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README.md

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Haplogrep is a command-line tool for mtDNA haplogroup classification. We also provide haplogrep as a fast and free haplogroup classification web service. You can upload your mtDNA profiles aligned to rCRS or RSRS (beta) and receive mitochondrial haplogroups in return. FASTA, VCF and hsd input files are supported. So far, Haplogrep and the updated Haplogrep 2 have been cited over 700 times (Google Scholar - August 13, 2020). Please join our Haplogrep Google User Group for future updates and ongoing discussions.

Requirements

Java 8 or higher

Download and Install

Download and install the latest version from our download page using the following commands:

curl -sL haplogrep.now.sh | bash
./haplogrep 

Available Tools

Currently two tools are available.

  • Classify allows to classify input profiles into haplogroups.
  • Distance calculates the distance between two haplogroups.

Haplogrep Classify

Run Haplogrep Classification with test data

  wget https://github.com/seppinho/haplogrep-cmd/raw/master/test-data/vcf/HG00097.vcf.gz
  ./haplogrep classify --in HG00097.vcf.gz --format vcf --out haplogroups.txt

Input File Formats

VCF or Fasta

The recommended input format is a single-sample/multi-sample VCF (*.vcf.gz or *.vcf).

FASTA

For alignment, bwa version 0.7.17 is used. For each input sequence, haplogrep excludes positions from the tested range that are (1) not covered by the input fragment or (2) has marked with a N in the sequence.

hsd Format

You can also specify your profiles in the original Haplogrep hsd format, which is a simple tab-delimited file format consisting of 4 columns (ID, Range, Haplogroup and Polymorphisms).

Sample1 1-16569 H100 263G 315.1C 750G 1041G 1438G 4769G 8860G 9410G 12358G 13656C 15326G 16189C 16192T 16519C
Sample2 1-16569 ? 73G 263G 315.1C 750G 1438G 3010A 3107C 4769G 5111T 8860G 10257T 12358G 15326G 16145A 16222T 16519C

For readability, the polymorphisms are also tab-delimited (so columns >= 4). A hsd example can be found here.

Required Parameters

Parameter Description
--in Please provide the input file name
--format Please provide the input format of your data - valid options are: hsd, vcf, or fasta files
--out Please provide an output name

Additional Parameters

Parameter Description
--rsrs By default Haplogrep expects that your data is aligned against rCRS (which is included in the human references hg19 and hg38). If your data is aligned against RSRS, add the --rsrs parameter (Default: off). Please read this blog post carefully before adding this option.
--metric To change the classification metric to Hamming Distance (hamming) or Jaccard (jaccard) add the --metric parameter (Default: Kulczynski Measure).
--extend-report For additional information on mtSNPs (e.g. found or remaining polymorphisms) please add the --extend-report flag (Default: off).
--phylotree The used Phylotree version can be changed using the --phylotree parameter (Default: 17, allowed numbers from 10,11,12,..,17 (latest version)).
--chip If you are using genotyping arrays, please add the --chip parameter to limit the range to array SNPs only (Default: off, VCF only). To get the same behaviour for hsd files, please add only the variants to the range, which are included on the array or in the range you have sequenced (e.g. control region). Range can be sepearted by a semicolon ;, both ranges and single positions are allowed (e.g. 16024-16569;1-576;8860).
--fixNomenclature To fix the mtDNA nomenclature after alignment of fasta files, set the --fixNomenclature parameter. See below for further information.
--hits To export the best n hits for each sample add the --hits parameter. By default only the tophit is exported.
--lineage Create a graph of all input samples by using the --lineage parameter. (Default: off). As an output we provide a Graphviz DOT file. You can then use graphviz (sudo apt-get install graphviz) to convert the dot file to a e.g. pdf (dot <dot-file> -Tpdf > graph.pdf).

Haplogrep Distance

This tool allows to calculate the distance between two haplogroups.

Required Parameters

Parameter Description
--in Input file must include 2 columns named "hg1" and "hg2" seperated by ";"
--out Output location of distance file

mtDNA reference sequences

Several mtDNA references exist, Haplogrep supports rCRS and RSRS. Please checkout our blog post to learn more about this topic.

Genotyping arrays

If you are using Haplogrep for genotyping array data, please have a look at the --chip parameter above.

mtDNA Nomenclature

When using fasta as an input format, Haplogrep uses bwa mem to align data. Since the mitochondrial phylogeny is using a 3′ alignment, indels are often not correctly placed for haplogroup classification, when using standard-aligner designed for nuclear DNA. In some cases, where haplogroup defining indels are expected (e.g. missing 8281d-8289d) this can yield to a lower haplogroup quality. To adjust for that, we provide a set of currently 66 rules that can be applied prior to classification. The rules have been estimated based on 7,848 fasta files in 4 steps:

  1. Downloading Phylotree defining sequences from GenBank
  2. Aligning data with bwa mem
  3. Classifying the profiles using Haplogrep
  4. Comparing final fasta profiles with the Phylotree input profiles (remaining vs. not found) in a txt format (derived from parsing Phylotree). For example, the subsequent rule changes input polymorphisms 309.1CCT 310C to 309.1C 309.2C 315.1C.

Heteroplasmies (VCF only)

Heteroplasmies are often stored as heterozygous genotypes (0/1). If a AF tag (= Allele Frequency) is specified in the VCF file, we add variants with a AF > 0.90 to the input profile. Mutation Server is able to create a valid VCF including heteroplasmies starting from BAM or CRAM.

Please have a look at mtDNA-Server to check for heteroplasmies and contamination in your NGS data.

Blog

Check out our blog regarding mtDNA topics.

Cite use

If you use Haplogrep, please cite our latest Haplogrep2 paper in combination with Phylotree 17. The first Haplogrep paper can be found here.

Contact

Sebastian Schoenherr (@seppinho)
Hansi Weissensteiner (@haansi)
Institute of Genetic Epidemiology
Medical University of Innsbruck

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