Metadata-Version: 2.1
Name: zorp
Version: 0.2.0
Summary: ZORP: A helpful GWAS parser
Home-page: https://github.com/abought/zorp
Author: Andy Boughton
Author-email: abought@umich.edu
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/abought/zorp/issues
Project-URL: Source, https://github.com/abought/zorp/
Description: # ZORP: A helpful GWAS parser
        
        [![Build Status](https://api.travis-ci.org/abought/zorp.svg?branch=develop)](https://api.travis-ci.org/abought/zorp)
        
        ## Why?
        ZORP is intended to abstract away differences in file formats, and help you work with GWAS data from many 
        different sources.
        
        - Provide a single unified interface to read text, gzip, or tabixed data
        - Separation of concerns between reading and parsing (with parsers that can handle the most common file formats)
        - Includes helpers to auto-detect data format and filter for variants of interest 
        
        ## Why not?
        ZORP provides a high level abstraction. This means that it is convenient, at the expense of speed.
        
        For GWAS files, ZORP does not sort the data for you, because doing so in python would be quite slow. You will still 
        need to do some basic data preparation before using.
        
        ## Usage
        ### Python
        ```python
        from zorp import readers, parsers
        
        # Create a reader instance. This example specifies each option for clarity, but sniffers are provided to auto-detect 
        #   common format options.
        sample_parser = parsers.GenericGwasLineParser(marker_col=1, pvalue_col=2, is_neg_log_pvalue=True,
                                                      delimiter='\t')
        reader = readers.TabixReader('input.bgz', parser=sample_parser, skip_rows=1, skip_errors=True)
        
        # After parsing the data, values of pre-defined fields can be used to perform lookups for the value of one field
        #  Lookups can be reusable functions with no dependence on zorp
        reader.add_lookup('rsid', lambda variant: some_rsid_finder(variant.chrom, variant.pos, variant.ref, variant.alt))
        
        # Sometimes a more powerful syntax is needed- the ability to look up several fields at once, or clean up parsed data 
        #   in some way unique to this dataset 
        reader.add_transform(lambda variant: mutate_entire_variant(variant))
        
        # We can filter data to the variants of interest. If you use a domain specific parser, columns can be referenced by name
        reader.add_filter('chrom', '19')  # This row must have the specified value for the "chrom" field
        reader.add_filter(lambda row: row.neg_log_pvalue > 7.301)  # Provide a function that can operate on all parsed fields
        reader.add_filter('neg_log_pvalue')  # Exclude values with missing data for the named field  
        
        # Iteration returns containers of cleaned, parsed data (with fields accessible by name).
        for row in reader:
            print(row.chrom)
        
        # Tabix files support iterating over all or part of the file
        for row in reader.fetch('X', 500_000, 1_000_000):
            print(row)
        
        # Write a compressed, tabix-indexed file containing the subset of variants that match filters, choosing only specific 
        #   columns. The data written out will be cleaned and standardized by the parser into a well-defined format. 
        out_fn = reader.write('outfile.txt', columns=['chrom', 'pos', 'pvalue'], make_tabix=True)
        
        # Real data is often messy. If a line fails to parse, the problem will be recorded.
        for number, message, raw_line in reader.errors:
            print('Line {} failed to parse: {}'.format(number, message))
        
        ```
        
        ### Command line file conversion
        The file conversion feature of zorp is also available as a command line utility. See `zorp-convert --help` for details
        and the full list of supported options.
        
        This utility is currently in beta; please inspect the results carefully.
        
        To auto-detect columns based on a library of commonly known file formats:
        
        `$ zorp-convert --auto infile.txt --dest outfile.txt --compress`
        
        Or specify your data columns exactly: 
        
        `$ zorp-convert infile.txt --dest outfile.txt --index  --skip-rows 1 --chrom_col 1 --pos_col 2 --ref_col 3 --alt_col 4 --pvalue_col 5 --beta_col 6 --stderr_beta_col 7 --allele_freq_col 8`
        
        The `--index` option requires that your file be sorted first. If not, you can tabix the standard output format manually 
        as follows.
        
        ```
        $ (head -n 1 <filename.txt> && tail -n +2 <file> | sort -k1,1 -k 2,2n) | bgzip > <filename.sorted.gz>
        $ tabix <filename.sorted.gz> -p vcf
        ```
        
        ## Development
        
        To install dependencies and run in development mode:
        
        `pip install -e '.[test,perf]'`
        
        To run unit tests, use
        
        ```bash
        $ flake8 zorp
        $ mypy zorp
        $ pytest tests/
        ```
        
Keywords: sample setuptools development
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: perf
