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comp-metadata/docs/alignment/transcriptome/SALv2.xml

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<?xml version="1.0"?>
<?xml-stylesheet type="text/css" href="http://deep.mpi-inf.mpg.de/DAC/files/style/deep_process_style.css"?>
<process>
<name>SAL</name>
<version>2</version>
<author>
<name>Filippos Klironomos</name>
<email>filippos.klironomos@mdc-berlin.de</email>
</author>
<description>
1) remove adaptors and map to reference without filtering or collapsing the reads
2) generate coverage track from the aligned reads
3) cluster reads that overlap windows of 501nts around TSS/TES
4) predict TSS/TES-miRNAs based on the following filters:
- pick sharply defined, well covered clusters and identify the peak
- peak from step (1) should be consisted of reads with identical 5&apos; start position
- average read length of peak from step (2) should be between 18 and 24nts
- average phastCons mean score per peak from step (3) should be 0.8 or above
5) generate coverage tracks for TSS/TES-miRNAs
</description>
<inputs>
<filetype>
<identifier>library</identifier>
<format>FASTQ</format>
<quantity>single</quantity>
<comment>{SampleID}.fastq library of raw reads to trim and map to the reference</comment>
</filetype>
</inputs>
<references>
<filetype>
<identifier>genome</identifier>
<format>fasta</format>
<quantity>single</quantity>
<comment><![CDATA[
hs37d5 and GRCm38mm10 genomes are modified as follows:
*) IDs are simplified, everything to the right of the first white space encountered is removed,
*) all ambiguously called nucleotides [URYSWKMBDHV] have been masked to 'N'.
The following script does all this:
sed -e 's/^>\(\S\+\)\s.*$/>\1/' -e '/^[^>]/s/[UuRrYySsWwKkMmBbDdHhVv]/N/g' hs37d5.fa > hs37d5_simple.fa
sed -e 's/^>\(\S\+\)\s.*$/>\1/' -e '/^[^>]/s/[UuRrYySsWwKkMmBbDdHhVv]/N/g' GRCm38mm10.fa > GRCm38mm10_simple.fa
]]>
</comment>
</filetype>
<filetype>
<identifier>genome_index</identifier>
<format>bowtie-index</format>
<quantity>collection</quantity>
<comment><![CDATA[
bowtie version 1.1.1 index of hs37d5_simple.fa and GRCm38mm10_simple.fa generated as follows:
bowtie-build -f hs37d5_simple.fa hs37d5_simple.fa
bowtie-build -f GRCm38mm10_simple.fa GRCm38mm10_simple.fa
]]>
</comment>
</filetype>
</references>
<outputs>
<filetype>
<identifier>SampleID.SALv2.DATE.trimmed.bam</identifier>
<format>BAM</format>
<quantity>single</quantity>
<comment>adaptor-trimmed reads mapped to the reference without any filtering or collapsing</comment>
</filetype>
<filetype>
<identifier>SampleID.SALv2.DATE.trimmed.bedGraph</identifier>
<format>bedGraph</format>
<quantity>single</quantity>
<comment>reference genome coverage track of aligned adaptor-trimmed reads</comment>
</filetype>
<filetype>
<identifier>SampleID.SALv2.DATE.TSS.{sense,antisense}.tsv</identifier>
<format>TSV</format>
<quantity>single</quantity>
<comment>Coverage track for clustered reads mapped sense/antisense to TSS regions. Each cluster with a unique clusterId represents a TSS-miRNA prediction.
The format is BED-like (0-based, end-exclusive) and the columns are:
chr start end readId score strand clusterId min_phastCons_score max_phastCons_score mean_phastCons_score median_phastCons_score
</comment>
</filetype>
<filetype>
<identifier>SampleID.SALv2.DATE.TSS.{sense,antisense}.summary.tsv</identifier>
<format>TSV</format>
<quantity>single</quantity>
<comment>Summary results of called peaks (clusters)
The format is BED-like (0-based, end-exclusive) and the columns are:
chr start end strand clusterId coverage geneId,symbol consensus_sequence
</comment>
</filetype>
<filetype>
<identifier>SampleID.SALv2.DATE.TSS.{sense,antisense}.bed</identifier>
<format>BED</format>
<quantity>single</quantity>
<comment>Simplified BED version (6 columns) of the corresonding TSV file with readIds removed.</comment>
</filetype>
<filetype>
<identifier>SampleID.SALv2.DATE.TES.{sense,antisense}.tsv</identifier>
<format>BED-like</format>
<quantity>single</quantity>
<comment>Coverage track for clustered reads mapped sense/antisense to TES regions. Each cluster with a unique clusterId represents a TES-miRNA prediction.
The format is BED-like (0-based, end-exclusive) and the columns are:
chr start end readId score strand clusterId min_phastCons_score max_phastCons_score mean_phastCons_score median_phastCons_score
</comment>
</filetype>
<filetype>
<identifier>SampleID.SALv2.DATE.TES.{sense,antisense}.summary.tsv</identifier>
<format>TSV</format>
<quantity>single</quantity>
<comment>Summary results of called peaks (clusters)
The format is BED-like (0-based, end-exclusive) and the columns are:
chr start end strand clusterId coverage geneId,symbol consensus_sequence
</comment>
</filetype>
<filetype>
<identifier>SampleID.SALv2.DATE.TES.{sense,antisense}.bed</identifier>
<format>BED</format>
<quantity>single</quantity>
<comment>Simplified BED version (6 columns) of the corresonding TSV file with readIds removed.</comment>
</filetype>
</outputs>
<software>
<tool>
<name>standard</name>
<version>n/a</version>
<command_line><![CDATA[ CMDLINE ]]></command_line>
<loop>n/a</loop>
<comment>The following software tools are used in the pipeline:
flexbar version 2.4
bowtie version 1.1.1
samtools version 1.1
bedtools version 2.23.0
R version 3.2.0
Bioconductor version 3.1 (BiocInstaller 1.18.1)
bwtool version 1.0
custom python 2.7 scripts
gawk version 4.0.1
</comment>
</tool>
</software>
</process>