Comparison of predicted results and TSSpredator

[mwittep]

Description

From the predicted results and an established method such as TSSpredator, we want to identify which TSS are found in both and which are unique for each class. Also, if they have unique characteristics, such as distance to the next annotated gene or gene expression level.

Acceptance

• [x] Create three sets of TSS based on their genomic position. Allow a certain threshold, since we might expect minimal differences for similar TSS
• [x] Gather different ideas on how to characterize the different groups, for example, distance to the next annotated genes or the expression level of the gene. Create a Wiki entry for this justifying each characterization.
• [x] Develop the functions that return this information.
• [x] Save this information in a file that should allow the user to download the data for further analysis.
• [x] Return the information in a way that it can be used by the client for visualization.

Definition of Ready

If this issues has been discussed with the team and everybody agrees that it is implementable within one iteration, you can assign the label ready to it.

[AmelieFl]

the issue was solved on branch 28-parsing-MasterTable The name of the wiki entry is: TSS classification the functions for classifying the TSSs are in the file app/TSSclassifier.py. The function find_common_tss() returns a data frame that only includes TSS found by both our prediction and TSS predator. The function df_to_response() in app/server.py by julian converts the dataframes to csv files so that the information can be sent to the frontend.

[AronWichtner]

Review:

I reviewed the file GFFParser.py on Branch dev (since branch 28 was merged into it) This review also covers #38 since the GFF and annotation of TSS is also required in this Issue

Wiki entry:

• Your wiki entry has a good structure and your choice of groups makes sense.

Code:

• Readability: The code is readable but some methods are way to long and hard to read. You used appropriate names for variables and methods.

• Functionality: The Enum 'TSSType' is redundant with the Enum 'TSS_TYPES' of MasterTableParser.py and could be extracted in a separate file and then imported. The rest of the code looks good.

• Documentation: the most important methods have internal documentation. Some more comments especially in long and hard to read functions would be helpful

• Testing: your code is tested