Last updated: 2021-02-19
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Knit directory: 2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/
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Following sequencing and obtaining .fastq.gz file, the first step is to perform trimming and mapping of the sequencing data to generate bam files. All these steps were performed using bash code.
Bam files were then used for removal of duplicated and low quality (<Q30) reads and subsequently subjected to read counting to generate a count matrix.
Mouse AAV6:lacZ-shRNA or AAV6:Yap-shRNA bulk nuclei ATAC-seq were performed using paired-end sequencing method and below are the scripts for primary processing of paired-end sequencing read.
#!/bin/bash
# function to run skewer quality trimming
runskew(){
FQZ1=$1
FQZ2=`echo $FQZ1 | sed 's/_R1.fastq.gz/_R2.fastq.gz/'`
skewer -t 8 -q 20 $FQZ1 $FQZ2
}
export -f runskew
# actually run skewer
parallel -j3 runskew ::: *_R1.fastq.gzrunbwamempe() {
FQ1=$1
FQ2=`echo $FQ1 | sed 's/R1.fastq-trimmed-pair1.fastq/R1.fastq-trimmed-pair2.fastq/'`
BASE=`echo $FQ1 | sed 's/_R1.fastq-trimmed-pair1.fastq//'`
REF=/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/refgenome/Mus_musculus.GRCm38.dna_sm.primary_assembly.fa
bwa mem -t 20 $REF $FQ1 $FQ2 \
| samtools view -uSh - \
| samtools sort -@10 -o ${BASE}.sort.bam
samtools index ${BASE}.sort.bam
samtools flagstat ${BASE}.sort.bam > ${BASE}.sort.bam.stats
}
export -f runbwamempe
# actually run bwa pe
ls *_R1.fastq-trimmed-pair1.fastq | parallel -u -j4 runbwamempe {}#!/bin/bash
nodup(){
BAM=$1
OUT=`echo $BAM | sed 's/.bam/_nodup.bam/'`
samtools rmdup $BAM $OUT
}
export -f nodup
parallel nodup ::: `ls *bam | grep -v dup`#!/bin/bash
BAMS='*bam'
BASENAME=humanATAC
PEAKBED=${BASENAME}_peaks.bed
PEAKSAF=${BASENAME}_peaks.saf
OUT=${BASENAME}_pks.txt
MX=${BASENAME}_pks_se.mx
PATH=$PATH:/usr/local/installed/macs/1.4.2-1/python-2.7.11/.//bin/
ls $BAMS | parallel macs14 -t {} -n {}_macs
done
exitfor BED in *peaks.bed ; do
awk '{OFS="\t"} {if ($2<1) print $1,1,$3 ; else print $0 }' $BED | awk 'NF=="5"'> tmp
mv tmp $BED
done
rm humanATAC_peaks_cov*.bed
for COV in 2 3 ; do
bedtools multiinter -i *_macs_peaks.bed \
| cut -f-4 | awk -v C=$COV '$4>=C && NF==4' \
| bedtools merge -i - > mouseATAC_peaks_cov${COV}.bed
done
exit#!/bin/bash
for BED in mouseATAC*bed ; do
SAF=$BED.saf
OUT=$SAF.pe.mx
awk '{OFS="\t"} {print "PK"NR"_"$1":"$2"-"$3,$1,$2,$3,"+"}' $BED > $SAF
( featureCounts -p -Q 10 -T 20 -s 0 -a $SAF -F SAF -o $OUT *bam
sed 1d $OUT | cut -f1,7- > tmp ; mv tmp $OUT ) &
done
awk: fatal: cannot open file `mouseATAC*bed' for reading (No such file or directory)
bash: line 7: featureCounts: command not found#!/bin/bash
for MX in `ls *mx` ; do
cat $MX | sed 's/-ATAC.sort_nodup.bam//g' > $MX.fix
done
wait
Filtering out low counts genes by running the following filter.sh as
bash filter.sh mouseATAC_peaks_cov2.bed.saf.pe.mx.fix
filter.sh
head -1 $1 > ${1}_filt
awk '{
min = max = sum = $2; # Initialize to the first value (2nd field)
sum2 = $2 * $2 # Running sum of squares
for (n=3; n <= NF; n++) { # Process each value on the line
if ($n < min) min = $n # Current minimum
if ($n > max) max = $n # Current maximum
sum += $n; # Running sum of values
sum2 += $n * $n # Running sum of squares
}
print sum/(NF-1) ;
}' $1 > avg
paste avg $1 | awk '$1 >= 10' | cut -f2- | tr ' ' '\t' >> ${1}_filt
rm avg
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 rstudioapi_0.11 whisker_0.4 knitr_1.30
[5] magrittr_1.5 R6_2.5.0 rlang_0.4.7 stringr_1.4.0
[9] tools_3.6.1 xfun_0.18 git2r_0.27.1 htmltools_0.5.0
[13] ellipsis_0.3.1 rprojroot_1.3-2 yaml_2.2.1 digest_0.6.27
[17] tibble_3.0.3 lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1
[21] vctrs_0.3.2 promises_1.1.1 fs_1.5.0 glue_1.4.2
[25] evaluate_0.14 rmarkdown_2.5 stringi_1.5.3 compiler_3.6.1
[29] pillar_1.4.6 backports_1.1.10 httpuv_1.5.4 pkgconfig_2.0.3