Last updated: 2021-02-19

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Knit directory: 2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/

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Introduction

Nuclei RNA-seq generates sequencing reads that map across a whole gene (intron and exon).

1. Generate reference genome for nuclei RNA-seq

A nuclei-specific reference genome is generated for sequencing read counting.

#!/bin/bash

grep -w gene Mus_musculus.GRCm38.96.gtf | cut -f1,4,5,7,9| cut -d '"' -f-2,6 \
        | sed 's/gene_id "//' | tr '"' '_'\
        | awk '{OFS="\t"}  {print $5,$1,$2,$3,$4}' > Mus_musculus.GRCm38.96.fulllength.saf
grep: Mus_musculus.GRCm38.96.gtf: No such file or directory

2. Trimming and mapping of sequencing reads

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 read counts to generate a count matrix.

Mouse AAV6:lacZ-shRNA or AAV6:Yap-shRNA bulk nuclei RNA-seq were performed using paired-end sequencing method and below are the scripts for trimming and mapping paired end sequencing read.

Used libraries and functions

  • skewer/0.2.2
  • star/2.5.3a
  • samtools/1.8
  • parallel
  • subread/1.5.0

Trimming of 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.gz

Mapping of Skewer trimmed .fastq to mouse reference genome

It will generate the following 4 outputs for individual .fastq.gz file:

  1. .STAR.bam
  2. .STAR.bam.bai
  3. .STAR.bam.stats
  4. _starlog.txt

#!/bin/bash

DIR=/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/refgenome/star
GTF=/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/refgenome/Mus_musculus.GRCm38.96.gtf

for FQ1 in `ls *1.fastq-trimmed-pair1.fastq` ; do
    FQ2=`echo $FQ1 | sed 's/1.fastq-trimmed-pair1.fastq/1.fastq-trimmed-pair2.fastq/'`
    BASE=`echo $FQ1 | sed 's/_1.fastq-trimmed-pair1.fastq//'`

STAR --genomeLoad NoSharedMemory --genomeDir $DIR --readFilesIn $FQ1 $FQ2 --runThreadN 30 \
--sjdbGTFfile $GTF --outSAMattributes NH HI NM MD

rm $FQ1 $FQ2
mv Aligned.out.sam ${BASE}.STAR.sam
mv Log.final.out ${BASE}_starlog.txt

( samtools view -uSh ${BASE}.STAR.sam | samtools sort -o ${BASE}.STAR.bam
rm ${BASE}.STAR.sam
samtools index ${BASE}.STAR.bam
samtools flagstat ${BASE}.STAR.bam > ${BASE}.STAR.bam.stats ) &

done

STAR genomeLoad Remove --genomeDir $DIR
wait
ls: cannot access *1.fastq-trimmed-pair1.fastq: No such file or directory
bash: line 24: STAR: command not found

Counting reads from bam files across mouse reference genome


#!/bin/bash

SAF=/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/refgenome/Mus_musculus.GRCm38.96.fulllength.saf
OUT=mrna_fulllen_pe_strrev.mx

#featureCounts -p -Q 10 -T 20 -s 2 -a $SAF -F SAF -o $OUT *bam

Tidy counted matrix


#!/bin/bash

for MX in `ls *mx` ; do
   sed 1d $MX | cut -f1-6 > $MX.chr
    sed 1d $MX | cut -f1,7- | sed 's/-RNA_R1.fastq-trimmed-pair1.fastq.STAR.bam//g' > $MX.fix

done
wait

Filter out low counts genes from matrix

Filtering out low counts genes by running the following filter.sh as

bash filter.sh mrna_fulllen_pe_strrev.mx

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