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Finally remerged changeset. Should be good now.
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#!/usr/bin/env python
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"""
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Run GeneTrack(atlas) with a faked conf file to generate GeneTrack data files.
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usage: %prog
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-l, --label=N: Data label for fit curve/peak plot
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-1, --fits=N/N/N/N/N,...: Data files (interval format) for fit curve/peak plot
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-2, --feats=N:M/N/N/N/N/N,...: Data files (interval format) for features.
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-d, --data=N: Output path for hdf5 and sqlite databases.
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-o, --output=N: Output path for export file.
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"""
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from galaxy import eggs
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import pkg_resources
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pkg_resources.require("GeneTrack")
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pkg_resources.require("bx-python")
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import commands as oscommands
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from atlas import commands
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from atlas import sql
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from bx.cookbook import doc_optparse
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from bx.intervals import io
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import os
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import tempfile
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from functools import partial
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SIGMA = 20
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WIDTH = 5 * SIGMA
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EXCLUSION_ZONE = 147
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def main(label, fit, feats, data_dir, output):
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os.mkdir(data_dir)
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conf = DummyConf(
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__name__=label,
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CLOBBER = True,
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DATA_SIZE = 3*10**6,
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MINIMUM_PEAK_SIZE = 0.1,
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LOADER_ENABLED = False,
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FITTER_ENABLED = False,
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PREDICTOR_ENABLED = False,
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EXPORTER_ENABLED = False,
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LOADER = loader,
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FITTER = fitter,
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PREDICTOR = predictor,
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EXPORTER = partial( commands.exporter, formatter=commands.bed_formatter),
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HDF_DATABASE = os.path.join( data_dir, "data.hdf" ),
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SQL_URI = "sqlite:///%s" % os.path.join( data_dir, "features.sqlite" ),
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SIGMA = SIGMA,
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WIDTH = WIDTH,
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DATA_LABEL = label,
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FIT_LABEL = "%s-SIGMA-%d" % ( label,SIGMA ),
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PEAK_LABEL = "PRED-%s-SIGMA-%d" % ( label,SIGMA ),
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EXCLUSION_ZONE = EXCLUSION_ZONE,
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LEFT_SHIFT = EXCLUSION_ZONE / 2,
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RIGHT_SHIFT = EXCLUSION_ZONE / 2,
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EXPORT_LABELS = [ "PRED-%s-SIGMA-%d" % ( label,SIGMA ) ],
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EXPORT_DIR = os.path.join( data_dir ),
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DATA_FILE=fit and fit[1] or None,
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fit=fit,
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feats=feats,
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)
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if fit:
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# Turn on fit processing.
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conf.LOADER_ENABLED = True,
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conf.FITTER_ENABLED = True,
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conf.PREDICTOR_ENABLED = True,
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conf.EXPORTER_ENABLED = True,
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for feat in feats:
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load_feature_files(conf, feats)
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commands.execute(conf)
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outname = "%s.%s.txt" % (conf.__name__, conf.EXPORT_LABELS[0] )
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if os.path.exists( os.path.join(data_dir, outname) ):
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os.rename( os.path.join(data_dir, outname), output)
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# mod454 seems to be a module without a package. The necessary funcitons are
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# stubbed out here until I'm sure of their final home. INS
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def loader( conf ):
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from atlas import hdf
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from mod454.schema import Mod454Schema as Schema
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last_chrom = table = None
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db = hdf.hdf_open( conf.HDF_DATABASE, mode='a', title='HDF database')
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gp = hdf.create_group( db=db, name=conf.DATA_LABEL, desc='data group', clobber=conf.CLOBBER )
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fit_meta = conf.fit[2]
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# iterate over the file and insert into table
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for line in open( conf.fit[1], "r" ):
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if line.startswith("chrom"): continue #Skip possible header
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if line.startswith("#"): continue
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fields = line.rstrip('\r\n').split('\t')
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chrom = fields[fit_meta.chromCol]
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if chrom != last_chrom:
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if table: table.flush()
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table = hdf.create_table( db=db, name=chrom, where=gp, schema=Schema, clobber=False )
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last_chrom = chrom
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try:
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position = int(fields[fit_meta.positionCol])
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forward = float(fields[fit_meta.forwardCol])
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reverse = fit_meta.reverseCol > -1 and float(fields[fit_meta.reverseCol]) or 0.0
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row = ( position, forward, reverse, forward+reverse, )
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table.append( [ row ] )
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except ValueError:
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# Ignore bad lines
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pass
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table.flush()
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db.close()
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def fitter( conf ):
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from mod454.fitter import fitter as mod454_fitter
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return mod454_fitter( conf )
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def predictor( conf ):
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from mod454.predictor import predictor as mod454_predictor
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return mod454_predictor( conf )
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def load_feature_files( conf, feats):
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"""
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Loads features from file names
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"""
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engine = sql.get_engine( conf.SQL_URI )
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sql.drop_indices(engine)
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conn = engine.connect()
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for label, fname, col_spec in feats:
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label_id = sql.make_label(engine, name=label, clobber=False)
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reader = io.NiceReaderWrapper( open(fname,"r"),
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chrom_col=col_spec.chromCol,
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start_col=col_spec.startCol,
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end_col=col_spec.endCol,
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strand_col=col_spec.strandCol,
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fix_strand=False )
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values = list()
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for interval in reader:
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print interval
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if not type( interval ) is io.GenomicInterval: continue
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row = {'label_id':label_id,
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'name':col_spec.nameCol == -1 and "%s-%s" % (str(interval.start), str(interval.end)) or interval.fields[col_spec.nameCol],
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'altname':"",
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'chrom':interval.chrom,
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'start':interval.start,
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'end':interval.end,
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'strand':interval.strand,
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'value':0,
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'freetext':""}
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values.append(row)
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insert = sql.feature_table.insert()
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conn.execute( insert, values)
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conn.close()
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sql.create_indices(engine)
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class Bunch( object ):
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def __init__(self, **kwargs):
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for key,value in kwargs.items():
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setattr( self, key, value )
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class DummyConf( Bunch ):
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"""
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Fake conf module for genetrack/atlas.
