Lischer, L. Summary: The analysis of genetic data often requires a combination of several approaches using different and sometimes incompatible programs. In order to facilitate data exchange and file conversions between population genetics programs, we introduce PGDSpider, a Java program that can read 27 different file formats and export data into 29, partially overlapping, other file formats.
The PGDSpider package includes both an intuitive graphical user interface and a command-line version allowing its integration in complex data analysis pipelines. Contact: heidi. Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press is a department of the University of Oxford.
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Sign In. Advanced Search. Search Menu. Article Navigation. PGDSpider is able to parse 33 and to write 36 different file formats:. Note that, PGDSpider is currently not meant to convert large NGS files as it loads into memory the whole input file, which may lead to memory issues. However, PGDSpider allows one to convert specific subsets of these NGS files into any other format, and this approach can be used to perform sliding windows analyses on large NGS files.
Help If you have any problems:. Bioinformatics Copyright c , Heidi E. All rights reserved. By using, modifying or distributing this software you agree to be bound by the terms of this license. If there are any bugs, send me an e-mail. Please give me a short description of the bug and tell me the input and output file format. If it is possible also attach the input file which caused the problem.
PGDSpider is an on-going project. For any comments or suggestions of further file formats, please send me an e-mail. Data format. References and Links. External dependency. Input format. Output format. Lischer and Excoffier, Excoffier and Lischer, Li et al. Gompert and Buerkle, ; Gompert et al. Tang et al. Wilson et al. Coop et al. Glaubitz, Others e. ANGSD , can trim reads during the data filtering step.
For that reason, we do not recommend trimming here. Figure 1. Visual depiction of workflow. Read group identifiers are used to identify sequence data by sequencing technology e. Illumina , flow cell, lane, sample ID, and library. Using these identifiers ensures that batch effects, or biases in the data that might have been introduced at different stages of the sequencing process can be properly accounted for.
The flowcell barcode is a unique identifier for a flow cell, lane is the lane of that flowcell, and sample is the sample or library specific identifier. SM : Sample The name of the sample represented by this read group. This will be the name used in the sample column of the VCF file. PL : Platform The sequencing technology used to create the data. This is used by MarkDuplicates to identify which read groups contain molecular e. PCR duplicates. The read group information can be found in the file header look for RG and the RG:Z tag for each sequence record.
This information is not automatically added to Fastq files following sequencing, but needs to be added either when mapping with BWA or separately after mapping with Picard's AddOrReplaceReadGroups tool.
If you don't know the information on the flowcell and lane for your data, you can derive the information from the sequence headers found in a Fastq file, as described in the Sequence reads section. Once you have your reads, you need to map them to a reference genome.
There are many different aligners out there e. Before you can align your reads to a reference genome, you need to create an index. This only needs to be completed once per reference genome. The genome prefix should be a short identifier to be used as the prefix for all output files e. For most resequencing data, we want to use the bwa mem algorithm for 70bp to 1Mbp query sequences to map our reads. A typical command would be:.
There are many arguments available to use, as you can read in the manual. Some of the key arguments for these purposes are:. This is a common file format, and detailed documentation can be found on the Samtools website. Samtools is part of a useful set of programs written to interact with high throughput sequencing data. The details of all you can do with this program are beyond the scope of this tutorial, but this program can be used to view, merge, calculate the depth of coverage, calculate other statistics, and index SAM-style files among other things.
Following alignment, you will need to sort the SAM file. We also recommend you store these types of alignment files in BAM format, which is similar to SAM format, but its binary equivalent, and therefore compressed and more efficient.
To use this BAM file, you also need to create a BAM index, so that software can efficiently access the compressed file.
This is a universal command that can be applied to any PicardTools program. However, if you need to create an index separately, we do this with the BuildBamIndex command. It may also be useful to calculate metrics on the aligned sequences. We can easily do this with the CollectAlignmentSummaryMetrics tool.
Note that you can collect the alignment metrics on several different levels. In the below example, I've included metrics both at the sample and read group level. You also need to include the reference fasta file.
After alignment, sorting and indexing, it is necessary to identify any duplicate sequences from the same DNA fragment in your files that occur due to sample preparation e. This possibility is why it is important to identify read groups for different lanes of the same sample. This is also a useful point to merge together any BAM files from the same sample that are currently separated demonstrated in example below. We identify duplicate sequences with MarkDuplicates , and additional details on how this is performed can be found in the tool documentation.
Note that it is not recommended to actually remove the duplicate sequences from the file, but simply to mark the flags appropriately in the BAM file, so that those sequences are ignored downstream. If using tools other than those we recommend here, make sure they can identify these flags. These sequences can also be removed later should the need arise. We also recommend creating a deduplications metrics file, which will report the proportion and type of duplicate sequences in your sample and read groups.
Following deduplication make sure to sort and index your file, as shown in the above section. Once you are done with the above steps, it is best practice to validate your BAM file, to make sure there were not issues or mistakes associated with previous analyses.
This is done with ValidateSamFile. This step corrects base quality scores in the data for systematic technical errors based on a set of known true variants, such as those generated for humans in the genomes project.
That is:. To run the BSQR tool , first create the recalibration table:. Calling variants with HaplotypeCaller is essentially a two-step process similar to indel realignment. First, you call genotypes individually for each sample. Second, you perform joint genotyping across samples to produce a multi-sample VCF call-set. The advantage to this strategy is that the most computationally intensive step, calling genotypes for each sample, only needs to be performed once, even if additional samples will be added later.
The joint genotyping, which is less computationally intensive, can be performed as many times as needed as individuals may be added to the dataset. Note that even if you are not planning on using SNP calls in downstream analyses e. For each sample, the HaplotypeCaller program is used to call variants. The minimum options needed are a reference genome, BAM files for that sample, and output file name.
See the program page for additional parameter options. Note, for low-coverage data, we recommend changing the defaults for two options: --min-pruning 1 and --min-dangling-branch-length 1.
These commands ensure that any paths in the sample graph see detailed documentation on the model are only dropped if there is no coverage. Otherwise the defaults of 2 and 4 respectively, will drop low-coverage regions. See the documentation for details on these and other available options. Figure 2. Scatter-gather approach. Once you have run HaplotypeCaller on your cohort of samples, the resulting GVCFs need to be combined into a single file using GenomicsDBImport before we can use them to call variants across all of our samples.
Also, for non-human organisms, you may wish to vary the heterozygosity prior from the default value of 0. You can do this with the heterozygosity option, for example with a value of 0.
If you wish to include all sites, both variant and invariant, you need to use the --include-non-variant-sites true option. Note, if you specify output file names with.
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