The read_gff() function

Before starting make sure you have a working read_gff() function. If you don't have it - here is the code:

Note

Your code

Use your own code, if it works, or see the tabs for my version.

Note in my version I've implemented the challenge questions about extra attributes, which are kindof useful to have!

R solution

read_gff = function( filename, extra_attributes = c() ) {
    result = readr::read_tsv(
        filename,
        comment = '#',
        na = ".",
        col_names = c( 'seqid', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase', 'attributes' ),
        col_types = readr::cols(
            readr::col_character(),
            readr::col_character(),
            readr::col_character(),
            readr::col_integer(),
            readr::col_double(),
            readr::col_double(),
            readr::col_character(),
            readr::col_integer(),
            readr::col_character()
        )
    )

    # Let's put ID and Parent at the start.
    result = tibble::add_column(result, ID = NA, .before = 1)
    result = tibble::add_column(result, Parent = NA, .before = 2)

    # Now extract all the attributes
    for( attribute in c( "ID", "Parent", extra_attributes )) {
        # Create the appropriate regex using `sprintf()`
        regex = sprintf("%s=([^;]+)[;|$]", attribute)
        # Extract the attribute...
        result[[attribute]] = stringr::str_extract(result[["attributes"]], regex, group = TRUE)
        # ...and remove it from the attributes column.
        result[["attributes"]] = stringr::str_remove(result[["attributes"]], regex)
    }
    return( result )
}

Python solution

def read_gff( file, extra_attributes = [] ):
    import pandas
    result = pandas.read_table(
        file,
        comment = '#',
        names = [ 'seqid', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase', 'attributes' ],
        na_values = '.',
        dtype = {
            'seqid': str,
            'source': str,
            'type': str,
            'start': int,
            'end': int,
            'score': float,
            'strand': str,
            'phase': str,
            'attributes': str
        }
    )
    result.insert( loc = 0, column = 'ID', value = None )
    result.insert( loc = 1, column = 'Parent', value = None )
    for attribute in [ 'ID', 'Parent' ] + extra_attributes:
        regexp = '%s=([^;]+)[;|$]' % attribute
        result[attribute] = result.attributes.str.extract( regexp )
        result["attributes"] = result["attributes"].str.replace( regexp, "" )
    return result