Analyses
The rawdata obtained were quality checked by using the software FastQC. The commands used for this step can be seen in the code page section fastaqc. Thereafter, a scaffold was chosen to continue the other analyses with. The scaffold 10 was chosen and a script was done using the program multiqc (the script can be seen in the code page section multiqc) so that the quality reports for scaffold 10 were gathered into a summary report instead. The generated summary reports from illumina, pacbio and the trancriptome were downloaded.
Illumina quality report
Figure 1: A graph of the mean quality scores of the illumina reads.
As one can see in figure 1, the quality of the illumina reads are quite high, since the curves are located in the green area. These might have a high quality due to the fact that illumina reads are short and have a low error rate. It is important that these reads have a high quality since the reads are later going to be used for correcting the genome assembly by mapping the reads to the genome assembly. Since the quality is good, the correcting of the genome assembly will probably be good as well.
Pacbio quality report
Figure 2: A graph of the mean quality score of the pacbio read.
From figure 2, one can conclude that the quality of the pacbio read across the base pairs is pretty bad since the curve is located in the red area. This can be due to the fact that pacbio read is long and has a higher error rate than the illumina reads.
Transcriptomic quality report
Trimmed:
Figure 3: A graph of the mean quality scores of the trimmed transcriptomic reads.
The trancriptomic reads have high quality across the base pairs since the curves are located in the green area, as one can see in figure 3 above. This high quality is important for the analysis step when the reads are supposed to be mapped against the genome assembly. This will probably result in a good mapping since the quality is high!
Untrimmed:
Figure 4: A graph of the mean quality scores of the untrimmed transcriptomic reads.
As one can see in the figure 4 above, the quality of the untrimmed transcriptomic reads across the base pairs are quite high. The quality is good since the curves are located in the green area.
Trimmed data that previously was untrimmed:
Figure 5: A graph of the mean quality scores of the newly trimmed transcriptomic reads.
In this figure, figure 5, one can see the newly trimmed transcriptomic reads that have been trimmed with trimmomatic. The quality of these trimmed transcriptomic reads are quite high since the curves are located in the green area. If comparing with figure 4, the quality is quite similar for 2 of the reads. However, this graph also have 2 additional reads which have a lower quality than the other 2 in this graph. This may be due to the fact that the reads are now cropped compared to before, since they are now trimmed.
The pacbio scaffold 10 data was assembled using the program canu. A script was made and can be seen in the code page section canu and was runned with the command sbatch.
The untrimmed transcriptomic data was trimmed using the program trimmomatic. A script was made and can be seen in the code page section trimmomatic and was runned with the command sbatch.
Mapping of the illumina reads (scaffold 10 1P and scaffold 10 2P) and the assembled pacbio scaffold 10 data was done with the program BWA so that the assembly thereafter could be corrected with the illumina reads. The code can be seen in the code page section mapping with bwa and the script was runned with the command sbatch.
Improvement of the assembly was made by using the program pilon that required the assembly as imput as well as the aligned bam file from the previous step. A script was made and can be seen in the code page section pilon.
Quality assessment was made with quast. The script can be seen in the code page section quast.
The program repeatmasker was used on the fasta file obtained from the pilon analysis. This program was used in order to improve the prediction accuracy by masking the repeats in the genome. A script was made and can be seen in the code page section repeatmasker.
The program star was used in order to map the trimmed RNA-seq reads to the assembled masked genome. A script was made and can be seen in the code page section star.