Re-run Directory: /projects/tomato_genome/fnb/dataprocessing/SRP441343
SL4: /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4
SL5: /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL5

Prefetch SRR Script:

#! /bin/bash

#SBATCH --job-name=prefetch_SRR
#SBATCH --partition=Orion
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem=4gb
#SBATCH --output=%x_%j.out
#SBATCH --time=24:00:00

cd   /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4
module load sra-tools/2.11.0
vdb-config --interactive

files=(
SRR24836276
SRR24836277
SRR24836278
SRR24836279
SRR24836280
SRR24836281
SRR24836282
SRR24836283
SRR24836284
SRR24836285
SRR24836286
SRR24836287
SRR24836288
SRR24836289
SRR24836290
SRR24836291
SRR24836292
SRR24836293
SRR24836294
SRR24836295
SRR24836296
SRR24836297
SRR24836298
SRR24836299
SRR24836300
SRR24836301
SRR24836302
SRR24836303
SRR24836304
SRR24836305
SRR24836306
SRR24836307
SRR24836308
SRR24836309
SRR24836310
SRR24836311
SRR24836312
SRR24836313
SRR24836314
SRR24836315
SRR24836316
SRR24836317
SRR24836318
SRR24836319
SRR24836320
SRR24836321
SRR24836322
SRR24836323
SRR24836324
SRR24836325
SRR24836326
SRR24836327
SRR24836328
SRR24836329
)

for f in "${files[@]}"; do echo $f; prefetch $f;  done

Execute:

chmod u+x prefetch.slurm

sbatch prefetch.slurm

Faster Dump Script:

#! /bin/bash

#SBATCH --job-name=fastqdump_SRR
#SBATCH --partition=Orion
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem=40gb
#SBATCH --output=%x_%j.out
#SBATCH --time=24:00:00
#SBATCH --array=1-54

#setting up where to grab files from
file=$(sed -n -e "${SLURM_ARRAY_TASK_ID}p"  /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/Sra_ids.txt)


cd /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4
module load sra-tools/2.11.0

echo "Starting faster-qdump on $file";

cd /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/$file

fasterq-dump ${file}.sra

perl /projects/tomato_genome/scripts/validateHiseqPairs.pl ${file}_1.fastq ${file}_2.fastq

cp ${file}_1.fastq /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/${file}_1.fastq
cp ${file}_2.fastq /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/${file}_2.fastq 

echo "finished"

Execute:

chmod u+x fasterdump.slurm

sbatch fasterdump.slurm

Nextflow Pipeline ran successfully with SL4 & SL5 genome
Directory:

• /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL5
• /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4

MultiQC report notes: No errors or warnings were present in the reports. The output files are named 'SRP441343_SL5_multiqc_report.html' & 'SRP441343_SL4_multiqc_report.html'.

Next steps:
Commit CSV and multiqc report to Flavonoid repo on bitbucket
Change sorted bam names
Create junction files
Create Coverage graphs

Launch renameBams.sh script:
./renameBams.sh
Launch Scaled Coverage graphs script:
./sbatch-doIt.sh .bam bamCoverage.sh >jobs.out 2>jobs.err
Launch Junction files script:
./sbatch-doIt.sh .bam find_junctions.sh >jobs.out 2>jobs.err

Directories:

• /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/results/star_salmon/
• /projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL5/results/star_salmon/

Reviewer:
Check that files have reasonable sizes (no "zero" size files, for example)
Check that every "FJ.bed.gz" file has a corresponding "FJ.bed.gz.tbi" index file
Check that every bam file has a corresponding "FJ.bed.gz" file
Check that every bam file has a corresponding "scaled.bedgraph.gz" file
Check that every "scaled.bedgraph.gz" has a corresponding "scaled.bedgraph.gz.tbi"

Branch: https://bitbucket.org/mdavis4290/molly-splicing-analysis/branch/IGBF-3500

• SRP441343_SL4_multiqc_report.html
• SRP441343_SL5_multiqc_report.html
• SRP441343.csv

SL4 Folder:

Overall NFCore structure appears as expected.

We see the expected # of bam, bai, bedgraph ad bed files, 54!
/projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/results/star_salmon$ ll *bam | wc -l
54
rreid2@str-i1:/projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/results/star_salmon$ ll *bai | wc -l
54
rreid2@str-i1:/projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/results/star_salmon$ ll *gz | wc -l
108
rreid2@str-i1:/projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/results/star_salmon$ ll *bedgraph.gz | wc -l
54
rreid2@str-i1:/projects/tomato_genome/fnb/dataprocessing/SRP441343/nfcore-SL4/results/star_salmon$ ll *bed.gz | wc -l
54
There are 54 tbi files as expected. (*scaled.bedgraph.gz.tbi)

All of the .err files are 0 bytes in size. As expected. All other files have at least some size!

scaled.bedgraph.gz.tbi files are all around 60-70kb in size.
All of the bamfiles are 1.3GB to 2.7GB in size as expected.
.bai files are look fine.

Next up SL5.

SL5 folder

The NFcore file structure is as expected.

54 files for each bam, bai, bed.gz. and bed.gz.tbi

Bam files are expected.

All .err files are 0.

All looks good.

Next up:
csv files and html in bitbucket.

In https://bitbucket.org/mdavis4290/molly-splicing-analysis/branch/IGBF-3500

the HTML files both exist with content.

The .csv file looks to have the full 55 lines (54 expts plus a header) with the proper fastq in each column.

I call this done!

Thank you! Robert Reid

PR: https://bitbucket.org/hotpollen/splicing-analysis/pull-requests/13

Testing suggestions:

• Open the newly added .html files in a Web browser to check that they didn't get corrupted somehow (by mistake, of course
• Check that the .html files mention the expected SRA identifiers
• Check that the SRA identifiers listed in the added csv files match up with the .html files
• Check that the csv file SRA identifiers are repeated in the expected way in the expected columns (e.g., sample names match up with file names)
• Make a note of any interesting (or not so interesting!) differences in results obtained for SL4 and SL5, recalling whether or not SL5 has more or less gene models and genes than SL4

Testing:

• html files open and report accurate information
• SRA identifiers are present
• csv SRA identifiers match the SRA identifiers in the html files
• the fastq file SRA identifiers match the sample SRA identifiers in the csv file
• There seems to be more 'reads mapped' for SL5 than SL4. But for SL4 there are more '% Proper Pairs' than SL5.

Next step: prepare data to be moved from the cluster to IGB quick load. Refer to IGBF-3499

Moving ticket to done!