* Check RNA-seq read quality with FastQC
    Version: 0.11.5

* Map reads to genome with Tophat
  * Build genome index for Tophat using PlasmoDB P. berghei ANKA genome version 28
      Version: bowtie2/2.2.9
      Command: bowtie2-build PlasmoDB-28_PbergheiANKA_Genome.fa <bowtie_ref>
  * Map reads with Tophat
      Versions:
        tophat/2.1.1
        bowtie2/2.2.9
        samtools/1.3.1
        boost/1.60.0
      Command: tophat -p 8 --library-type fr-firststrand --max-intron-length 3000 --min-intron-length 4 --output-dir <output_directory> <bowtie_ref> <1.fastq> <2.fastq>

* Check quality of mapping with flagstats
    Version: samtools/1.3.1
    Command: samtools flagstat <accepted_hits.bam>

* Analyse differential expression with limma/voom
    Versions:
      limma/3.28.14
      edgeR/3.4.0
      Rsubread/1.22.2
      R/3.3.1
    Commands:
      fa_targets <- readTargets(file=fa_bam_list)
      fa_celltype <- factor(fa_targets$CellType)
      fa_design <- model.matrix(~fa_celltype)
      fa_fcounts <- featureCounts(files=fa_targets$BamFile,annot.ext=gff_file,isGTFAnnotationFile=TRUE,nthreads=8,isPairedEnd=TRUE,strandSpecific=2)
      fa_isexpr <- rowSums(cpm(fa_fcounts$counts) > 3) >= 3
      fa_x <- fa_fcounts$counts[fa_isexpr,]
      fa_y <- voom(fa_x,fa_design,plot=TRUE)
      plotMDS(fa_y,xlim=c(-5,5))
      fa_fit <- eBayes(lmFit(fa_y,fa_design))
      write.csv(topTable(fa_fit,coef=2,number=Inf,p.value=0.05),file="limma_out_fa")
      write.csv(topTable(fa_fit,coef=2,number=Inf,p.value=1),file="limma_out_fa_full")

* Quantify RPKM counts with limma
    Versions:
      limma/3.28.14
      Rsubread/1.22.2
      R/3.3.1
    Commands:
      all_targets <- readTargets(file=all_bam_list)
      all_celltype <- factor(all_targets$CellType)
      all_design <- model.matrix(~all_celltype)
      all_fcounts <- featureCounts(files=all_targets$BamFile,annot.ext=gff_file,isGTFAnnotationFile=TRUE,nthreads=8,isPairedEnd=TRUE,strandSpecific=2)
      x <- DGEList(counts=all_fcounts$counts, genes=all_fcounts$annotation[,c("GeneID","Length")])
      x_rpkm <- rpkm(x,x$genes$Length)
      write.csv(x_rpkm,file="all_rpkm")