cutadapt 1.15 with Python 3.4.3:

cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT -m 6 -o "output file path" -p "input file paths"




STAR version 2.5.4b:

STAR --runThreadN 4 --genomeDir /media/biow/STARindexes/Hs38.91 --readNameSeparator . --outFileNamePrefix "output file path" --readFilesIn "input file paths" --outSAMtype BAM SortedByCoordinate --quantMode GeneCounts --outReadsUnmapped Fastx




RNA-seq data analysis with DESeq2:

cts <- as.matrix(read.csv("path to a file with raw read counts",row.names="gene_id"))
coldata<-cbind(c("Ctr1","Dox1","Ctr2","Dox2","Ctr3","Dox3","Dmso1","Das1","Dmso2","Das2","Dmso3","Das3"),
               c("control","dox","control","dox","control","dox","dmso","das","dmso","das","dmso","das"),
               rep(c("repn1","repn2","repn3"),2,each=2),
               rep(c("mesenchymal","amoeboid"),6))

colnames(coldata)<-c("sample","condition","repn","phenotype")
coldata<-as.data.frame(coldata)
coldata$repn<-as.factor(coldata$repn)
coldata$condition<-factor(coldata$condition,levels=c("control","dox","dmso","das"))
coldata$phenotype<-factor(coldata$phenotype,levels=c("mesenchymal","amoeboid"))
coldata

#dox only
coldata<-coldata[1:6,]
cts<-cts[,1:6]
head(cts)

#das only
coldata<-coldata[7:12,]
cts<-cts[,7:12]
head(cts)

library("AnnotationDbi")
library("org.Hs.eg.db")
convertIDs <- function( ids, from, to, db, ifMultiple=c("useFirst","putNA")) {
  stopifnot( inherits( db, "AnnotationDb" ) )
  ifMultiple <- match.arg( ifMultiple )
  suppressWarnings( selRes <- AnnotationDbi::select(
    db, keys=ids, keytype=from, columns=c(from,to) ) )
  if ( ifMultiple == "putNA" ) {
    duplicatedIds <- selRes[ duplicated( selRes[,1] ), 1 ]
    selRes <- selRes[ ! selRes[,1] %in% duplicatedIds, ]
  }
  return( selRes[ match( ids, selRes[,1] ), 2 ] )
}

library(DESeq2)
dds <- DESeqDataSetFromMatrix(countData = cts,
                              colData = coldata,
                              design = ~ repn + condition)
dds
nrow(dds)
dds <- dds[rowSums(counts(dds)) >= 240, ]
nrow(dds)

dds <- DESeq(dds)
res <- results(dds)
summary(res)
plotMA(res, alpha = 0.4, ylim=c(-3,3))

res$symbol <- convertIDs(row.names(res), "ENSEMBL", "SYMBOL", org.Hs.eg.db)
res$gene_name <- convertIDs(row.names(res), "ENSEMBL", "GENENAME", org.Hs.eg.db)
res$entrez_ID <- convertIDs(row.names(res), "ENSEMBL", "ENTREZID", org.Hs.eg.db)
resOrdered <- res[order(-res$log2FoldChange),]
write.csv(resOrdered,file="output file path")







Mass spectrometry data processing with MSnbase and limma:

library(MSnbase)
###### data import
f0 <- "path to a csv file with non-normalized intensities"
ecol <- c("DMSO1","DAS1","DMSO2","DAS2","DMSO3","DAS3") #for dasatinib series
#ecol <- c("CTR1","DOX1","CTR2","DOX2","CTR3","DOX3") #for inducible caRhoA series
fname <- "Accession"
eset <- readMSnSet2(f0, ecol, fname,sep="\t")
pData <- read.table("path to a tsv file with a table of samples and treatments", row.names=1, header=TRUE, sep="\t")
pData(eset)<-pData
###### imputation
table(is.na(eset))
dim(exprs(eset))
eset <- eset[rowSums(is.na(exprs(eset)))<=1,]
dim(exprs(eset))
head(exprs(eset))
x <- impute(eset, "min")
processingData(x)
table(is.na(x))

###### diff.median normalization
exprs(x)<-log2(exprs(x))
eset <- normalise(x, "diff.median")
head(exprs(eset))
colSums(exprs(eset),na.rm=T)

#####################dasatinib
library(limma)
sampleMatrix<-cbind(Replicate=c("1","1","2","2","3","3"),Treatment=c("DMSO","DAS","DMSO","DAS","DMSO","DAS"))
sampleMatrix<-as.data.frame(sampleMatrix)
rownames(sampleMatrix)<-ecol
Replicate <- factor(sampleMatrix$Replicate, levels=c("1","2","3"))
Treatment <- factor(sampleMatrix$Treatment, levels=c("DAS","DMSO"))
sampleMatrix$Replicate <- factor(sampleMatrix$Replicate, levels=c("1","2","3"))
sampleMatrix$Treatment <- factor(sampleMatrix$Treatment, levels=c("DAS","DMSO"))

design <- model.matrix(~Replicate+Treatment,sampleMatrix)
colnames(design) <- c("Intercept","Replicate2","Replicate3","DMSO")
fit <- lmFit(eset,design)
cont.matrix<-makeContrasts(DMSO,levels=design)
fit2 <- contrasts.fit(fit,cont.matrix)
fit2 <- eBayes(fit2)

write.table(topTable(fit2, n=9999, adjust="BH",sort.by="logFC"),"output file path",sep="\t",col.names=NA)

#########################icaRhoA
library(limma)
sampleMatrix<-cbind(Replicate=c("1","1","2","2","3","3"),Treatment=c("CTR","DOX","CTR","DOX","CTR","DOX"))
sampleMatrix<-as.data.frame(sampleMatrix)
rownames(sampleMatrix)<-ecol
Replicate <- factor(sampleMatrix$Replicate, levels=c("1","2","3"))
Treatment <- factor(sampleMatrix$Treatment, levels=c("CTR","DOX"))
sampleMatrix$Replicate <- factor(sampleMatrix$Replicate, levels=c("1","2","3"))
sampleMatrix$Treatment <- factor(sampleMatrix$Treatment, levels=c("CTR","DOX"))

design <- model.matrix(~Replicate+Treatment,sampleMatrix)
colnames(design) <- c("Intercept","Replicate2","Replicate3","DOX")
fit <- lmFit(eset,design)
cont.matrix<-makeContrasts(DOX,levels=design)
fit2 <- contrasts.fit(fit,cont.matrix)
fit2 <- eBayes(fit2)

write.table(topTable(fit2, n=9999, adjust="BH",sort.by="logFC"),"output file path",sep="\t",col.names=NA)