snps

tools for reading, writing, merging, and remapping SNPs

snps

tools for reading, writing, merging, and remapping SNPs 🧬

snps strives to be an easy-to-use and accessible open-source library for working with genotype data

Features

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
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

Data Cleaning

Perform quality control (QC) / filter low quality SNPs based on chip clusters
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
Deduplicate alleles on MT
Assign PAR SNPs to the X or Y chromosome

Analysis

Derive sex from SNPs
Detect deduced genotype / chip array and chip version based on chip clusters
Predict ancestry from SNPs (when installed with ezancestry)

Supported Genotype Files

snps supports VCF files and genotype files from the following DNA testing sources:

Additionally, snps can read a variety of “generic” CSV and TSV files.

Dependencies

snps requires Python 3.8+ and the following Python packages:

Installation

snps is available on the Python Package Index. Install snps (and its required Python dependencies) via pip:

$ pip install snps

For ancestry prediction capability, snps can be installed with ezancestry:

$ pip install snps[ezancestry]

Examples

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")
>>> s.source
'23andMe'
>>> s.count
991786

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 normalized and available via a pandas.DataFrame:

>>> df = s.snps
>>> df.columns.values
array(['chrom', 'pos', 'genotype'], dtype=object)
>>> df.index.name
'rsid'
>>> df.chrom.dtype.name
'object'
>>> df.pos.dtype.name
'uint32'
>>> df.genotype.dtype.name
'object'
>>> len(df)
991786

snps also attempts to detect the build / assembly of the data:

>>> s.build
37
>>> s.build_detected
True
>>> s.assembly
'GRCh37'

Merge Raw Data Files

The dataset consists of raw data files from two different DNA testing sources - let’s combine these files. Specifically, we’ll update the SNPs object with SNPs from a Family Tree DNA file.

>>> merge_results = s.merge([SNPs("resources/662.ftdna-illumina.341.csv.gz")])
Merging SNPs('662.ftdna-illumina.341.csv.gz')
SNPs('662.ftdna-illumina.341.csv.gz') has Build 36; remapping to Build 37
Downloading resources/NCBI36_GRCh37.tar.gz
27 SNP positions were discrepant; keeping original positions
151 SNP genotypes were discrepant; marking those as null
>>> s.source
'23andMe, FTDNA'
>>> s.count
1006960
>>> s.build
37
>>> s.build_detected
True

If the SNPs being merged have a build that differs from the destination build, the SNPs to merge will be remapped automatically. After this example merge, the build is still detected, since the build was detected for all SNPs objects that were merged.

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.) These discrepant SNPs are available for inspection after the merge via properties of the SNPs object.

>>> len(s.discrepant_merge_genotypes)
151

Additionally, any non-called / null genotypes will be updated during the merge, if the file being merged has a called genotype for the SNP.

Moreover, merge takes a chrom parameter - this enables merging of only SNPs associated with the specified chromosome (e.g., “Y” or “MT”).

Finally, merge returns a list of dict, where each dict has information corresponding to the results of each merge (e.g., SNPs in common).

>>> sorted(list(merge_results[0].keys()))
['common_rsids', 'discrepant_genotype_rsids', 'discrepant_position_rsids', 'merged']
>>> merge_results[0]["merged"]
True
>>> len(merge_results[0]["common_rsids"])
692918

Remap SNPs

Now, let’s remap the merged SNPs to change the assembly / build:

>>> s.snps.loc["rs3094315"].pos
752566
>>> chromosomes_remapped, chromosomes_not_remapped = s.remap(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).

Save SNPs

Ok, so far we’ve merged the SNPs from two files (ensuring the same build in the process and identifying discrepancies along the way). Then, we remapped the SNPs to Build 38. Now, let’s save the merged and remapped dataset consisting of 1M+ SNPs to a tab-separated values (TSV) file:

>>> saved_snps = s.to_tsv("out.txt")
Saving output/out.txt
>>> print(saved_snps)
output/out.txt

Moreover, let’s get the reference sequences for this assembly and save the SNPs as a VCF file:

>>> saved_snps = s.to_vcf("out.vcf")
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.1.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.2.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.3.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.4.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.5.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.6.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.7.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.8.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.9.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.10.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.11.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.12.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.13.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.14.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.15.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.16.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.17.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.18.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.19.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.20.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.21.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.22.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.X.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.Y.fa.gz
Downloading resources/fasta/GRCh38/Homo_sapiens.GRCh38.dna.chromosome.MT.fa.gz
Saving output/out.vcf
1 SNP positions were found to be discrepant when saving VCF

When saving a VCF, if any SNPs have positions outside of the reference sequence, they are marked as discrepant and are available via a property of the SNPs object.

All output files are saved to the output directory.

Documentation

Documentation is available here.

Acknowledgements

Thanks to Mike Agostino, Padma Reddy, Kevin Arvai, openSNP, Open Humans, and Sano Genetics.

snps incorporates code and concepts generated with the assistance of OpenAI’s ChatGPT (GPT-3.5). ✨

Output Files

The various output files produced by snps are detailed below. Output files are saved in the output directory, which is defined at the instantiation of a SNPs object.

Save SNPs

SNPs can be saved with SNPs.save. By default, one tab-separated .txt or .vcf file (vcf=True) is output when SNPs are saved. If comma is specified as the separator (sep=","), the default extension is .csv.

The content of non-VCF files (after comment lines, which start with #) is as follows:

Column	Description
rsid	SNP ID
chromosome	Chromosome of SNP
position	Position of SNP
genotype	Genotype of SNP

When filename is not specified, default filenames are used as described below.

`SNPs.save`

<source>_<assembly>.txt / <source>_<assembly>.csv

Where source is the detected source(s) of SNPs data and assembly is the assembly of the SNPs being saved.

