commands.txt

## Commands
This section lists command(s) run by ercc workflow

* Running ercc

ercc workflow checks out spike-in signal (reads) and plots the QC graph(s).

Count reads in fastq file:

```
  zcat FASTQ_R1 | paste - - - - | wc -l  

```
Align reads to chimeric reference (modified reference also containing all spike-in sequences)

```
  bwa mem -t THREADS -M REF_GENOME FASTQ_R1 FASTQ_R2 | samtools view -S - | 
      cut -f 3 | grep ERCC | sort | uniq -c | sed s/^\ *// > CNV_FILE
```

```
 In this custom script we build the RPKM table

 python <<CODE
 import os
 ercc = os.path.expandvars("~{erccData}")
 counts = "~{erccCounts}"
 total = ~{totalReads}
 output_file = "~{basename(erccCounts, '.csv')}_rpkm.csv"

 # 1. Open file, read and collect length data
 ercc_data = open(ercc, 'r')
 length_hash = {}
 for nextLine in ercc_data.readlines():
     nextFields = nextLine.split("\t")
     length_hash[nextFields[0]] = int(nextFields[4])
 ercc_data.close()

 # 2. Calculate RPKMs, put them in a hash
 count_data = open(counts, 'r')
 rpkm_hash = {}
 count_hash = {}

 for nextLine in count_data.readlines():
     nextLine = nextLine.strip("\n")
     countChunk = nextLine.split(" ")
     if len(countChunk) == 2 and countChunk[1] in length_hash.keys():
         count_hash[countChunk[1]] = int(countChunk[0])

 pm = int(total)/1000000.0
 for e in sorted(length_hash.keys()):
    rpkm = 0.0
    if e in count_hash.keys():
        rpkm = count_hash[e]/pm/(length_hash[e]/1000.0)
    rpkm_hash[e] = "{0:.2f}".format(rpkm)
 count_data.close()

 # 3. print out RPKMS sorted by
 f = open(output_file, "w+")
 for e in sorted(rpkm_hash.keys()):
     f.write('\t'.join([e, rpkm_hash[e]]) + '\n')
 f.close()
 CODE

```

Report Making using oputputs from the other steps:

```
 Rscript IMAGING_SCRIPT RPKM_TABLE CONTROL_DATA ERCC_DATA PREFIX DoseResponse SAMPLES
```