) exports a text file containing read counts for all SNP calls as part of the final output, but does not report read counts higher than 127. This maximum number of reported read counts is not an issue for filtering out genotypes with low read counts, but does interfere with analysis of polyploid species or bulked samples, for which accurate calculation of read count ratios is needed. TASSEL’s GBS version 2 pipeline  (for species with a reference genome) exports accurate read counts in VCF format only, requiring moderate programming skill to extract those values for analysis. Stacks  exports read counts, although it has the disadvantage of only running on Unix-like operating systems. pyRAD  exports genotypes but not read counts. RADtools  exports read counts in a custom format, but is no longer being updated since Stacks was determined to have superior genotyping and performance . RADtools also does not allow barcode lengths other than 5 nucleotides, and does not allow multiple barcodes per individual.
For tasks such as genomic selection or the assignment of new individuals to known populations, it is also desirable to call alleles from a user-specified set of SNPs, rather than re-running the entire SNP-mining pipeline. With both TASSEL and Stacks one can run data from new samples against a previously-generated library of SNPs. However, neither program allows the user to specify subset of SNPs to examine. pyRAD and RADtools will only genotype SNPs de-novo; they do not have options to add new samples to an existing genotype set.
For cross-study comparisons, it is also useful to have a universal set of marker names across multiple projects. Reference-genome-based pipelines such as TASSEL-GBS facilitate such comparisons by naming SNPs according to alignment position, but reference-free pipelines assign arbitrary numbers to loci since no other information is available for naming. For pipelines such as TASSEL-UNEAK and Stacks, utilities are needed to compare tag sequences across projects and generate universal sets of marker names.
We present new software, TagDigger, which can manage sets of DNA sequence tags and rapidly search for those tags in FASTQ files. TagDigger is open-source, written in Python 3, and will run on any operating system. It is designed to require minimal RAM and processing power so that it can be run on a laptop computer. It can read FASTQ files in either uncompressed or GZIP format, and does not waste hard drive space by uncompressing zipped files or generating other working files before producing the final output. All input and output files are in comma-separated value (CSV) formats so that they can be created and opened with common software such as R or Microsoft Excel. Tag sequences can additionally be read in the formats output by TASSEL, Stacks, and pyRAD. Lastly, TagDigger is designed to be accessible to users lacking programming experience and avoids the use of incomprehensible error messages.
Currently, three Python programs are included with TagDigger: tagdigger_interactive.py, which counts the occurrences of tags in FASTQ files and outputs a table of tag counts indexed by tag and barcode, as well as (optionally) a table of numeric diploid genotypes for biallelic markers; tag_manager.py, which consolidates marker names, tag sequences, alignment and other information across multiple projects; and barcode_splitter.py, which splits a FASTQ file, by barcode, into multiple FASTQ files with barcode, adapter, and potentially chimeric sequence removed, and optionally generates MD5 checksums for all output files to facilitate archiving data with NCBI (National Center for Biotechnology Information), EBI (European Bioinformatics Institute) or DDBJ (DNA Data Bank of Japan). All functions used by these three programs are contained in the file tagdigger_fun.py so that they can be used by Python programmers for tasks such as batch processing. Restriction enzyme cut sites and adapter sequences are also included at the top of the tagdigger_fun.py file so that new enzymes and adapters can be easily added.
For tagdigger_interactive.py, a user-generated key file lists all barcodes to search for in each FASTQ file. Three headers are needed for this CSV file: “File” (indicating the name of the FASTQ file), “Barcode” (indicating the barcode sequence), and “Sample”. Other columns are ignored. If FASTQ file names end with “.gz”, they are assumed to be compressed with GZIP, and otherwise they are assumed to be uncompressed. Barcodes that the user does not wish to investigate can be omitted to speed processing time. If the same sample name appears multiple times, tag counts are summed across all instances of that sample. If barcodes have been removed from the FASTQ file, the user can simply list one sample per file and leave the Barcode column blank. A very similar file is needed for barcode_splitter.py, but with the headers “Input File”, “Barcode”, and “Output File”.
