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#!/bin/bash
#SBATCH --job-name=star # Job name
#SBATCH --nodes=1
#SBATCH --ntasks=8
#SBATCH --time=60
#SBATCH --mem=32000 # Memory pool for all cores (see also --mem-per-cpu)
#SBATCH --partition=production
#SBATCH --reservation=workshop
#SBATCH --account=workshop
#SBATCH --array=1-16
#SBATCH --output=slurmout/star_%A_%a.out # File to which STDOUT will be written
#SBATCH --error=slurmout/star_%A_%a.err # File to which STDERR will be written
start=`date +%s`
echo $HOSTNAME
echo "My SLURM_ARRAY_TASK_ID: " $SLURM_ARRAY_TASK_ID
sample=`sed "${SLURM_ARRAY_TASK_ID}q;d" samples.txt`
REF="References/star.overlap100.gencode.v31"
outpath='02-STAR_alignment'
[[ -d ${outpath} ]] || mkdir ${outpath}
[[ -d ${outpath}/${sample} ]] || mkdir ${outpath}/${sample}
echo "SAMPLE: ${sample}"
module load star/2.7.0e
call="STAR
--runThreadN 8 \
--genomeDir $REF \
--outSAMtype BAM SortedByCoordinate \
--readFilesCommand zcat \
--readFilesIn 01-HTS_Preproc/${sample}/${sample}_R1.fastq.gz 01-HTS_Preproc/${sample}/${sample}_R2.fastq.gz \
--quantMode GeneCounts \
--outFileNamePrefix ${outpath}/${sample}/${sample}_ \
${outpath}/${sample}/${sample}-STAR.stdout 2> ${outpath}/${sample}/${sample}-STAR.stderr"
echo $call
eval $call
end=`date +%s`
runtime=$((end-start))
echo $runtime
#!/bin/python
print 'one'
print 'two'
if x == 1:
print 'one'
cond1 = True
cond2 = False
if cond1 and cond2:
# do something
top.table$Gene <- rownames(top.table)
top.table <- top.table[,c("Gene", names(top.table)[1:6])]
top.table <- data.frame(top.table,anno[match(top.table$Gene,anno$Gene.stable.ID.version),],logcpm[match(top.table$Gene,rownames(logcpm)),])
write.table(top.table, file = "A.C_v_B.C.txt", row.names = F, sep = "\t", quote = F)
This browser does not support PDFs. Please download the PDF to view it: Download PDF.
- run the
alter_rmd.py
script to change some of the R formatting.
python alter_rmd.py -i Intro2R.md
- will produce a file with the same name
Intro2R_fixed.md
# assign number 150 to variable a.
a <- 150
a
## [1] 150
# assign number 150 to variable a.
a <- 150
a
[1] 150
Description | R_function |
---|---|
Mean | mean() |
Standard deviation | sd() |
Variance | var() |
Minimum | min() |
Maximum | max() |
Median | median() |
Range of values: minimum and maximum | range() |
Sample quantiles | quantile() |
Generic function | summary() |
Interquartile range | IQR() |
Taxon_Name MeanBootstrapValue MeanLengthMerged PercentageAsPairs Total
d__Bacteria 0.997 421 0.0 126506
d__Bacteria;p__Acetothermia;c__Acetothermia_genera_incertae_sedis;o__Acetothermia_genera_incertae_sedis;f__Acetothermia_genera_incertae_sedis;g__Acetothermia_genera_incertae_sedis 0.56 421 0.0 1
d__Bacteria;p__Acidobacteria 0.605 424 0.0 1483
d__Bacteria;p__Acidobacteria;c__Acidobacteria_Gp1 0.983 403 0.0 1049
d__Bacteria;p__Acidobacteria;c__Acidobacteria_Gp10;o__Gp10;f__Gp10;g__Gp10 0.98 427 0.0 8312
d__Bacteria;p__Acidobacteria;c__Acidobacteria_Gp11;o__Gp11;f__Gp11;g__Gp11 0.792 406 0.0 321
d__Bacteria;p__Acidobacteria;c__Acidobacteria_Gp12;o__Gp12;f__Gp12;g__Gp12 0.999 403 0.0 34
d__Bacteria;p__Acidobacteria;c__Acidobacteria_Gp13;o__Gp13;f__Gp13;g__Gp13 0.998 423 0.0 13
d__Bacteria;p__Acidobacteria;c__Acidobacteria_Gp15;o__Gp15;f__Gp15;g__Gp15 0.961 414 0.0 1356