PIPELINE RNASEQ PAIR-END



Principal steps:
  
1-Quality control of the original fastq
  
2-Trimmed Reads
  
3-Quality control of the TRIMMED fastq
  
4-Mapping
  
5-Quality control of the mapping
  
6-Generate count tables
In R:
7-Normalisation and Differential expresion


Execution:

  

bash PipelineRNAseqSE SampleNames_Dir Fastq_Dir Work_Dir Code_Dir Reference_Dir
 
 
    
-SampleNames_Dir: example/fileId.txt (in this txt file are the ids of the samples)
    
-Fastq_Dir: /example/fastq -> the location of fastq files
    
-Work_Dir: /example/workdir -> where are going to be all the results(folder, files...)
    
-Code_Dir: /example/scripts -> the scripts file
    
-Reference_Dir: /example/reference -> where are located reference genome and gtf file
 

   
    
      

Programs:
  

-Fastqc: quality control of fastq files
  
-Trimmomatic: trim reads
  
-TopHat: mapping
  
-Picard (CollectAllignmentSummaryMetrics): quality control of mapping
  
-Python:
      
	-htseq-count: generate count tables
      
	-multqc: visualization
  
-bowtie: index reference genome
  
-samtools: index reference genome
  
  
  



Output Folders:
 
 
-fastqQC -> quality data of original fastq files
  
-trimmed_reads -> trimmed fastq      
-trimmed_fastqQC -> quality data of original trimmed fastq files
  
-bam
      
	-SampleID1 -> mapping results of this sample ID
      
	-SampleID2
      
	-SampleIDN
  
-count_tables -> all the count_table of all samples
  
-multiQCPlots -> multiqc info





Notes:
  In each script have to change the headers and adjust them into your requisites