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"""
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pass
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if __name__ == "__main__":
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options, args = doc_optparse.parse( __doc__ )
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try:
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label = options.label
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if options.fits:
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fit_name, fit_meta = options.fits.split(':')[0], [int(x)-1 for x in options.fits.split(':')[1:]]
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fit_meta = Bunch(chromCol=fit_meta[0], positionCol=fit_meta[1], forwardCol=fit_meta[2], reverseCol=fit_meta[3])
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fit = ( label, fit_name, fit_meta, )
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else:
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fit = []
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# split apart the string into nested lists, preserves order
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if options.feats:
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feats = [ (
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feat_label,
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fname,
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Bunch(chromCol=int(chromCol)-1, startCol=int(startCol)-1, endCol=int(endCol)-1,
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strandCol=int(strandCol)-1, nameCol=int(nameCol)-1),
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)
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for feat_label, fname, chromCol, startCol, endCol, strandCol, nameCol
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in ( feat.split(':') for feat in options.feats.split(',') if len(feat) > 0 )]
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else:
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feats = []
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data_dir = options.data
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output = options.output
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except:
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doc_optparse.exception()
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main(label, fit, feats, data_dir, output)
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@@ -0,0 +1,62 @@
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<tool id="genetrack1" name="GeneTrack">
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<description>Track creator/viewer</description>
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<code file="genetrack_code.py">
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<hook exec_after_process="exec_after_process" />
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</code>
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<command interpreter="python">
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genetrack.py -l $data_label
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#if not str($fit_data) == "None"
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-1
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${fit_data}:${fit_data.metadata.chromCol}:${fit_data.metadata.positionCol}:${fit_data.metadata.forwardCol}:${fit_data.metadata.reverseCol}
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#end if
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#if $feature_data
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-2
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#end if
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#for $data in $feature_data
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${data.name}:${data.input}:${data.input.metadata.chromCol}:${data.input.metadata.startCol}:${data.input.metadata.endCol}:${data.input.metadata.strandCol}:${data.input.metadata.nameCol},
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#end for
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-d ${genetrack.files_path}
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-o ${bed_out}
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</command>
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<inputs>
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<param name="data_label" type="text" label="Track Label" size="50">
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<validator type="regex" message="Please name the track with only alphanumeric characters.">[a-zA-Z0-9]{0,25}</validator>
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</param>
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<param name="fit_data" type="data" format="coverage" label="Coverage Dataset" optional="true" />
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<repeat name="feature_data" title="Features">
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<param name="input" type="data" format="interval" label="Dataset" />
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<param name="name" type="text" label="Feature Type (mRNA, ESTs, ORFs, etc.)" size="25">
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<validator type="regex" message="Please name the feature with only alphanumeric characters.">[a-zA-Z0-9]{0,25}</validator>
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</param>
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</repeat>
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</inputs>
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<outputs>
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<data format="genetrack" name="genetrack" />
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<data format="bed" name="bed_out" />
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</outputs>
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<requirements>
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<requirement type="python-module">tables</requirement>
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<requirement type="python-module">atlas</requirement>
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<requirement type="python-module">pychartdir</requirement>
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<requirement type="python-module">numpy</requirement>
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</requirements>
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<help>
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This tool takes the input Fit Data and creates a peak and curve plot showing
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the reads and fitness on each basepair. Features can be plotted below as tracks.
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-----
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**Syntax**
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- **Track Label** is the name of the generated track.
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- **Fit Data** are the datasets to calculate coverage/reads across basepairs and generate a curve.
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- **Features** are additional datasets (interval format) to be plotted below as tracks.
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</help>
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</tool>
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@@ -0,0 +1,13 @@
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import sets, os
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from galaxy import eggs
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from galaxy import jobs
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from galaxy.tools.parameters import DataToolParameter
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def exec_after_process(app, inp_data, out_data, param_dict, tool=None, stdout=None, stderr=None):
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"""
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Copy data_label to genetrack.metadata.label
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"""
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out_data['genetrack'].metadata.label = param_dict['data_label']
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out_data['genetrack'].info = "Use the link below to view the custom track."
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out_data['bed_out'].info = ""
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