Installation

snps is available on the Python Package Index. Install snps (and its required Python dependencies) via pip:

$ pip install snps

Installation and Usage on a Raspberry Pi

The instructions below provide the steps to install snps on a Raspberry Pi (tested with “Raspberry Pi OS (32-bit) Lite”, release date 2020-08-20). For more details about Python on the Raspberry Pi, see here.

Note

Text after a prompt (e.g., $) is the command to type at the command line. The instructions assume a fresh install of Raspberry Pi OS and that after logging in as the pi user, the current working directory is /home/pi.

Install pip for Python 3:
```
pi@raspberrypi:~ $ sudo apt install python3-pip
```
Press “y” followed by “enter” to continue. This enables us to install packages from the Python Package Index.
Install the venv module:
```
pi@raspberrypi:~ $ sudo apt install python3-venv
```
Press “y” followed by “enter” to continue. This enables us to create a virtual environment to isolate the snps installation from other system Python packages.
Install ATLAS:
```
pi@raspberrypi:~ $ sudo apt install libatlas-base-dev
```
Press “y” followed by “enter” to continue. This is required for NumPy, a dependency of snps.

Create a directory for snps and change working directory:

pi@raspberrypi:~ $ mkdir snps
pi@raspberrypi:~ $ cd snps

Create a virtual environment for snps:
```
pi@raspberrypi:~/snps $ python3 -m venv .venv
```
The virtual environment is located at /home/pi/snps/.venv.
Activate the virtual environment:
```
pi@raspberrypi:~/snps $ source .venv/bin/activate
```
Now when you invoke Python or pip, the virtual environment’s version will be used (as indicated by the (.venv) before the prompt). This can be verified as follows:
```
(.venv) pi@raspberrypi:~/snps $ which python
/home/pi/snps/.venv/bin/python
```

Install snps:

(.venv) pi@raspberrypi:~/snps $ pip install snps

Start Python:

(.venv) pi@raspberrypi:~/snps $ python
Python 3.7.3 (default, Jul 25 2020, 13:03:44)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

Use snps; examples shown in the README should now work.

At completion of usage, the virtual environment can be deactivated:

(.venv) pi@raspberrypi:~/snps $ deactivate
pi@raspberrypi:~/snps $

snps Banner

The snps banner is composed of nucleotides from GRCh38 mitochondrial DNA. SNPs are represented by lighter colored nucleotides.

The SVG file was modified from a version created with macSVG.

The PNG file was exported from macSVG.

The color scheme was generated by ColorBrewer.

SNPs

The SNPs highlighted in the banner were identified with the UCSC Genome Browser. The dbSNP accessions for the SNPs are as follows:

rs3883917
rs370271105
rs3087742
rs369034419
rs147830800
rs369070397
rs144402189
rs139684161
rs375589100
rs370482130
rs62581312
rs117135796
rs370716192
rs41473347
rs113913230
rs368807878
rs371543232
rs2857291
rs72619362
rs372099630
rs3135032
rs369669319
rs368534078
rs372889209
rs372439069
rs41531144
rs372946833
rs41323649
rs368463610
rs3937037
rs375896687
rs145412228
rs376013487
rs372529808
rs41334645
rs372003323
rs41400048
rs2853515
rs201801609
rs41528348
rs3927813
rs66492218
rs371975106
rs373732637
rs117394573
rs3883865
rs28678375

References

Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1; 29(1):308-11.
Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD): National Center for Biotechnology Information, National Library of Medicine. dbSNP accession: <listed above> (dbSNP Build ID: 142). Available from: http://www.ncbi.nlm.nih.gov/SNP/

Changelog

The changelog is maintained here: https://github.com/apriha/snps/releases

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

Bug reports

When reporting a bug please include:

Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.

Documentation improvements

snps could always use more documentation, whether as part of the official snps docs, in docstrings, or even on the web in blog posts, articles, and such. See below for info on how to generate documentation.

Feature requests and feedback

The best way to send feedback is to file an issue at https://github.com/apriha/snps/issues.

If you are proposing a feature:

Explain in detail how it would work.
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that code contributions are welcome :)

Development

To set up snps for local development:

Fork snps (look for the “Fork” button).

Clone your fork locally:

$ git clone git@github.com:your_name_here/snps.git

Create a branch for local development from the develop branch:

$ cd snps
$ git checkout develop
$ git checkout -b name-of-your-bugfix-or-feature develop

Setup a development environment:

$ pip install pipenv
$ pipenv install --dev

When you’re done making changes, run all the tests with:
```
$ pipenv run pytest --cov-report=html --cov=snps tests
```
Note

Downloads during tests are disabled by default. To enable downloads, set the environment variable DOWNLOADS_ENABLED=true.

Note

If you receive errors when running the tests, you may need to specify the temporary directory with an environment variable, e.g., TMPDIR="/path/to/tmp/dir".

Note

After running the tests, a coverage report can be viewed by opening htmlcov/index.html in a browser.
Check code formatting:
```
$ pipenv run black --check --diff .
```

Commit your changes and push your branch to GitHub:

$ git add .
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

Submit a pull request through the GitHub website.

Pull request guidelines

If you need some code review or feedback while you’re developing the code, just make the pull request.

For merging, you should:

Ensure tests pass.
Update documentation when there’s new API, functionality, etc.
Add yourself to CONTRIBUTORS.rst if you’d like.

Documentation

After the development environment has been setup, documentation can be generated via the following command:

$ pipenv run sphinx-build -T -E -D language=en docs docs/_build

Then, the documentation can be viewed by opening docs/_build/index.html in a browser.

Contributors

Contributors to snps are listed below.

Core Developers

Name	GitHub
Andrew Riha	@apriha
Will Jones	@willgdjones

Other Contributors

Listed in alphabetical order.

