>truseq-forward-contam
AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC
 /data/results/tools/align/adapter/minion search-adapter -i 131119_SN365_B_L001_GZS-34_R1.fastq.gz
criterion=sequence-density
sequence-density=6.95
sequence-density-rank=1
fanout-score=44.24
fanout-score-rank=1
prefix-density=8.22
prefix-fanout=37.4
sequence=AGATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTGAAAAAAAAA

/data/results/tools/adapter/cutadapt-1.9.1/bin/cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -o 131119_SN365_B_L001_GZS-34_R1.trm.fq.gz 131119_SN365_B_L001_GZS-34_R1.fastq.gz >131119_SN365_B_L001_GZS-34_R1.trm.log
/data/results/tools/adapter/cutadapt-1.9.1/bin/cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -o 131119_SN365_B_L001_GZS-35_R1.trm.fq.gz 131119_SN365_B_L001_GZS-35_R1.fastq.gz >131119_SN365_B_L001_GZS-35_R1.trm.log
/data/results/tools/adapter/cutadapt-1.9.1/bin/cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -o 131119_SN365_B_L001_GZS-36_R1.trm.fq.gz 131119_SN365_B_L001_GZS-36_R1.fastq.gz >131119_SN365_B_L001_GZS-36_R1.trm.log
/data/results/tools/adapter/cutadapt-1.9.1/bin/cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -o 131218_SND393_A_L006_GZS-17_R1.trm.fq.gz 131218_SND393_A_L006_GZS-17_R1.fastq.gz >131218_SND393_A_L006_GZS-17_R1.trm.log


/home/moulos/Rbase/bin/R
library(metaseqR)
the.path="/data/images/proton/Cereghini/e17.5"
metaseqr(
    sample.list=file.path(the.path,"targets.txt"),
    contrast=c("WT_vs_HET"),
    org="mm9",
    refdb="refseq",
    normalization="edger",
    statistics="edger",
    export.where=file.path(the.path,"metaseqr_E17.5"),
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise",
        "correl","pairwise","boxplot","volcano","biodist","deheatmap"
    ),
    pcut=0.05,
    export.what=c("annotation","p.value","adj.p.value","fold.change","stats",
        "counts"),
    export.scale=c("log2"),
    export.values="normalized",
    export.counts.table=TRUE,
    restrict.cores=0.3,
fig.format=c("png","pdf")
)

forward
WT_vs_HET: 193 (12) statistically significant genes of which 59 (0) up regulated, 134 (12) down regulated and 0 (0) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log2 scale. 

reverse
WT_vs_HET: 1422 (550) statistically significant genes of which 2 (2) up regulated, 120 (119) down regulated and 1300 (429) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log2 scale. 

unstranded:
metaseqr(
    sample.list=file.path(the.path,"targets.txt-u"),
    contrast=c("WT_vs_HET"),
    org="mm9",
    refdb="refseq",
    normalization="edger",
    statistics="edger",
    export.where=file.path(the.path,"metaseqr_E17.5"),
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise",
        "correl","pairwise","boxplot","volcano","biodist","deheatmap"
    ),
    pcut=0.05,
    export.what=c("annotation","p.value","adj.p.value","fold.change","stats",
        "counts"),
    export.scale=c("log2"),
    export.values="normalized",
    export.counts.table=TRUE,
    restrict.cores=0.3,
fig.format=c("png","pdf")
)

#--library-type fr-firststrand


#with pandora
metaseqr(
    sample.list=file.path(the.path,"targets.txt"),
    contrast=c("WT_vs_HET"),
    org="mm9",
    refdb="refseq",
    normalization="edger",
statistics = c("deseq", "edger", "limma", "nbpseq"),
             meta.p = "pandora",
#    statistics="edger",
    export.where=file.path(the.path,"metaseqr_E17.5"),
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise",
        "correl","pairwise","boxplot","volcano","biodist","deheatmap"
    ),
    pcut=0.05,
     export.what=c("annotation","p.value","adj.p.value","fold.change","meta.p.value","adj.meta.p.value","stats",
        "counts","flags"), # if you use pandora, the fields "meta.p.value" and "adj.meta.p.value" should be added
#   export.what=c("annotation","p.value","adj.p.value","fold.change","stats",
#        "counts"),
    export.scale=c("log2"),
    export.values="normalized",
    export.counts.table=TRUE,
    restrict.cores=0.3,
fig.format=c("png","pdf")
)


metaseqr(
    sample.list=file.path(the.path,"targets.txt"),
    contrast=c("WT_vs_HET"),
    org="mm9",
    refdb="refseq",
    normalization="edger",
statistics = c("edger"),
#    statistics="edger",
    export.where=file.path(the.path,"metaseqr_E17.5_edger"),
    qc.plots=c(
        "mds","biodetection","countsbio","saturation","readnoise",
        "correl","pairwise","boxplot","volcano","biodist","deheatmap"
    ),
    pcut=0.05,
     export.what=c("annotation","p.value","adj.p.value","fold.change","stats",
        "counts","flags"), # if you use pandora, the fields "meta.p.value" and "adj.meta.p.value" should be added
#   export.what=c("annotation","p.value","adj.p.value","fold.change","stats",
#        "counts"),
    export.scale=c("log2"),
    export.values="normalized",
    export.counts.table=TRUE,
    restrict.cores=0.3,
fig.format=c("png","pdf")
)