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Uploaded

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We developed an in-silico method "seq2HLA", written in python and R, which
takes standard paired end RNA-Seq sequence reads in fastq format
as input, uses a bowtie index comprising all HLA alleles and outputs the most
likely HLA class I and class II genotypes, a p-value for each call, and the
expression of each class.
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Author:
Sebastian Boegel
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Tool dependencies:

- bowtie
- R

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To implement this script into Galaxy you have to copy the scripts folder in
your "$GALAXYHOME/tools/" folder and add the path of seq2HLA.xml
to your "$GALAXYHOME/tool_conf.xml"

Example:
#  <tool file="rna_seq/mapping_counter.xml" />
#    <tool file="rna_seq/count_genes_exons_junctions.xml" />
#    <tool file="rna_seq_auto/get_RNA-seq_data.xml" />
#  </section>
  <section name="HLA Typing" id="hla_type">
    <tool file="seq2HLA/seq2HLA.xml"/>
  </section>
#  <section name="RSeQC" id="rseqc">
#    <tool file="RSeQC/bam2wig.xml" />
#    <tool file="RSeQC/bam_stat.xml" />
#    <tool file="RSeQC/clipping_profile.xml" />
#    <tool file="RSeQC/geneBody_coverage.xml" />
#    <tool file="RSeQC/infer_experiment.xml" />
...
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The script uses in its subprocesses 4 cores, so you will also have to add a
setting to your "$GALAXYHOME/universe_wsgi.ini" to configure the queue for that
script

...
[galaxy:tool_runners]

#biomart = local:///
#encode_db1 = local:///
#hbvar = local:///
#microbial_import1 = local:///
ucsc_table_direct1 = local:///
#ucsc_table_direct_archaea1 = local:///
#ucsc_table_direct_test1 = local:///
upload1 = local:///
bowtie_wrapper = pbs:////-l nodes=1:ppn=4
bowtie2_wrapper = pbs:////-l nodes=1:ppn=4
tophat = pbs:////-l nodes=1:ppn=4
tophat_color = pbs:////-l nodes=1:ppn=4
cuffdiff = pbs:////-l nodes=1:ppn=4
cufflinks = pbs:////-l nodes=1:ppn=4
cuffmerge = pbs:////-l nodes=1:ppn=4
seqhla = pbs:////-l nodes=1:ppn=6               (this line should be added)
...
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