Name	GitHub
Alan Moffet	@amoffet
Anatoli Babenia	@abitrolly
Castedo Ellerman	@castedo
Gerard Manning	@GerardManning
Julian Runnels	@JulianRunnels
Kevin Arvai	@arvkevi
Phil Palmer	@PhilPalmer
Yoan Bouzin

Code Documentation

SNPs

SNPs reads, writes, merges, and remaps genotype / raw data files.

class snps.snps.SNPs(file='', only_detect_source=False, assign_par_snps=False, output_dir='output', resources_dir='resources', deduplicate=True, deduplicate_XY_chrom=True, deduplicate_MT_chrom=True, parallelize=False, processes=2, rsids=())[source]

Bases: object

__init__(file='', only_detect_source=False, assign_par_snps=False, output_dir='output', resources_dir='resources', deduplicate=True, deduplicate_XY_chrom=True, deduplicate_MT_chrom=True, parallelize=False, processes=2, rsids=())[source]

Object used to read, write, and remap genotype / raw data files.

Parameters:

file (str or bytes) – path to file to load or bytes to load
only_detect_source (bool) – only detect the source of the data
assign_par_snps (bool) – assign PAR SNPs to the X and Y chromosomes
output_dir (str) – path to output directory
resources_dir (str) – name / path of resources directory
deduplicate (bool) – deduplicate RSIDs and make SNPs available as SNPs.duplicate
deduplicate_MT_chrom (bool) – deduplicate alleles on MT; see SNPs.heterozygous_MT
deduplicate_XY_chrom (bool or str) – deduplicate alleles in the non-PAR regions of X and Y for males; see SNPs.discrepant_XY if a str then this is the sex determination method to use X Y or XY
parallelize (bool) – utilize multiprocessing to speedup calculations
processes (int) – processes to launch if multiprocessing
rsids (tuple, optional) – rsids to extract if loading a VCF file

property assembly

Assembly of SNPs.

Return type:: str

property build

Build of SNPs.

Return type:: int

property build_detected

Status indicating if build of SNPs was detected.

Return type:: bool

property chip

Detected deduced genotype / chip array, if any, per compute_cluster_overlap.

Returns:: detected chip array, else empty str
Return type:: str

property chip_version

Detected genotype / chip array version, if any, per compute_cluster_overlap.

Notes

Chip array version is only applicable to 23andMe (v3, v4, v5) and AncestryDNA (v1, v2) files.

Returns:: detected chip array version, e.g., ‘v4’, else empty str
Return type:: str

property chromosomes

Chromosomes of SNPs.

Returns:: list of str chromosomes (e.g., [‘1’, ‘2’, ‘3’, ‘MT’], empty list if no chromosomes
Return type:: list

property chromosomes_summary

Summary of the chromosomes of SNPs.

Returns:: human-readable listing of chromosomes (e.g., ‘1-3, MT’), empty str if no chromosomes
Return type:: str

property cluster

Detected chip cluster, if any, per compute_cluster_overlap.

Notes

Refer to compute_cluster_overlap for more details about chip clusters.

Returns:: detected chip cluster, e.g., ‘c1’, else empty str
Return type:: str

compute_cluster_overlap(cluster_overlap_threshold=0.95)[source]

Compute overlap with chip clusters.

Chip clusters, which are defined in [1], are associated with deduced genotype / chip arrays and DTC companies.

This method also sets the values returned by the cluster, chip, and chip_version properties, based on max overlap, if the specified threshold is satisfied.

Parameters:

cluster_overlap_threshold (float) – threshold for cluster to overlap this SNPs object, and vice versa, to set values returned by the cluster, chip, and chip_version properties

Returns:

pandas.DataFrame with the following columns:

company_composition: DTC company composition of associated cluster from [1]
chip_base_deduced: deduced genotype / chip array of associated cluster from [1]
snps_in_cluster: count of SNPs in cluster
snps_in_common: count of SNPs in common with cluster (inner merge with cluster)
overlap_with_cluster: percentage overlap of snps_in_common with cluster
overlap_with_self: percentage overlap of snps_in_common with this SNPs object

Return type:

pandas.DataFrame

References

property count

Count of SNPs.

Return type:: int

detect_build()[source]

Detect build of SNPs.

Use the coordinates of common SNPs to identify the build / assembly of a genotype file that is being loaded.

Notes

rs3094315 : plus strand in 36, 37, and 38
rs11928389 : plus strand in 36, minus strand in 37 and 38
rs2500347 : plus strand in 36 and 37, minus strand in 38
rs964481 : plus strand in 36, 37, and 38
rs2341354 : plus strand in 36, 37, and 38
rs3850290 : plus strand in 36, 37, and 38
rs1329546 : plus strand in 36, 37, and 38

Returns:: detected build of SNPs, else 0
Return type:: int

References

Yates et. al. (doi:10.1093/bioinformatics/btu613), http://europepmc.org/search/?query=DOI:10.1093/bioinformatics/btu613
Zerbino et. al. (doi.org/10.1093/nar/gkx1098), https://doi.org/10.1093/nar/gkx1098
Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1;29(1):308-11.
Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD): National Center for Biotechnology Information, National Library of Medicine. dbSNP accession: rs3094315, rs11928389, rs2500347, rs964481, rs2341354, rs3850290, and rs1329546 (dbSNP Build ID: 151). Available from: http://www.ncbi.nlm.nih.gov/SNP/

determine_sex(heterozygous_x_snps_threshold=0.03, y_snps_not_null_threshold=0.3, chrom='X')[source]

Determine sex from SNPs using thresholds.

Parameters:

heterozygous_x_snps_threshold (float) – percentage heterozygous X SNPs; above this threshold, Female is determined
y_snps_not_null_threshold (float) – percentage Y SNPs that are not null; above this threshold, Male is determined
chrom ({“X”, “Y”}) – use X or Y chromosome SNPs to determine sex

Returns:

‘Male’ or ‘Female’ if detected, else empty str

Return type:

str

property discrepant_XY

Discrepant XY SNPs.