Tag sequences can be imported to tagdigger_interactive.py and tag_manager.py in any of seven different formats. Four of these are the direct output of other SNP-calling software: FASTA files from TASSEL-UNEAK, the SAM file used for generating markers in TASSEL-GBS, tab-delimited text output of the cstacks program in Stacks, and the.alleles file output from pyRAD. Three CSV formats are also readable: two tags per row, with column headers “Marker name”, “Tag sequence 0”, and “Tag sequence 1”; one tag per row (allowing for non-biallelic markers), with column headers “Marker name”, “Allele name”, and “Tag sequence”; and two merged tags per row, with column headers “Marker name” and “Tag sequence”. For the latter format, the variable portion of the tag is put between square brackets with a forward slash separating the two alleles, e.g., ACAGACTT[A/T]GTACCCA. This merged format is also used as the output of tag_manager.py, since it conserves hard drive space and RAM and makes it easy for the human eye to see the polymorphism. For any of the seven tag formats, the user can supply, as a separate file, a list of names of markers to include in the output. Supplying such a list conserves processing time and output file size by ignoring tags in which the user is not interested.
The barcode_splitter.py program uses the above algorithm to assign reads to barcodes. The same search algorithm is used for identifying adapter sequence (to be trimmed out of the read), but the search is performed from the end of the read rather than the beginning. Additionally, the barcode splitter uses more conventional text searching to detect full restriction cut sites present anywhere in the read. Since a full cut site may indicate a chimera of two genomic DNA fragments, sequence after a full cut site is trimmed from the read.
Because the tag_manager.py program only assigns markers the same name if tag sequences are an exact match (as opposed to one tag sequence being a truncated version of another), it does not use the same search algorithm as the other two programs. Instead, a binary search using the bisect module in Python is used for matching tags across two sets of markers.
The tagdigger_interactive.py program generates a CSV file of read counts, with samples in rows and tags in columns. The first row contains tag names, and the first column contains sample names. Tag names consist of the marker name and allele name(s) separated by an underscore. For biallelic markers, the final allele name is ‘0’ or ‘1’, with the tag that comes first alphabetically being allele ‘0’ (except in formats where the user specifies which tag is ‘0’ and which is ‘1’). The nucleotides at variable sites are also included as an allele name. Because TASSEL-GBS may mine multiple SNPs from the same locus where a tag aligns to the genome, TagDigger does not use the original SNP names when naming tags imported from the TASSEL-GBS pipeline, but can output a table indicating how the TASSEL-GBS SNP names correspond to the marker names generated by TagDigger.
When all markers are biallelic, tagdigger_interactive.py can also export a numeric genotype table, with samples in rows and markers in columns. Homozygous genotypes are coded as ‘0’ or ‘2’, heterozygous genotypes are coded as ‘1’, and cells with missing data are left blank. Homozygotes for allele ‘0’ are coded as ‘0’ and homozygotes for allele ‘1’ are coded as ‘2’.
The barcode_splitter.py program generates uncompressed FASTQ files. Barcodes, adapter sequence, and potentially chimeric sequence are removed as described in the previous section. The original comment line is retained for each read, with the barcode added to the end of the comment in keeping with Illumina FASTQ format. Optionally, barcode_splitter.py can also generate a CSV file listing the MD5 checksum of each new FASTQ file that has been created.
The tag_manager.py program outputs a CSV file with the headers “Marker name” and “Tag sequence”. Tag sequences are in the merged format, e.g., CCGATTAG[C/T]AGGGGTT, and can be read back into the TagDigger program. Marker names consist of a prefix supplied by the user followed by a number, e.g., MyLabsTags000102. Optionally, additional columns can contain the original marker names or other data provided by the user in CSV format. A FASTA file of sequences for each marker, with IUPAC nucleotide codes for variable sites, can also be exported. If this FASTA file is used for alignment to a reference genome, tag_manager.py can import the resulting SAM file and add columns to the marker list containing chromosome, position, and alignment quality.
