Deep Y-haplogroup calls using mapping data to a reference genome.
Project description
y_call
A Python package designed to make Y-haplogroup calls using large sets of SNPs and adressed to deal with ancient DNA issues.
Overview
y_call is a reproducible pipeline for studying Y‑haplogroups inferred from low‑coverage or ancient DNA BAM files,
since it evaluates a large number of genomic positions in the Non-recombining Region of the Y chromosome (NRY).
It performs SNP filtering, genotyping via pileup extraction, deamination filtering, and Y-haplogroup calls,
placing samples into known Y-phylogenies and displaying a phylogenetic tree if desired by the user.
Features
- SNP filtering and database‑driven Y-haplogroup structure.
- Pileup extraction and genotyping using BamTable (v0.1.2).
- Ancestral vs. derived allele quantification.
- Y-haplogroup scoring and MRCA resolution.
- Per‑sample tree generation.
- Global phylogenetic tree (optional).
- Summary statistics and lineage analysis.
- CLI and Python API.
Installation
Install from PyPI:
pip install y_call
Also, if preferred, install from source:
git clone https://github.com/EricGH-018/y_call
cd y_call
pip install .
Downloading reference databases
The y_call package does not bundle the full Y-chromosome reference datasets (YFull and OY)
to keep the installation lightweight.
Instead, these databases are hosted as versioned assets on GitHub Releases.
You can download them directly using the built‑in command:
y_call_download_data --database YFull
y_call_download_data --database OY
y_call_download_data --database translation
y_call_download_data --database ages
The corresponding files will be stored in:
~/data/input/
NOTE: always make sure that the working directory includes the ./data/input directory,
since y_call needs it to look for input data.
The YFull and OY reference datasets included in this package are derived from
publicly available information provided by YFull/YTeam and OY, respectively.
These files are reformatted and processed versions created specifically for use with y_call.
Original data sources:
- YFull YTree: https://github.com/YFullTeam/YTree
- OY: https://ysnp.info/downloads/
The y_call package does not redistribute the original tree files.
Only derived, reformatted tables are included.
Updating the file mm.tsv for filtering
y_call makes a filtering of certain SNPs found to exhibit ancestral states
in 1 sample or more.
After a first Y-haplofgroup call for a set of individuals, the user can update
the corresponding file with:
y_call_filter_mm --database YFull
y_call_filter_mm --database OY
The existing mm.tsv considers this filtering from a subset of 247 samples.
Unifying all Y-haplogroup calls in summary files
After Y-haplogroup calling at the sample level, y_call enables the user to create
file integrating information for the whole dataset analysed. This can be done with:
y_call_unify --database YFull --create_phylogeny Y
y_call_unify --database OY --create_phylogeny Y
Command‑line usage (with default parameters)
y-call \
--bam-list samples.tsv \
--index_i 0 \
--index_f 0 \
--base_qual 20 \
--map_qual 25 \
--rg "hg38" \
--width 60 \
--height 80 \
--transitions "N" \
--k 3 \
--database "YFull" \
--translation "./data/input/YF-translations.csv"
--ex_limit 5 \
--ages "./data/input/ages.csv" \
--snip3 0 \
--snip5 0
Python API (with default parameters)
from pulldown import y_call
y_call(
bam_list="samples.tsv",
initial=0,
final=0,
base_qual=20,
map_qual=25,
k=3,
database="YFull",
reference_genome="hg38",
width=60,
height=80,
transitions="N",
translation="./data/input/YF-translations.csv",
ex_limit=5,
ages="./data/input/ages.csv",
snip3=0,
snip5=0
)
Input data
From the root directory, y_call expects the following organisation:
data/
input/
<database>/
snps.csv
tree.csv
mm.tsv
haps.csv
ages.csv
YF-translations.csv
Output data
After execution, y_call generates the following output structure:
data/
output/
markers/<idd>/
snps_<iid>.tsv
snps_der_<iid>.tsv
haplogroups/<iid>/
haplogroup_<iid>.tsv
trees/<iid>/
tree_output_<iid>.txt
temp/
<iid>.tsv
hap_ages.csv
paths.txt
scores.csv
simple_tree.nwk
snps_branch.csv
figures/
Y-haplogroup_tree.jpg
Y-haplogroup_tree.pdf
To better understand the content of every output file, refer to the Jupyter Vignette stored in: https://github.com/EricGH-018/y_call/blob/master/Jupyter_Vignette/y_call_vignette.ipynb. The different files genertaed contain:
data/output/markers/<iid>/snps_<iid>.tsv— SNP information: SNP-ID, chrom, pos, ref allele, alt allele, Y-haplogroup, YFull translation, Level, ACGT read counts, ref and alt read counts.data/output/markers/<iid>/snps_<iid>.tsv— SNP information (only for variants where alt count > ref count): SNP-ID, chrom, pos, ref allele, alt allele, Y-haplogroup, YFull translation, Level, ACGT read counts, ref and alt read counts.data/output/haplogroups/<iid>/haplogroup_<iid>.tsv— haplogroup scoring table: Y-haplogroup, Level, Total number of SNPs, SNPs supporting ancestral state, SNPs supporting alternative state, SNPs uncovered, SNPs supporting ancestral alleles in nodes until root, SNPs supporting alternative alleles in nodes until root, SNPs uncovered until root, Total number of SNPs until root, Proportion of derived SNPs compared to Total of SNPs, Ratio of ancestral SNPs and derived SNPs, Supporting nodes until Y-haplogroup and Score.data/output/trees/<iid>/tree_output_<iid>.txt— tree file for an individual sample, using ASCII characters and indicating Alternative, Ancestral, Total SNPs, Alternative in par., Ancestral in par., Total in par. or nodes Not Found.data/output/hap_ages.csv— Formed and TMRCA ages for every Y-haplogroup call.data/output/paths.csv— List of nodes until root for assigned Y-haplogroups.data/output/scores.txt— Summary statistics for Y-haplogroup calls: iid, Y-haplogroup, Level, Ratio Anc in par./Der in par., Score.data/output/simple_tree.nwk— phylogenetic tree in newick format with branch lengths as average number of alternative SNPs positions.data/output/snps_branch.csv— total number of alternative SNPs found per node.figures/Y-haplogroup_tree.*— image of the phylogenetic tree (in .jpg and .pdf) in simple_tree.nwk.
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