annotate multiple_to_gd_genotype.xml @ 32:03c22b722882

remove BeautifulSoup dependency

<tool id="gd_multiple_to_gd_genotype" name="Convert" version="1.0.0">
  <description>: CSV, FSTAT, Genepop or VCF to either gd_snp or gd_genotype</description>

  <command interpreter="python">
    #import base64
    #set species_arg = base64.b64encode(str($species))
    multiple_to_gd_genotype.py --input '$input' --output '$output' --dbkey '$dbkey' --species '$species_arg' --format '$input_format'
  </command>

  <inputs>
    <param name="input" type="data" format="txt" label="Input dataset" />

    <param name="input_format" type="select" label="Input format">
      <option value="csv" selected="true">CSV</option>
      <option value="fstat">FSTAT</option>
      <option value="genepop">Genepop</option>
      <option value="vcf">VCF</option>
    </param>

    <param name="species" type="text" label="Focus species">
      <sanitizer>
        <valid initial="string.printable"/>
      </sanitizer>
    </param>

    <param name="dbkey" type="genomebuild" label="Reference species" />
  </inputs>

  <outputs>
    <data name="output" format="gd_genotype" />
  </outputs>

  <!--
  <tests>
    <test>
      <param name="input" value="test_in/sample.gd_snp" ftype="gd_snp" />
      <param name="p1_input" value="test_in/a.gd_indivs" ftype="gd_indivs" />
      <param name="lo_coverage" value="0" />
      <param name="hi_coverage" value="1000" />
      <param name="low_ind_cov" value="3" />
      <param name="lo_quality" value="30" />
      <output name="output" file="test_out/modify_snp_table/modify.gd_snp" />
    </test>
  </tests>
  -->

  <help>

**Dataset formats**

The input dataset is formated as VCF, FSTAT, Genepop, or CSV, and is of
Galaxy datatype text_.  Additionally, the name of the focus species (from
which the SNPs in the VCF file were obtained) and a reference species
are required.  The output dataset is in gd_genotype_ or gd_snp_ format.

For input datasets in Genepop, FSTAT, or CSV formats, the program ignores
population structures as well as alleles other than those encoded by 0,
1, and 2.  For input datasets in FSTAT format the program accepts up to
9 digits and for Genepop files only 2 digits.  Chromosome and position
for each SNPs must be separated by a space or a tab.  Ancestral loci must
be encoded as 1, derived as 2 and missing as 0.  In all cases ancestral
and derived SNPs are returned as N.  Alternatively, a dataset in CSV
format can include nucleotides.  In this case the ancestral nucleotide
is defined as the most common allele.

.. _text: ./static/formatHelp.html#text
.. _gd_snp: ./static/formatHelp.html#gd_snp
.. _gd_genotype: ./static/formatHelp.html#gd_genotype

-----

**What it does**

This tool returns a gd_genotype dataset from VCF formatted files or three
other conventional population genetics formats (i.e. FSTAT, Genepop,
and CSV).  For VCF files that include the fields allelic depth, genotype
quality and genotype ("AD", "GQ", and "GT" respectively in the "FORMAT"
field) the input dataset can be converted into a gd_snp file.

-----

**Examples**

- If the input format is VCF and includes the fields allelic depth, genotype quality and genotype ("AD", "GQ", and "GT" respectively in the "FORMAT" field).  Focus species name is "aye-aye" and reference species is "Human Feb. 2009 (GRCh37/hg19) (hg19)"::

   #CHROM POS    ID          REF ALT QUAL    FILTER INFO FORMAT         19_F                    19.1_F             19.2_F
   Chr21  382242 rs134033430 T   C   3296.97 .      .    GT:GQ:DP:PL:AD 1/1:75:26:943,75,0:0,26 1/1:3:1:30,3,0:0,1 ./.
   Chr21  383680 rs137652597 T   C   2236.62 .      .    GT:GQ:DP:PL:AD 1/1:36:12:436,36,0:0,12 ./.                1/1:3:1:31,3,0:0,1
   Chr21  387251 .           G   T   2407.88 .      .    GT:GQ:DP:PL:AD 1/1:30:12:394,30,0:0,10 ./.                ./.
   etc.