A discrepant XY SNP is a heterozygous SNP in the non-PAR region of the X or Y chromosome found during deduplication for a detected male genotype.

Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

property discrepant_merge_genotypes

SNPs with discrepant genotypes discovered while merging SNPs.

Notes

Definitions of columns in this dataframe are as follows:

Column	Description
rsid	SNP ID
chrom	Chromosome of existing SNP
pos	Position of existing SNP
genotype	Genotype of existing SNP
chrom_added	Chromosome of added SNP
pos_added	Position of added SNP
genotype_added	Genotype of added SNP (discrepant with genotype)

Return type:: pandas.DataFrame

property discrepant_merge_positions

SNPs with discrepant positions discovered while merging SNPs.

Notes

Definitions of columns in this dataframe are as follows:

Column	Description
rsid	SNP ID
chrom	Chromosome of existing SNP
pos	Position of existing SNP
genotype	Genotype of existing SNP
chrom_added	Chromosome of added SNP
pos_added	Position of added SNP (discrepant with pos)
genotype_added	Genotype of added SNP

Return type:: pandas.DataFrame

property discrepant_merge_positions_genotypes

SNPs with discrepant positions and / or genotypes discovered while merging SNPs.

Notes

Definitions of columns in this dataframe are as follows:

Column	Description
rsid	SNP ID
chrom	Chromosome of existing SNP
pos	Position of existing SNP
genotype	Genotype of existing SNP
chrom_added	Chromosome of added SNP
pos_added	Position of added SNP (possibly discrepant with pos)
genotype_added	Genotype of added SNP (possibly discrepant with genotype)

Return type:: pandas.DataFrame

property discrepant_vcf_position

SNPs with discrepant positions discovered while saving VCF.

Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

property duplicate

Duplicate SNPs.

A duplicate SNP has the same RSID as another SNP. The first occurrence of the RSID is not considered a duplicate SNP.

Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

get_count(chrom='')[source]

Count of SNPs.

Parameters:: chrom (str, optional) – chromosome (e.g., “1”, “X”, “MT”)
Return type:: int

static get_par_regions(build)[source]

Get PAR regions for the X and Y chromosomes.

Parameters:: build (int) – build of SNPs
Returns:: PAR regions for the given build
Return type:: pandas.DataFrame

References

Genome Reference Consortium, https://www.ncbi.nlm.nih.gov/grc/human
Yates et. al. (doi:10.1093/bioinformatics/btu613), http://europepmc.org/search/?query=DOI:10.1093/bioinformatics/btu613
Zerbino et. al. (doi.org/10.1093/nar/gkx1098), https://doi.org/10.1093/nar/gkx1098

heterozygous(chrom='')[source]

Get heterozygous SNPs.

Parameters:: chrom (str, optional) – chromosome (e.g., “1”, “X”, “MT”)
Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

property heterozygous_MT

Heterozygous SNPs on the MT chromosome found during deduplication.

Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

homozygous(chrom='')[source]

Get homozygous SNPs.

Parameters:: chrom (str, optional) – chromosome (e.g., “1”, “X”, “MT”)
Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

identify_low_quality_snps()[source]

Identify low quality SNPs based on chip clusters.

Any low quality SNPs are removed from the snps_qc dataframe and are made available as low_quality.

Notes

Chip clusters, which are defined in [1], are associated with low quality SNPs. As such, low quality SNPs will only be identified when this SNPs object corresponds to a cluster per compute_cluster_overlap().

property low_quality

SNPs identified as low quality, if any, per identify_low_quality_snps().

Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

merge(snps_objects=(), discrepant_positions_threshold=100, discrepant_genotypes_threshold=500, remap=True, chrom='')[source]

Merge other SNPs objects into this SNPs object.

Parameters:

snps_objects (list or tuple of SNPs) – other SNPs objects to merge into this SNPs object
discrepant_positions_threshold (int) – threshold for discrepant SNP positions between existing data and data to be loaded; a large value could indicate mismatched genome assemblies
discrepant_genotypes_threshold (int) – threshold for discrepant genotype data between existing data and data to be loaded; a large value could indicated mismatched individuals
remap (bool) – if necessary, remap other SNPs objects to have the same build as this SNPs object before merging
chrom (str, optional) – chromosome to merge (e.g., “1”, “Y”, “MT”)

Returns:

for each SNPs object to merge, a dict with the following items:

merged (bool): whether SNPs object was merged
common_rsids (pandas.Index): SNPs in common
discrepant_position_rsids (pandas.Index): SNPs with discrepant positions
discrepant_genotype_rsids (pandas.Index): SNPs with discrepant genotypes

Return type:

list of dict

References

Fluent Python by Luciano Ramalho (O’Reilly). Copyright 2015 Luciano Ramalho, 978-1-491-94600-8.

notnull(chrom='')[source]

Get not null genotype SNPs.

Parameters:: chrom (str, optional) – chromosome (e.g., “1”, “X”, “MT”)
Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

property phased

Indicates if genotype is phased.

Return type:: bool

predict_ancestry(output_directory=None, write_predictions=False, models_directory=None, aisnps_directory=None, aisnps_set=None)[source]

Predict genetic ancestry for SNPs.

Predictions by ezancestry.

Notes

Populations below are described here.

Parameters:

various (optional) – See the available settings for predict at ezancestry.