Results and discussion
Performance of the TagDigger search algorithm on a FASTQ file from RAD-seq with 96 barcodes
Number of tags
Time to build indexing tree (s)
Time to process 10,000 FASTQ reads (s)
Estimated time to process 200,000,000 FASTQ reads (min)
0.03 ± 0.01
0.218 ± 0.016
0.84 ± 0.09
0.238 ± 0.005
7.79 ± 1.18
0.291 ± 0.007
Performance results indicate that the tag searching algorithm is highly scalable, not unlike a binary search algorithm. For large sets of tags, more processing time (a few seconds) is required to build an indexing tree, but that amount of time remains insignificant compared to the amount of time needed to process an entire FASTQ file (one to two hours for ~200 million reads, the typical output from Illumina HiSeq technology; Table 1). Using our search algorithm, the time to process a FASTQ file is not drastically different whether 100 tags or 10,000 tags are being examined.
Performance of de-novo GBS and RAD-seq pipelines when analyzing a single FASTQ file
Size of intermediate files generated (Gb)
RAM utilized by pipeline (Gb)
Total time, across all processor cores, to process 203,000,000 FASTQ reads and output genotypes (min)
UNEAK pipeline in TASSEL 3.0
TASSEL-UNEAK was the only pipeline that was faster than TagDigger, needing twenty minutes to process the file (Table 2), compared to the one to two hours needed by TagDigger (Table 1). However, UNEAK required more RAM and hard drive space than TagDigger. Stacks was the next fastest with seven hours of processing time, and pyRAD was by far the slowest with 387 h of processing time (Table 2), making it impractical for users without access to a computer cluster for parallel processing. UNEAK, Stacks, and pyRAD ranked in the same order in terms of processing time, RAM, and hard drive space needed (Table 2). The main advantage of pyRAD, and the likely reason why it requires long processing times, is its ability to detect insertions and deletions . Given that TagDigger can import the output of pyRAD and genotype the same insertions and deletions on new samples much more quickly and with much less available RAM and hard drive space, we expect TagDigger to be especially useful to pyRAD users. We did not benchmark RADtools on our FASTQ file, given that the file included barcodes of multiple lengths, which is not supported by RADtools.
Performance of software for de-multiplexing a single FASTQ file
Barcode_splitter.py in TagDigger
All three of the TagDigger programs can be launched from the operating system’s shell or command prompt. For example, one would use cd to navigate into the directory containing the TagDigger programs, then type “python tagdigger_interactive.py” without any additional arguments. The program then prompts the user to supply information such as restriction enzyme, directory for reading and writing files, and names of input and output files. If the user makes a mistake, for example misspelling the name of a restriction enzyme or input file, the software simply prompts them again for that piece of information. Information is printed to the console such as the number of barcodes and tags read from input files. As each FASTQ file is processed, TagDigger also prints its progress to the console so that the user can estimate how much time remains.
With the increasing popularity of GBS among research groups that lack previous bioinformatics experience, we expect that TagDigger will help many users to more easily manage their data and evaluate genotype quality. Universal marker names generated by TagDigger will make meta-analysis of mapping and association studies more straightforward. Easy accessibility of read count data via TagDigger will facilitate the development of new statistical methodologies that utilize read depth information. Lastly, we hope that TagDigger will encourage the archiving of raw GBS sequence reads in public databases such as NCBI, EBI, and DDBJ by providing a platform-independent tool for splitting FASTQ files by barcode and calculating MD5 checksums. All source code for TagDigger is available with this manuscript (Additional file 1).
Availability and requirements
Project name: TagDigger
Project home page: https://github.com/lvclark/tagdigger
Archived version: DOI: https://doi.org/10.5281/zenodo.55760
Operating systems: Platform independent
Programming language: Python
Other requirements: Python 3.3 or higher
License: GNU GPL v. 3
Any restrictions to use by non-academics: none
CSV, comma-separated value; DDBJ, DNA Data Bank of Japan; ddRAD, double digest restriction-site associated DNA sequencing; EBI, European Bioinformatics Institute; GBS, genotyping-by-sequencing; IUPAC, International Union of Pure and Applied Chemistry; NCBI, National Center for Biotechnology Information; RAD-seq, restriction site-associated DNA sequencing; SAM, sequence alignment/map; SNP, single nucleotide polymorphism
We thank Gitanshu Munjal for bringing issues with other software to our attention and Hongxu Dong for testing TagDigger.
Author LC was supported by the DOE Office of Science, Office of Biological and Environmental Research (grant number DE-SC0012379).
LC created the software and drafted the manuscript. ES requested universal marker names, provided data for testing the software, and critically revised the manuscript. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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