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TranslationalBioinformaticsUnit/RNASEQ-Pair-end-Pipeline
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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
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Description of the different step in Pipeline RNAseq Pair End. From fastq to count tables. Then normalize and obtain the genes highly differentiated.
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