Returns:

dict with the following keys:

population_code (str)

max predicted population for the sample

population_percent (float)

predicted probability for the max predicted population

superpopulation_code (str)

max predicted super population (continental) for the sample

superpopulation_percent (float)

predicted probability for the max predicted super population

ezancestry_df (pandas.DataFrame)

pandas.DataFrame with the following columns:

component1, component2, component3: The coordinates of the sample in the dimensionality-reduced component space. Can be used as (x, y, z,) coordinates for plotting in a 3d scatter plot.
predicted_ancestry_population: The max predicted population for the sample.
ACB, ASW, BEB, CDX, CEU, CHB, CHS, CLM, ESN, FIN, GBR, GIH, GWD, IBS, ITU, JPT, KHV, LWK, MSL, MXL, PEL, PJL, PUR, STU, TSI, YRI: Predicted probabilities for each of the populations. These sum to 1.0.
predicted_ancestry_superpopulation: The max predicted super population (continental) for the sample.
AFR, AMR, EAS, EUR, SAS: Predicted probabilities for each of the super populations. These sum to 1.0.

Return type:

dict

remap(target_assembly, complement_bases=True)[source]

Remap SNP coordinates from one assembly to another.

This method uses the assembly map endpoint of the Ensembl REST API service (via Resources’s EnsemblRestClient) to convert SNP coordinates / positions from one assembly to another. After remapping, the coordinates / positions for the SNPs will be that of the target assembly.

If the SNPs are already mapped relative to the target assembly, remapping will not be performed.

Parameters:

target_assembly ({‘NCBI36’, ‘GRCh37’, ‘GRCh38’, 36, 37, 38}) – assembly to remap to
complement_bases (bool) – complement bases when remapping SNPs to the minus strand

Returns:

chromosomes_remapped (list of str) – chromosomes remapped
chromosomes_not_remapped (list of str) – chromosomes not remapped

Notes

An assembly is also know as a “build.” For example:

Assembly NCBI36 = Build 36 Assembly GRCh37 = Build 37 Assembly GRCh38 = Build 38

See https://www.ncbi.nlm.nih.gov/assembly for more information about assemblies and remapping.

References

Ensembl, Assembly Map Endpoint, http://rest.ensembl.org/documentation/info/assembly_map
Yates et. al. (doi:10.1093/bioinformatics/btu613), http://europepmc.org/search/?query=DOI:10.1093/bioinformatics/btu613
Zerbino et. al. (doi.org/10.1093/nar/gkx1098), https://doi.org/10.1093/nar/gkx1098

property sex

Sex derived from SNPs.

Returns:: ‘Male’ or ‘Female’ if detected, else empty str
Return type:: str

property snps

Normalized SNPs.

Notes

Throughout snps, the “normalized snps dataframe” is defined as follows:

Column	Description	pandas dtype
rsid [*]	SNP ID	object (string)
chrom	Chromosome of SNP	object (string)
pos	Position of SNP (relative to build)	uint32
genotype [†]	Genotype of SNP	object (string)

Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

property snps_qc

Normalized SNPs, after quality control.

Any low quality SNPs, identified per identify_low_quality_snps(), are not included in the result.

Returns:: normalized snps dataframe
Return type:: pandas.DataFrame

sort()[source]: Sort SNPs based on ordered chromosome list and position.

property source

Summary of the SNP data source(s).

Returns:: Data source(s) for this SNPs object, separated by “, “.
Return type:: str

property summary

Summary of SNPs.

Returns:: summary info if SNPs is valid, else {}
Return type:: dict

to_csv(filename='', atomic=True, **kwargs)[source]

Output SNPs as comma-separated values.

Parameters:

filename (str or buffer) – filename for file to save or buffer to write to
atomic (bool) – atomically write output to a file on local filesystem
**kwargs – additional parameters to pandas.DataFrame.to_csv

Returns:

path to file in output directory if SNPs were saved, else empty str

Return type:

str

to_tsv(filename='', atomic=True, **kwargs)[source]

Output SNPs as tab-separated values.

Note that this results in the same default output as save.

Parameters:

filename (str or buffer) – filename for file to save or buffer to write to
atomic (bool) – atomically write output to a file on local filesystem
**kwargs – additional parameters to pandas.DataFrame.to_csv

Returns:

path to file in output directory if SNPs were saved, else empty str

Return type:

str

to_vcf(filename='', atomic=True, alt_unavailable='.', chrom_prefix='', qc_only=False, qc_filter=False, **kwargs)[source]

Output SNPs as Variant Call Format.

Parameters:

filename (str or buffer) – filename for file to save or buffer to write to
atomic (bool) – atomically write output to a file on local filesystem
alt_unavailable (str) – representation of ALT allele when ALT is not able to be determined
chrom_prefix (str) – prefix for chromosomes in VCF CHROM column
qc_only (bool) – output only SNPs that pass quality control
qc_filter (bool) – populate FILTER column based on quality control results
**kwargs – additional parameters to pandas.DataFrame.to_csv

Returns:

path to file in output directory if SNPs were saved, else empty str

Return type:

str

Notes

Parameters qc_only and qc_filter, if true, will identify low quality SNPs per identify_low_quality_snps(), if not done already. Moreover, these parameters have no effect if this SNPs object does not map to a cluster per compute_cluster_overlap().

References

The Variant Call Format (VCF) Version 4.2 Specification, 8 Mar 2019, https://samtools.github.io/hts-specs/VCFv4.2.pdf

property unannotated_vcf

Indicates if VCF file is unannotated.

Return type:: bool

property valid

Determine if SNPs is valid.

SNPs is valid when the input file has been successfully parsed.

Returns:: True if SNPs is valid
Return type:: bool

snps.ensembl

Ensembl REST client.

Notes

Modified from https://github.com/Ensembl/ensembl-rest/wiki/Example-Python-Client.

