# export CISTEMATIC_ROOT=/my/path/to/cistematic_genomes
#
# preliminary: set ERANGEPATH, e.g.
-# export ERANGEPATH=/proj/genome/experiments/commoncode
+# export ERANGEPATH=/my/path/to/erange
#
# preliminary: set CISTEMATIC_TEMP to a local directory with ample space (default is /tmp), e.g.
# export CISTEMATIC_TEMP=/any/local/dir
# create rds file with one lane's worth of data (add -index if using only one lane)
# The example below sets the default cache to 1000000
# The name::value pairs are optional documentart metadata, and can be set to any desired name or value
-python $ERANGEPATH/makerdsfromblat.py 200GFAAXX 200GFAAXXs7.hg19.psl LHCN10213.rds -strict 5 -cache 1000000 library::10213 cellLine::LHCN genome::hg18v2 cellState::confluent flowcell::200GFAAXX
+python $ERANGEPATH/makerdsfromblat.py 200GFAAXX 200GFAAXXs7.hg19.psl LHCN10213.rds --strict 5 --cache 1000000 library::10213 cellLine::LHCN genome::hg18v2 cellState::confluent flowcell::200GFAAXX
# can change a database cache size using rdsmetadata.py to speed up indexing and index-based lookups
# rule of thumb for RNA-seq: set the cache size to half of the RAM on the computer
-#python $ERANGEPATH/rdsmetadata.py LHCN10213.rds -defaultcache 2000000 -nocount
+#python $ERANGEPATH/rdsmetadata.py LHCN10213.rds --defaultcache 2000000 --nocount
# append more data (only add -index when adding last lane)
-python $ERANGEPATH/makerdsfromblat.py 200GFAAXX 200GFAAXXs6.hg19.psl LHCN10213.rds -strict 5 -cache 1000000 -append -index
+python $ERANGEPATH/makerdsfromblat.py 200GFAAXX 200GFAAXXs6.hg19.psl LHCN10213.rds --strict 5 --cache 1000000 --append --index
# count the unique reads falling on the gene models ; the nomatch files are
# mappable reads that fell outside of the Cistematic gene models and not the
# unmappable of Eland (i.e, the "NM" reads)
-python $ERANGEPATH/geneMrnaCounts.py hsapiens LHCN10213.rds LHCN10213.uniqs.count -markGID -cache 1
+python $ERANGEPATH/geneMrnaCounts.py hsapiens LHCN10213.rds LHCN10213.uniqs.count --markGID --cache 1
# count splice reads
-python $ERANGEPATH/geneMrnaCounts.py hsapiens LHCN10213.rds LHCN10213.splices.count -splices -noUniqs -cache 1
+python $ERANGEPATH/geneMrnaCounts.py hsapiens LHCN10213.rds LHCN10213.splices.count --splices --noUniqs --cache 1
# calculate a first-pass RPKM to re-weigh the unique reads,
# using 'none' for the splice count
-python $ERANGEPATH/normalizeExpandedExonic.py hsapiens LHCN10213.rds LHCN10213.uniqs.count none LHCN10213.firstpass.rpkm -cache
+python $ERANGEPATH/normalizeExpandedExonic.py hsapiens LHCN10213.rds LHCN10213.uniqs.count none LHCN10213.firstpass.rpkm --cache
# recount the unique reads with weights calculated during the first pass
-python $ERANGEPATH/geneMrnaCountsWeighted.py hsapiens LHCN10213.rds LHCN10213.firstpass.rpkm LHCN10213.uniqs.recount -uniq -cache 1
+python $ERANGEPATH/geneMrnaCountsWeighted.py hsapiens LHCN10213.rds LHCN10213.firstpass.rpkm LHCN10213.uniqs.recount --uniq --cache 1
# There is a choice of either identifying new regions from the data alone
# (Alternative 1), or using a pre-computed list of new regions (presumably
# file (Alternative 2)
# Alternative 1: find new regions outside of gene models with reads piled up
-python $ERANGEPATH/findall.py RNAFAR LHCN10213.rds LHCN10213.newregions.txt -RNA -minimum 1 -nomulti -flag NM -log rna.log -cache 1
+python $ERANGEPATH/findall.py RNAFAR LHCN10213.rds LHCN10213.newregions.txt --RNA --minimum 1 --nomulti --flag NM --log rna.log --cache 1
# Alternative 1: filter out new regions that overlap repeats more than a certain fraction
# use "none" if you don't have a repeatmask database
-python $ERANGEPATH/checkrmask.py ../hg19repeats/rmask.db LHCN10213.newregions.txt LHCN10213.newregions.repstatus LHCN10213.newregions.good -log rna.log -startField 1 -cache 1
+python $ERANGEPATH/checkrmask.py ../hg19repeats/rmask.db LHCN10213.newregions.txt LHCN10213.newregions.repstatus LHCN10213.newregions.good --log rna.log --startField 1 --cache 1
# Alternative 2: use a precomputed list of "new" regions (outside of gene models)
#python2.5 $ERANGEPATH/regionCounts.py ../RNAFAR/all.newregions.good LHCN10213.rds LHCN10213.newregions.good
# map all candidate regions that are within a 20kb radius of a gene in bp
# take out -cache if running locally
-python $ERANGEPATH/getallgenes.py hsapiens LHCN10213.newregions.good LHCN10213 -radius 20001 -trackfar -cache
+python $ERANGEPATH/getallgenes.py hsapiens LHCN10213.newregions.good LHCN10213 --radius 20001 --trackfar --cache
# calculate expanded exonic read density
-python $ERANGEPATH/normalizeExpandedExonic.py hsapiens LHCN10213.rds LHCN10213.uniqs.recount LHCN10213.splices.count LHCN10213.expanded.rpkm LHCN10213.candidates.txt LHCN10213.accepted.rpkm -cache
+python $ERANGEPATH/normalizeExpandedExonic.py hsapiens LHCN10213.rds LHCN10213.uniqs.recount LHCN10213.splices.count LHCN10213.expanded.rpkm LHCN10213.candidates.txt LHCN10213.accepted.rpkm --cache
# create bed file of accepted candidate regions
python2.5 $ERANGEPATH/regiontobed.py RNAFAR LHCN10213.accepted.rpkm RNAFAR.bed 255,0,0
# weigh multi-reads
-python $ERANGEPATH/geneMrnaCountsWeighted.py hsapiens LHCN10213.rds LHCN10213.expanded.rpkm LHCN10213.multi.count -accept LHCN10213.accepted.rpkm -multi -cache 1
+python $ERANGEPATH/geneMrnaCountsWeighted.py hsapiens LHCN10213.rds LHCN10213.expanded.rpkm LHCN10213.multi.count --accept LHCN10213.accepted.rpkm --multi --cache 1
# calculate final exonic read density
-python $ERANGEPATH/normalizeFinalExonic.py LHCN10213.rds LHCN10213.expanded.rpkm LHCN10213.multi.count LHCN10213.final.rpkm -multifraction -withGID -cache
+python $ERANGEPATH/normalizeFinalExonic.py LHCN10213.rds LHCN10213.expanded.rpkm LHCN10213.multi.count LHCN10213.final.rpkm --multifraction --withGID --cache
projects / erange.git / blobdiff