sequenceTools: A package with tools for processing DNA sequencing data

[ bioinformatics, gpl, library, program ] [ Propose Tags ]

The tools in this package process sequencing Data, in particular from ancient DNA sequencing libraries. Key tool in this package is pileupCaller, a tool to randomly sample genotypes from sequencing data.

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Versions [RSS],,,,, 1.5.0, 1.5.2
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Dependencies ansi-wl-pprint (>=0.6.9), base (>= && <5), bytestring (>=, foldl (>=1.4.10), lens-family (>=2.0.0), optparse-applicative (>=, pipes (>=4.3.14), pipes-group (>=1.0.12), pipes-ordered-zip (>=1.1.0), pipes-safe (>=2.3.2), random (>=1.1), sequence-formats (>=1.6.3), sequenceTools, split (>=, transformers (>=, vector (>= [details]
License GPL-3.0-only
Author Stephan Schiffels
Category Bioinformatics
Uploaded by stephan_schiffels at 2022-02-17T08:23:45Z
Distributions LTSHaskell:1.5.2, NixOS:1.5.2
Executables genoStats, vcf2eigenstrat, pileupCaller
Downloads 2184 total (17 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2022-02-17 [all 1 reports]

Readme for sequenceTools-1.5.2

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Install with Bioconda

This repository contains some programs that I use for processing sequencing data.


Simple Installation via stack and hackage

This installation installs the latest version that is up on hackage:

  1. Download stack (
  2. Run stack install sequenceTools --resolver nightly. You should now have the executables from this package under ~/.local/bin.
  3. Add ~/.local/bin to your PATH, for example by adding to your ~/.profile or ~/.bash_profile the line PATH=$PATH:$HOME/.local/bin. Run source ~/.profile or source ~/.bash_profile, respectively, to update your path.

Installation from source via stack

  1. Download stack (
  2. Clone this repository via git clone
  3. Install via cd sequenceTools; stack install



The main tool in this repository is the program pileupCaller to sample alleles from low coverage sequence data. The first step is to generate a “pileup” file at all positions you wish to genotype. To do that, here is a typical command line, which restricts to mapping and base quality of 30:

samtools mpileup -R -B -q30 -Q30 -l <list_of_positions.txt> \
    -f <reference_genome.fasta> \
    Sample1.bam Sample2.bam Sample3.bam > pileup.txt

Important Note: You should definitely use the -B flag, which disables base alignment quality recalibration. This mechanism is turned on by default and causes huge reference bias with low coverage ancient DNA data. This flag disables the mechanism.

In the above command line, the file "list_of_positions.txt" should either contain positions (0-based) or a bed file (see samtools manual for details). The output is a simple text file with all positions that could be genotyped in the three samples.

Next, you need to run my tool pileupCaller, which you run like this:

pileupCaller --randomHaploid --sampleNames Sample1,Sample2,Sample3 \
    --samplePopName MyPop -f <Eigenstrat.snp> \
    -e <My_output_prefix> < pileup.txt

Here, options --sampleNames gives the names of the samples that is output in the Eigenstrat *.ind file, and and -–samplePopName is optional to also give the population names in that file (defaults to Unknown, you can also change it later in the output). Then, (required) option -f needs an Eigenstrat positions file. This is required for pileupCaller to know what is the reference and which the alternative allele in your reference dataset that you want to call. An Eigenstrat positions file is a line-based file format, where each line denotes a SNP position, and there are exactly six required columns, denoting in order i) SNP ID, ii) chromosome, iii) genetic position (can be set to zero), iv) physical position, v) reference allele, vi) alternate allele. Here is an example:

rs0000  11  0.000000    0   A   C
rs1111  11  0.001000    100000  A   G
rs2222  11  0.002000    200000  A   T
rs3333  11  0.003000    300000  C   A
rs4444  11  0.004000    400000  G   A
rs5555  11  0.005000    500000  T   A
rs6666  11  0.006000    600000  G   T

Finally, the -e option specifies Eigenstrat as output format and gives the prefix for the *.ind, *.pos and *.geno files. Without the -e option, pileupCaller will output in FreqSum format, described here, which is useful for debugging your pipeline, since it's just a single file that is output into the terminal and can therefore easily be inspected.

You can also get some help by typing pileupCaller -h, which shows a lot more option, for example the sampling method, minimal coverage and other important options.

Note that you can also fuse the two steps above into one unix pipe:

samtools mpileup -R -B -q30 -Q30 -l <list_of_positions.txt> \
    -f <reference_genome.fasta> \
    Sample1.bam Sample2.bam Sample3.bam | \
pileupCaller --randomHaploid --sampleNames Sample1,Sample2,Sample3 \
    --samplePopName MyPop -f <Eigenstrat.snp> \
    -e <My_output_prefix>

There is however an issue here: If you have aligned your read data to a version of the reference genome that uses chr1, chr2 and so on as chromosome names, the resulting Eigenstrat file will be valid, but won't merge with other Eigenstrat datasets that use chromosome names 1, 2 and so on. I would therefore recommend to strip the chr from your chromosome names if necessary. You can do that easily using a little UNIX filter using the sed tool. In the full pipeline, it looks like this:

samtools mpileup -R -B -q30 -Q30 -l <list_of_positions.txt> \
    -f <reference_genome.fasta> \
    Sample1.bam Sample2.bam Sample3.bam | sed 's/chr//' | \
pileupCaller --sampleNames Sample1,Sample2,Sample3 \
    --samplePopName MyPop -f <Eigenstrat.snp> \
    -o EigenStrat -e <My_output_prefix>


Simple tool to convert a VCF file to an Eigenstrat file. Pretty self-explanatory. Please run vcf2eigenstrat --help to output some documentation.


A simple tool to get some per-individual statistics from an Eigenstrat or Freqsum-file. Run genoStats --help for documentation.


This package also contains several haskell wrapper scripts for the following ADMIXTOOLS and EIGENSOFT commands: convertf, mergeit, qp3Pop, qpDstat and smartPCA. The original tools require parameter files as input, which I find tedious to use in bioinformatics pipelines. I wrote those wrapper scripts to be able to start the tools with a simple command line option interface.

If you have stack installed your system (see above), you should be able to run those scripts on your machine without any difficult setup. Simply clone this repository, navigate to the scripts subfolder and invoke any script using standard bash execution, for example


If you start this the first time it may take a while, since stack downloads all dependencies and even the script interpreter for you, but after that it should start instantanious. If you want to use the scripts from your path, I suggest to put symbolic links into any folder that is already on your path (for example ~/.local/bin).