Last updated: 2021-02-19

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Knit directory: Human_Development_RNAseq_bulk/

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Introduction

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.

Human bulk RNA-seq were performed using single end sequencing method and below are the scripts for trimming and mapping single end sequencing read.

Used libraries and functions

  • pigz/2.4
  • fastx-toolkit/0.0.13
  • star/2.5.3a
  • samtools/1.8
  • parallel
  • subread/1.5.0

Scripts for trimming and mapping .fastq.gz

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/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/star
GTF=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/star/Homo_sapiens.GRCh38.96.gtf

for FQZ in `ls *fastq.gz` ; do

FQ=`echo $FQZ | sed 's/.gz//'`

pigz -dc $FQZ | fastq_quality_trimmer -t 20 -l 20 -Q33 > $FQ

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

rm $FQ
mv Aligned.out.sam ${FQ}.STAR.sam
mv Log.final.out ${FQ}_starlog.txt

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

done
STAR genomeLoad Remove --genomeDir $DIR
wait

ls: cannot access *fastq.gz: No such file or directory
bash: line 25: STAR: command not found

Here are the outcomes

Caption for the picture.

Caption for the picture.

Counting reads from bam files


#!/bin/bash

SAF=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/Homo_sapiens.GRCh38.96.fulllength.saf
OUT=hrna_dev_mf_fulllen_se_strrev_q30.mx

# featureCounts -Q 30 -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 | sed 's/_R1.fastq.STAR.bam//g' > $MX.all_unfiltered.csv
   sed 1d $MX | cut -f1-6 | sed 's/_R1.fastq.STAR.bam//g' > $MX.chr
   sed 1d $MX | cut -f1,7- | sed 's/_R1.fastq.STAR.bam//g' > $MX.all.fix
   sed 1d $MX | cut -f1,7-23 | sed 's/_R1.fastq.STAR.bam//g' > $MX.all.MvsF.fix
done
wait

ls: cannot access *mx: No such file or directory

Filter out low counts genes from matrix

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

bash filter.sh hrna_dev_mf_fulllen_se_strrev_q30.mx.all.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