Trying/exploring the features from Transit: a Python software to analyse transposon sequencing dataset in bacteria

[leilaicruz]

Here a description on this software that captures many of the features that we would like to have in our pipeline concerning:

• Methods of analysis
• Organization
• Documentation
• Usability
• Maintenance

[leilaicruz]

The documentation of the software package can be found here : https://transit.readthedocs.io/en/latest/index.html

• It can be easily installed via downloading, and use it through a GUI or can be used as a python package
• The GUI looks like :
  

Advantadges:

• Serve as an example pipeline that meet many of the requirements we want to reach for our pipeline regarding documentation , ease of use and maintenance. (I could explore the tool thanks of some emails exchange with the maintener)
• It has implemented relevant methods for us concerning the detection of genetic interactions due to significant variability of transposon counts between two conditions , as well as many more statistics on comparing different libraries.

Limitations:

• Currently the methods are to tackle Himar and Tn5 like transposon which are prokaryotic transposons and they have a different insertion profile from our Maize Ac/Ds transposon type.
• Furthermore our expermental conditions do not include sampling and sequence before our selection process , because we measure all "in vivo" , we dont have an "invitro" condition , where we generate also many reads. Because if we sequence before reseeding we wont have that much reads to align back to the genome. The only "in vitro" condition I could think of is measuring the reseed in a diploid where the selection on a gene deletion in one chromosome should play a lesser effect .

Current status:

• We are in touch with the developer/mainteners of the software (https://orca1.tamu.edu/essentiality/transit/) to see how can we adapt their software with our needs if possible.
• Keep exploring all the analysis methods they have in order to implement them as part of our pipeline.
• With the software you can visualize our reads counts on the genome
  
• And have some statistics per dataset: