Project planning
I chose paper 4, which is about the whole genome assembly of the durian fruit. The aim of this project is to re-analyze the data from the paper and figure out which genes and pathways that are important for the different processes in the durian fruit. This will be done in order to see whether the results from this project will differ from the results in the paper, as well as to gain knowledge about different bioinformatic tools and methods.
preprocessing qualitycheck med fastaqc för illumina qualitycheck med inside canu för pacbio trimming med trimmomatic för illumina
genome assembly pacbio assembly canu för pacbio correcta assembly med pilon för illumina reads ta bort repeats med repeatmasker
transriptome qualitycheck med fastaqc för illumina trimmining med trimmomtatic för illumina rna assembly med trinity
quality assesmenent med quast för genome assembly och rna assembly
annotation braker pga vi har eukaryot structural för genome eggNOGmapper functional för genome
mapper (aligner) BWA för genome input illumina reads och genome STAR för transcriptome input rna seq och genome
differential expression htseq för transcriptome
The analyses I will perform can be seen in the diagram below.
transcriptome data is rna representative
The project must be finished before may 18. Milestones for when certain steps in this project must be finished can be seen below in a bullet list:
- 6/4 - Preprocessing
- 19/4 - Genome assembly
- 21/4 - Correct assembly
- 27/4 - Assembly quality assesment
- 11/5 - Annotation
- 17/5 - Analysis of result
The data handled in this project is from an eukaryot and a whole genome assembly is to be made. This will result in a big amount of data, which means a lot of space is needed in order to store the data. This may cause a problem if only storing in the home directory since I only got 32 Gb. However, if more space is needed, I will create a personal folder within the project directory and save the data there. I will also compress big data files in order to save space!
transcriptome data, pacbio reads and illumina reads
The metadata is going to be stored in an excel sheet. I will have one sample per row and one variable per column, informative names and avoiding spaces when naming, as well as be consistent in order to avoid confusion.
The code and the data (such as figures, text, results) are going to be separated in my working directory. The code is going to be in a repository with version control (github) so I can track the coding process and be able to go back to a previous code that works if the current code has errors. I will have a readme file where I explain the workflow and in which order the codes are executed. The files will have informative and unique name so I do not get confused when I want to find a specific file. Folder and file names will start with a number, since then I can keep track on the order of when files/folders were created. Big data files will be compressed in order to save space.