References

Yates et. al. (doi:10.1093/bioinformatics/btu613), http://europepmc.org/search/?query=DOI:10.1093/bioinformatics/btu613
Zerbino et. al. (doi.org/10.1093/nar/gkx1098), https://doi.org/10.1093/nar/gkx1098

class snps.ensembl.EnsemblRestClient(server='https://rest.ensembl.org', reqs_per_sec=15)[source]

Bases: object

__init__(server='https://rest.ensembl.org', reqs_per_sec=15)[source]

perform_rest_action(endpoint, hdrs=None, params=None)[source]

snps.io

Classes for reading and writing SNPs.

snps.io.reader

Class for reading SNPs.

class snps.io.reader.Reader(file='', only_detect_source=False, resources=None, rsids=())[source]

Bases: object

Class for reading and parsing raw data / genotype files.

__init__(file='', only_detect_source=False, resources=None, rsids=())[source]

Initialize a Reader.

Parameters:

file (str or bytes) – path to file to load or bytes to load
only_detect_source (bool) – only detect the source of the data
resources (Resources) – instance of Resources
rsids (tuple, optional) – rsids to extract if loading a VCF file

static is_gzip(bytes_data)[source]: Check whether or not a bytes_data file is a valid gzip file.

static is_zip(bytes_data)[source]: Check whether or not a bytes_data file is a valid Zip file.

read()[source]

Read and parse a raw data / genotype file.

Returns:

dict with the following items:

snps (pandas.DataFrame): dataframe of parsed SNPs
source (str): detected source of SNPs
phased (bool): flag indicating if SNPs are phased

Return type:

dict

read_23andme(file, compression, joined=True)[source]

Read and parse 23andMe file.

https://www.23andme.com

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_ancestry(file, compression)[source]

Read and parse Ancestry.com file.

http://www.ancestry.com

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_circledna(file, compression)[source]

Read and parse CircleDNA file.

https://circledna.com/

Notes

This method attempts to read and parse a whole exome file, optionally compressed with gzip or zip. Some assumptions are made throughout this process:

SNPs that are not annotated with an RSID are skipped

Insertions and deletions are skipped

Parameters:: file (str or bytes) – path to file or bytes to load
Returns:: result of read_helper
Return type:: dict

read_dnaland(file, compression)[source]

Read and parse DNA.land files.

https://dna.land/

Parameters:: data (str) – data string
Returns:: result of read_helper
Return type:: dict

read_ftdna(file, compression)[source]

Read and parse Family Tree DNA (FTDNA) file.

https://www.familytreedna.com

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_ftdna_famfinder(file, compression)[source]

Read and parse Family Tree DNA (FTDNA) “famfinder” file.

https://www.familytreedna.com

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_generic(file, compression, skip=1)[source]

Read and parse generic CSV or TSV file.

Notes

Assumes columns are ‘rsid’, ‘chrom’ / ‘chromosome’, ‘pos’ / ‘position’, and ‘genotype’; values are comma separated; unreported genotypes are indicated by ‘–’; and one header row precedes data. For example:

rsid,chromosome,position,genotype rs1,1,1,AA rs2,1,2,CC rs3,1,3,–

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_genes_for_good(file, compression)[source]

Read and parse Genes For Good file.

https://genesforgood.sph.umich.edu/readme/readme1.2.txt

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_gsa(data_or_filename, compresion, comments)[source]

Read and parse Illumina Global Screening Array files

Parameters:: data_or_filename (str or bytes) – either the filename to read from or the bytes data itself
Returns:: result of read_helper
Return type:: dict

read_helper(source, parser)[source]

Generic method to help read files.

Parameters:

source (str) – name of data source
parser (func) – parsing function, which returns a tuple with the following items:

0 (pandas.DataFrame)
dataframe of parsed SNPs (empty if only detecting source)

1 (bool), optional
flag indicating if SNPs are phased

2 (int), optional
detected build of SNPs

Returns:

dict with the following items:

snps (pandas.DataFrame): dataframe of parsed SNPs
source (str): detected source of SNPs
phased (bool): flag indicating if SNPs are phased
build (int): detected build of SNPs

Return type:

dict

References

Fluent Python by Luciano Ramalho (O’Reilly). Copyright 2015 Luciano Ramalho, 978-1-491-94600-8.

read_livingdna(file, compression)[source]

Read and parse LivingDNA file.

https://livingdna.com/

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_mapmygenome(file, compression, header)[source]

Read and parse Mapmygenome file.

https://mapmygenome.in

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_myheritage(file, compression)[source]

Read and parse MyHeritage file.

https://www.myheritage.com

Parameters:: file (str) – path to file
Returns:: result of read_helper
Return type:: dict

read_snps_csv(file, comments, compression)[source]

Read and parse CSV file generated by snps.

https://pypi.org/project/snps/

Parameters:

file (str or buffer) – path to file or buffer to read
comments (str) – comments at beginning of file

Returns:

result of read_helper

Return type:

dict

read_tellmegen(file, compression)[source]

Read and parse tellmeGen files.

https://www.tellmegen.com/

Parameters:: data (str) – data string
Returns:: result of read_helper
Return type:: dict

read_vcf(file, compression, provider, rsids=())[source]

Read and parse VCF file.

Notes

This method attempts to read and parse a VCF file or buffer, optionally compressed with gzip. Some assumptions are made throughout this process:

SNPs that are not annotated with an RSID are skipped

If the VCF contains multiple samples, only the first sample is used to lookup the genotype

Insertions and deletions are skipped

If a sample allele is not specified, the genotype is reported as NaN

If a sample allele refers to a REF or ALT allele that is not specified, the genotype is reported as NaN

Parameters:

file (str or bytes) – path to file or bytes to load
rsids (tuple, optional) – rsids to extract if loading a VCF file

Returns:

result of read_helper

Return type:

dict

snps.io.reader.get_empty_snps_dataframe()[source]

Get empty dataframe normalized for usage with snps.

Return type:: pd.DataFrame

snps.io.writer

Class for writing SNPs.

class snps.io.writer.Writer(snps=None, filename='', vcf=False, atomic=True, vcf_alt_unavailable='.', vcf_chrom_prefix='', vcf_qc_only=False, vcf_qc_filter=False, **kwargs)[source]

Bases: object

Class for writing SNPs to files.