- output (gd_snp)::

   #{"column_names":["chr","pos","A","B","Q","1A","1B","1G","1Q","2A","2B","2G","2Q","3A","3B","3G","3Q"],"dbkey":"aye-aye","individuals":[["19_F",6],["19.1_F",10],["19.2_F",14]],"pos":2,"rPos":2,"ref":1,
   #"scaffold":1,"species":"hg19"}
   Chr21   382242  T       C       3296.97 0       26      0       75      0       1       0       3       0       0       -1      -1
   Chr21   383680  T       C       2236.62 0       12      0       36      0       0       -1      -1      0       1       0       3
   Chr21   387251  G       T       2407.88 0       10      0       30      0       0       -1      -1      0       0       -1      -1
   etc.

- If the input format is VCF, focus species is "aye-aye" and reference species is "Human Feb. 2009 (GRCh37/hg19) (hg19)"::

   #CHROM POS    ID          REF ALT QUAL    FILTER INFO FORMAT         19_F                    19.1_F             19.2_F
   Chr21  382242 rs134033430 T   C   3296.97 .      .    GT:GQ:DP:PL    1/1:75:26:943,75,0      1/1:3:1:30,3,0:0,1 ./.
   Chr21  383680 rs137652597 T   C   2236.62 .      .    GT:GQ:DP:PL    1/1:36:12:436,36,0      ./.                1/1:3:1:31,3,0:0,1
   Chr21  387251 .           G   T   2407.88 .      .    GT:GQ:DP:PL    1/1:30:12:394,30,0      ./.                ./.
   etc.

- output (gd_genotype)::

   #{"column_names":["chr","pos","A","B","1G","2G","3G"],"dbkey":"aye-aye","individuals":[["19_F",5],["19.1_F",6],["19.2_F",7]],"pos":2,"rPos":2,"ref":1,"scaffold":1,"species":"hg19"}
   Chr21   382242  T       C       0       0       -1
   Chr21   383680  T       C       0       -1      0
   Chr21   387251  G       T       0       -1      -1
   etc.

- If the input format is Genepop::

   Microsat on aye-aye from different locations
        Chr21   382242, Chr21   383680, Chr21   387251
   POP
   19_F, 0202 0202 0202
   Pop
   19.1_F, 0202 0000 0000
   19.2_F, 0000 0202 0000
   etc.

- or the input format is FSTAT::

   300  3  2  1
   Chr21   382242
   Chr21   383680
   Chr21   387251
      19_F   22 22 22
      19.1_F   22 00 00
      19.2_F   00 22 00
   etc.

- or the input format is CSV::

   ,19_F,19.1_F,19.2_F,...
   Chr21   382242,22,22,00
   Chr21   383680,22,00,22
   Chr21   387251,22,00,00
   etc.

- output (gd_genotype)::

   #{"column_names":["chr","pos","A","B","1G","2G","3G"],"dbkey":"aye-aye","individuals":[["19_F",5],["19.1_F",6],["19.2_F",7]],"pos":2,"rPos":2,"ref":1,"scaffold":1,"species":"hg19"}
   Chr21   382242  N       N       0       0       -1
   Chr21   383680  N       N       0       -1      0
   Chr21   387251  N       N       0       -1      -1
   etc.

- if the input format is CSV::

   ,19_F,19.1_F,19.2_F,...
   Chr21   382242,C,C,N
   Chr21   383680,C,N,C
   Chr21   387251,T,N,N
   etc.

- output (gd_genotype)::

   #{"column_names":["chr","pos","A","B","1G","2G","3G","4G"],"dbkey":"aye-aye","individuals":[["19_F",5],["19.1_F",6],["19.2_F",7],["...",8]],"pos":2,"rPos":2,"ref":1,"scaffold":1,"species":"hg19"}
   Chr21   382242  C       N       2       2       -1
   Chr21   383680  T       N       2       -1      2
   Chr21   387251  T       N       2       -1      -1
   etc.
  </help>
</tool>