tools for reading, writing, merging, and remapping SNPs
tools for reading, writing, merging, and remapping SNPs 🧬
Input / Output
- Read raw data (genotype) files from a variety of direct-to-consumer (DTC) DNA testing sources with a SNPs object
- Read and write VCF files (e.g., convert 23andMe to VCF)
- Merge raw data files from different DNA tests, identifying discrepant SNPs in the process with a SNPsCollection object
- Read data in a variety of formats (e.g., files, bytes, compressed with gzip or zip)
- Handle several variations of file types, validated via openSNP parsing analysis
Build / Assembly Detection and Remapping
- Detect the build / assembly of SNPs (supports builds 36, 37, and 38)
- Remap SNPs between builds / assemblies
- Fix several common issues when loading SNPs
- Sort SNPs based on chromosome and position
- Deduplicate RSIDs
- Deduplicate alleles in the non-PAR regions of the X and Y chromosomes for males
- Assign PAR SNPs to the X or Y chromosome
Supported Genotype Files
snps supports VCF files and genotype files from the following DNA testing sources:
- Código 46
- Family Tree DNA
- Genes for Good
- Sano Genetics
Additionally, snps can read a variety of “generic” CSV and TSV files.
$ pip install snps
Download Example Data
First, let’s setup logging to get some helpful output:
>>> import logging, sys >>> logger = logging.getLogger() >>> logger.setLevel(logging.INFO) >>> logger.addHandler(logging.StreamHandler(sys.stdout))
Now we’re ready to download some example data from openSNP:
>>> from snps.resources import Resources >>> r = Resources() >>> paths = r.download_example_datasets() Downloading resources/662.23andme.340.txt.gz Downloading resources/662.ftdna-illumina.341.csv.gz
Load Raw Data
Load a 23andMe raw data file:
>>> from snps import SNPs >>> s = SNPs('resources/662.23andme.340.txt.gz')
The SNPs class accepts a path to a file or a bytes object. A Reader class attempts to infer the data source and load the SNPs. The loaded SNPs are available via a pandas.DataFrame:
>>> df = s.snps >>> df.columns.values array(['chrom', 'pos', 'genotype'], dtype=object) >>> df.index.name 'rsid' >>> len(df) 991786
snps also attempts to detect the build / assembly of the data:
>>> s.build 37 >>> s.build_detected True >>> s.assembly 'GRCh37'
Let’s remap the SNPs to change the assembly / build:
>>> s.snps.loc["rs3094315"].pos 752566 >>> chromosomes_remapped, chromosomes_not_remapped = s.remap_snps(38) Downloading resources/GRCh37_GRCh38.tar.gz >>> s.build 38 >>> s.assembly 'GRCh38' >>> s.snps.loc["rs3094315"].pos 817186
SNPs can be remapped between Build 36 (NCBI36), Build 37 (GRCh37), and Build 38 (GRCh38).
Merge Raw Data Files
The dataset consists of raw data files from two different DNA testing sources. Let’s combine these files using a SNPsCollection.
>>> from snps import SNPsCollection >>> sc = SNPsCollection("resources/662.ftdna-illumina.341.csv.gz", name="User662") Loading resources/662.ftdna-illumina.341.csv.gz >>> sc.build 36 >>> chromosomes_remapped, chromosomes_not_remapped = sc.remap_snps(37) Downloading resources/NCBI36_GRCh37.tar.gz >>> sc.snp_count 708092
As the data gets added, it’s compared to the existing data, and SNP position and genotype discrepancies are identified. (The discrepancy thresholds can be tuned via parameters.)
>>> sc.load_snps(["resources/662.23andme.340.txt.gz"], discrepant_genotypes_threshold=300) Loading resources/662.23andme.340.txt.gz 27 SNP positions were discrepant; keeping original positions 151 SNP genotypes were discrepant; marking those as null >>> len(sc.discrepant_snps) # SNPs with discrepant positions and genotypes, dropping dups 169 >>> sc.snp_count 1006960
Ok, so far we’ve remapped the SNPs to the same build and merged the SNPs from two files, identifying discrepancies along the way. Let’s save the merged dataset consisting of over 1M+ SNPs to a CSV file:
>>> saved_snps = sc.save_snps() Saving output/User662_GRCh37.csv
Moreover, let’s get the reference sequences for this assembly and save the SNPs as a VCF file:
>>> saved_snps = sc.save_snps(vcf=True) Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.1.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.2.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.3.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.4.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.5.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.6.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.7.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.8.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.9.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.10.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.11.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.12.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.13.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.14.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.15.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.16.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.17.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.18.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.19.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.20.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.21.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.22.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.X.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.Y.fa.gz Downloading resources/fasta/GRCh37/Homo_sapiens.GRCh37.dna.chromosome.MT.fa.gz Saving output/User662_GRCh37.vcf
All output files are saved to the output directory.
Documentation is available here.
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