__init__(snps=None, filename='', vcf=False, atomic=True, vcf_alt_unavailable='.', vcf_chrom_prefix='', vcf_qc_only=False, vcf_qc_filter=False, **kwargs)[source]

Initialize a Writer.

Parameters:

snps (SNPs) – SNPs to save to file or write to buffer
filename (str or buffer) – filename for file to save or buffer to write to
vcf (bool) – flag to save file as VCF
atomic (bool) – atomically write output to a file on local filesystem
vcf_alt_unavailable (str) – representation of VCF ALT allele when ALT is not able to be determined
vcf_chrom_prefix (str) – prefix for chromosomes in VCF CHROM column
vcf_qc_only (bool) – for VCF, output only SNPs that pass quality control
vcf_qc_filter (bool) – for VCF, populate VCF FILTER column based on quality control results
**kwargs – additional parameters to pandas.DataFrame.to_csv

write()[source]

Write SNPs to file or buffer.

Returns:

str – path to file in output directory if SNPs were saved, else empty str
discrepant_vcf_position (pd.DataFrame) – SNPs with discrepant positions discovered while saving VCF

snps.resources

Class for downloading and loading required external resources.

References

International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001 Feb 15;409(6822):860-921. http://dx.doi.org/10.1038/35057062
hg19 (GRCh37): Hiram Clawson, Brooke Rhead, Pauline Fujita, Ann Zweig, Katrina Learned, Donna Karolchik and Robert Kuhn, https://genome.ucsc.edu/cgi-bin/hgGateway?db=hg19
Yates et. al. (doi:10.1093/bioinformatics/btu613), http://europepmc.org/search/?query=DOI:10.1093/bioinformatics/btu613
Zerbino et. al. (doi.org/10.1093/nar/gkx1098), https://doi.org/10.1093/nar/gkx1098

class snps.resources.ReferenceSequence(ID='', url='', path='', assembly='', species='', taxonomy='')[source]

Bases: object

Object used to represent and interact with a reference sequence.

property ID

Get reference sequence chromosome.

Return type:: str

__init__(ID='', url='', path='', assembly='', species='', taxonomy='')[source]

Initialize a ReferenceSequence object.

Parameters:

ID (str) – reference sequence chromosome
url (str) – url to Ensembl reference sequence
path (str) – path to local reference sequence
assembly (str) – reference sequence assembly (e.g., “GRCh37”)
species (str) – reference sequence species
taxonomy (str) – reference sequence taxonomy

References

The Variant Call Format (VCF) Version 4.2 Specification, 8 Mar 2019, https://samtools.github.io/hts-specs/VCFv4.2.pdf

property assembly

Get reference sequence assembly.

Return type:: str

property build

Get reference sequence build.

Returns:: e.g., “B37”
Return type:: str

property chrom

Get reference sequence chromosome.

Return type:: str

clear()[source]: Clear reference sequence.

property end

Get reference sequence end position (1-based).

Return type:: int

property length

Get reference sequence length.

Return type:: int

property md5

Get reference sequence MD5 hash.

Return type:: str

property path

Get path to local reference sequence.

Return type:: str

property sequence

Get reference sequence.

Return type:: np.array(dtype=np.uint8)

property species

Get reference sequence species.

Return type:: str

property start

Get reference sequence start position (1-based).

Return type:: int

property taxonomy

Get reference sequence taxonomy.

Return type:: str

property url

Get URL to Ensembl reference sequence.

Return type:: str

class snps.resources.Resources(*args, **kwargs)[source]

Bases: object

Object used to manage resources required by snps.

__init__(resources_dir='resources')[source]

Initialize a Resources object.

Parameters:: resources_dir (str) – name / path of resources directory

download_example_datasets()[source]

Download example datasets from openSNP.

Per openSNP, “the data is donated into the public domain using CC0 1.0.”

Returns:: paths – paths to example datasets
Return type:: list of str or empty str

References

Greshake B, Bayer PE, Rausch H, Reda J (2014), “openSNP-A Crowdsourced Web Resource for Personal Genomics,” PLOS ONE, 9(3): e89204, https://doi.org/10.1371/journal.pone.0089204

get_all_reference_sequences(**kwargs)[source]

Get Homo sapiens reference sequences for Builds 36, 37, and 38 from Ensembl.

Notes

This function can download over 2.5GB of data.

Returns:: dict of ReferenceSequence, else {}
Return type:: dict

get_all_resources()[source]

Get / download all resources used throughout snps.

Notes

This function does not download reference sequences and the openSNP datadump, due to their large sizes.

Returns:: dict of resources
Return type:: dict

get_assembly_mapping_data(source_assembly, target_assembly)[source]

Get assembly mapping data.

Parameters:

source_assembly ({‘NCBI36’, ‘GRCh37’, ‘GRCh38’}) – assembly to remap from
target_assembly ({‘NCBI36’, ‘GRCh37’, ‘GRCh38’}) – assembly to remap to

Returns:

dict of json assembly mapping data if loading was successful, else {}

Return type:

dict

get_chip_clusters()[source]

Get resource for identifying deduced genotype / chip array based on chip clusters.

Return type:: pandas.DataFrame

References

Chang Lu, Bastian Greshake Tzovaras, Julian Gough, A survey of direct-to-consumer genotype data, and quality control tool (GenomePrep) for research, Computational and Structural Biotechnology Journal, Volume 19, 2021, Pages 3747-3754, ISSN 2001-0370, https://doi.org/10.1016/j.csbj.2021.06.040.

get_dbsnp_151_37_reverse()[source]

Get and load RSIDs that are on the reference reverse (-) strand in dbSNP 151 and lower.

