genetic analysis

1. RNA-seq project
1
Dr.Mostafa
Nahed Ahmed

2. bioproject PRJNA272719,
• sample group
• ros1-3_ABA_rep1 mutant-ABA
• ros1-3_ABA_rep2 mutant-ABA
• ros1-3_ABA_rep3 mutant-ABA
• WT_ABA_rep1 WT-ABA
• WT_ABA_rep2 WT-ABA
• WT_ABA_rep3 WT-ABA
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3. Download the raw fastq files using bash script from SRA Explorer
Project sample accession
Copy the bash script for terminal download
3

4. login-20-26:/proj$ nano FQdownload.sh
login-20-26:/proj$ sbatch FQdownload.sh
login-20-26:/proj$ squeue -u mosabdel
Create a bash script named FQdownload.sh
in the terminal using nano function
Run the bash script named FQdownload.sh in the terminal using
sbatch function (I use my server due to sample size o my PC)
Check the job submission and running time
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5. for R1 in *_Arabidopsis_thaliana_RNA-Seq_1.fastq.gz*
do
R2=${R1//_Arabidopsis_thaliana_RNA-
Seq_1.fastq.gz/_Arabidopsis_thaliana_RNA-Seq_2.fastq.gz}
echo $R1 $R2
fastqc $R1 $R2
done
QC using fastqc
Creating a loop for Read1 and Read2, printing the R1 and R2 and then running fastqc
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6. for R1 in *_Arabidopsis_thaliana_RNA-Seq_1.fastq.gz*
do
R2=${R1//_Arabidopsis_thaliana_RNA-Seq_1.fastq.gz/_Arabidopsis_thaliana_RNA-Seq_2.fastq.gz}
R1_trim=${R1//_Arabidopsis_thaliana_RNA-Seq_1.fastq.gz/_Trimmed_Arabidopsis_thaliana_RNA-Seq_1.fastq.gz}
R2_trim=${R2//_Arabidopsis_thaliana_RNA-Seq_2.fastq.gz/_Trimmed_Arabidopsis_thaliana_RNA-Seq_2.fastq.gz}
sample_name=${R1%%_Arabidopsis_thaliana_RNA-Seq_1.fastq.gz*}
fastpReport="${sample_name}_fastpReport.html"
fastp -i $R1 -I $R2 -o $R1_trim -O $R2_trim --html $fastpReport --qualified_quality_phred 30 detect_adapter_for_pe --
trim_poly_x --trim_poly_g
done
QC and trimming using fastp through bash looping
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7. kallisto index -i Arabidopsis_thaliana.TAIR10.cdna.all.fa.gz_kallisto_index Arabidopsis_thaliana.TAIR10.cdna.all.fa.gz
Kallisto index
Ensemble Plants
for R1 in *1.fastq.gz*
do
R2=${R1//1.fastq.gz/2.fastq.gz}
Output=${R1//Trimmed_Arabidopsis_thaliana_RNA-Seq_1.fastq.gz/Count}
kallisto quant --threads 8 -i Arabidopsis_thaliana.TAIR10.cdna.all.fa.gz_kallisto_index -o $Output $R1 $R2
done
Kallisto count
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8. -Extract count and
move to Rstudio
-merge the count
and retrive the
annotation file
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9. Error during plotting the pre and post filtering and normalization
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10. DGElist
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11. Linear model
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12. Volcano plot
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13. DGE data
geneID ros1-3_ABA_rep1 ros1-3_ABA_rep2 ros1-3_ABA_rep3 WT_ABA_rep1 WT_ABA_rep2 WT_ABA_rep3
ACX5 6.19410627 6.08782961 5.93398995 4.99633711 5.05698225 4.75977404
AK3 5.82176273 5.49049027 5.53494047 4.38915343 4.05045033 3.57635916
AT1G08940 0.80754054 0.93280625 1.41320692 3.98832267 3.59269005 3.75920022
AT1G09500 6.2824994 6.45486263 6.44315465 5.63763775 5.37151911 4.47642192
AT1G13970 -0.7754095 -0.0109627 1.03626894 1.88695023 1.40770059 1.9683238
AT1G15415 2.22469414 2.69213741 2.63219241 1.57996759 1.17206261 1.45734827
AT1G18980 1.41058228 2.07054484 2.69495327 2.89012389 3.39711874 3.56850137
AT1G29630 2.54362669 3.03535032 2.01338544 1.57602605 1.23117503 1.55718629
AT1G44608 1.36834932 -0.0104452 1.04000646 2.48284347 2.01966943 2.40882067
AT1G50040 3.35937878 3.18349988 2.7898586 4.37113827 4.0934148 4.10383685
AT1G80450 3.80005442 4.15295398 3.71727958 3.14992208 2.47421196 2.92286865
AT2G03640 5.72709978 5.89293791 5.90533009 6.85305028 6.73441287 7.0644062
AT2G20720 2.48355365 2.31095195 2.16401311 1.55381229 1.46114171 0.79385901
AT2G21830 0.65986382 0.38296674 0.18310782 1.47115886 2.10452702 2.1019507
AT2G21860 2.75678633 2.79646375 2.72492106 3.94819561 3.92295983 3.98239326
AT2G22890 3.4489177 2.21964697 2.65866221 1.55154994 1.82955313 1.5969389
AT2G24700 1.06096638 0.68095889 0.92561238 -0.8064822 -0.0722775 -0.6346444
AT2G24960 0.03305586 -0.7003793 -0.5083691 1.38923551 1.50547946 1.19001472
AT2G25355 3.02642766 3.05873657 3.32565642 1.89547135 1.57602847 1.44943122
AT2G25590 2.52426829 2.07022943 2.85319648 3.70710753 3.70771016 3.76927073
AT2G25964 5.56757055 5.52100276 5.52575698 3.90425778 3.91354544 3.82548297
AT2G25980 -0.1190364 0.68611987 1.14533939 3.32550297 3.2320068 3.15435921
AT2G26360 2.03098079 2.07013865 2.11562058 0.49511592 0.0721216 -0.4275157
AT2G26750 2.07371764 1.5884731 1.62638188 0.25708126 -0.0548241 -0.4034907
AT2G28815 2.29595625 2.55605656 2.69552267 0.77413775 0.80911196 0.71239892
AT2G32140 -0.7764641 0.37624908 0.67505166 2.78548321 2.57461172 2.35205744
AT2G32650 2.76428557 2.40821791 2.79775317 4.90868741 4.7493922 4.93335878
AT2G33830 7.77909628 7.73413703 7.77075318 11.0814478 11.0502026 11.1680475
AT2G35810 6.41105478 6.27776888 6.31093228 3.88045665 4.11198379 4.20477651
AT2G35830 3.3227856 3.46602601 3.2459166 5.63082387 5.88905081 5.71215351
AT2G36540 1.76949602 2.01741942 1.49711874 -0.6316424 -0.6197055 -0.4240393
AT2G36650 -0.1247313 -0.5412232 -0.0163993 1.11806742 1.10606734 1.81565154
AT2G36780 3.49610675 3.58325339 3.89880404 2.57719909 2.76135368 2.41898263
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14. Functional gene enrichment
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