Return type:: pandas.DataFrame

References

Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001 Jan 1; 29(1):308-11.
Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD): National Center for Biotechnology Information, National Library of Medicine. (dbSNP Build ID: 151). Available from: http://www.ncbi.nlm.nih.gov/SNP/

get_gsa_chrpos()[source]

Get and load GSA chromosome position map.

https://support.illumina.com/downloads/infinium-global-screening-array-v2-0-product-files.html

Return type:: pandas.DataFrame

get_gsa_resources()[source]

Get resources for reading Global Screening Array files.

https://support.illumina.com/downloads/infinium-global-screening-array-v2-0-product-files.html

Return type:: dict

get_gsa_rsid()[source]

Get and load GSA RSID map.

https://support.illumina.com/downloads/infinium-global-screening-array-v2-0-product-files.html

Return type:: pandas.DataFrame

get_low_quality_snps()[source]

Get listing of low quality SNPs for quality control based on chip clusters.

Return type:: pandas.DataFrame

References

Chang Lu, Bastian Greshake Tzovaras, Julian Gough, A survey of direct-to-consumer genotype data, and quality control tool (GenomePrep) for research, Computational and Structural Biotechnology Journal, Volume 19, 2021, Pages 3747-3754, ISSN 2001-0370, https://doi.org/10.1016/j.csbj.2021.06.040.

get_opensnp_datadump_filenames()[source]

Get filenames internal to the openSNP datadump zip.

Per openSNP, “the data is donated into the public domain using CC0 1.0.”

Notes

This function can download over 27GB of data. If the download is not successful, try using a different tool like wget or curl to download the file and move it to the resources directory (see _get_path_opensnp_datadump).

Returns:: filenames – filenames internal to the openSNP datadump
Return type:: list of str

References

Greshake B, Bayer PE, Rausch H, Reda J (2014), “openSNP-A Crowdsourced Web Resource for Personal Genomics,” PLOS ONE, 9(3): e89204, https://doi.org/10.1371/journal.pone.0089204

get_reference_sequences(assembly='GRCh37', chroms=('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y', 'MT'))[source]

Get Homo sapiens reference sequences for chroms of assembly.

Notes

This function can download over 800MB of data for each assembly.

Parameters:

assembly ({‘NCBI36’, ‘GRCh37’, ‘GRCh38’}) – reference sequence assembly
chroms (list of str) – reference sequence chromosomes

Returns:

dict of ReferenceSequence, else {}

Return type:

dict

load_opensnp_datadump_file(filename)[source]

Load the specified file from the openSNP datadump.

Per openSNP, “the data is donated into the public domain using CC0 1.0.”

Parameters:: filename (str) – filename internal to the openSNP datadump
Returns:: content of specified file internal to the openSNP datadump
Return type:: bytes

References

Greshake B, Bayer PE, Rausch H, Reda J (2014), “openSNP-A Crowdsourced Web Resource for Personal Genomics,” PLOS ONE, 9(3): e89204, https://doi.org/10.1371/journal.pone.0089204

snps.utils

Utility classes and functions.

class snps.utils.Parallelizer(parallelize=False, processes=2)[source]

Bases: object

__init__(parallelize=False, processes=2)[source]

Initialize a Parallelizer.

Parameters:

parallelize (bool) – utilize multiprocessing to speedup calculations
processes (int) – processes to launch if multiprocessing

class snps.utils.Singleton[source]: Bases: type

snps.utils.clean_str(s)[source]

Clean a string so that it can be used as a Python variable name.

Parameters:: s (str) – string to clean
Returns:: string that can be used as a Python variable name
Return type:: str

snps.utils.create_dir(path)[source]

Create directory specified by path if it doesn’t already exist.

Parameters:: path (str) – path to directory
Returns:: True if path exists
Return type:: bool

snps.utils.get_utc_now()[source]

Get current UTC time.

Return type:: datetime.datetime

snps.utils.gzip_file(src, dest)[source]

Gzip a file.

Parameters:

src (str) – path to file to gzip
dest (str) – path to output gzip file

Returns:

path to gzipped file

Return type:

str

snps.utils.save_df_as_csv(df, path, filename, comment='', prepend_info=True, atomic=True, **kwargs)[source]

Save dataframe to a CSV file.

Parameters:

df (pandas.DataFrame) – dataframe to save
path (str) – path to directory where to save CSV file
filename (str or buffer) – filename for file to save or buffer to write to
comment (str) – header comment(s); one or more lines starting with ‘#’
prepend_info (bool) – prepend file generation information as comments
atomic (bool) – atomically write output to a file on local filesystem
**kwargs – additional parameters to pandas.DataFrame.to_csv

Returns:

path to saved file or buffer (empty str if error)

Return type:

str or buffer

snps.utils.zip_file(src, dest, arcname)[source]

Zip a file.

Parameters:

src (str) – path to file to zip
dest (str) – path to output zip file
arcname (str) – name of file in zip archive

Returns:

path to zipped file

Return type:

str

snps

snps

Features

Input / Output

Build / Assembly Detection and Remapping

Data Cleaning

Analysis

Supported Genotype Files

Dependencies

Installation

Examples

Download Example Data

Load Raw Data

Merge Raw Data Files

Remap SNPs

Save SNPs

Documentation

Acknowledgements

Output Files

Save SNPs

SNPs.save

<source>_<assembly>.txt / <source>_<assembly>.csv

Installation

Installation and Usage on a Raspberry Pi

snps Banner

SNPs

References

Changelog

Contributing

Bug reports

Documentation improvements

Feature requests and feedback

Development

Pull request guidelines

Documentation

Contributors

Core Developers

Other Contributors

Code Documentation

SNPs

snps.ensembl

snps.io

snps.io.reader

snps.io.writer

snps.resources

snps.utils

Indices and tables

`SNPs.save`