samples module in lib is missing

[Bo-UT]

Hi,

Thanks for developing the MICRO algorithm! I would like to try it with our data.

I first tried the sample data in jabba_data, and got an error "ImportError: cannot import name 'samples' from 'lib' (unknown location) ". I checked the lib folder, and found the samples module is not there.

Could you please upload that module so that I can give a try?

Thanks

Bo

[PijusSimonaitis]

Hi Bo,

Thanks for your message. My bad, I have just added the missing file, let me know how it goes. Do you have a particular research question in mind?

Pijus

[Bo-UT]

Hi Pijus,

Thanks so much for adding the missing module!

I do have a specific question currently. I am using JaBba to call SV, and using gGnome$walks to find out all possible rearrangements. gGnome$walks works well for simple JaBba graphs, and it crashes for complicated graphs, e.g., more than 400 edges. I am wondering if the MICRO could help to find all rearrangements (also defined 'walks') from complicated graphs based on the JaBba results. Thanks again and really appreciate any help!

Regards, Bo

On Sat, Nov 12, 2022 at 8:22 AM PijusSimonaitis @.***> wrote:

Hi Bo,

Thanks for your message. My bad, I have just added the missing file, let me know how it goes. Do you have a particular research question in mind?

Pijus

— Reply to this email directly, view it on GitHub https://github.com/raphael-group/MICRO/issues/1#issuecomment-1312492025, or unsubscribe https://github.com/notifications/unsubscribe-auth/ANEBFVQMDDJAN7ZEBLFQ6DTWH6R3PANCNFSM6AAAAAARZNUFHI . You are receiving this because you authored the thread.Message ID: @.***>

[PijusSimonaitis]

What is "rearrangement" for you? Is it a subset of SVs that were introduced by the same mutation? MICRO can potentially be used to find such subsets of SVs and it should work on complicated graphs, however it is conservative in the following way: we expect that the SVs in a subset have been introduced by the same mutation, but that mutation might have introduced multiple subsets of SVs.

If you think that this might be what you are looking for and you are comfortable with python then you could play with the functions below. If you are not that comfortable with python but still are interested in those subsets of SVs, then I might be able to write a script for you at some point.

from lib import breakpointgraph from lib import jabba_preporcessing DUPLICATION_LENGTH = 1000 DELETION_LENGTH = 100000 jabba_preporcessing.read_json('your jabba file') bg, iidTOlength = jabba_preporcessing.jabba_to_breakpoint_graph(data, DUPLICATION_LENGTH, filename, DELETION_LENGTH) nxcomponents = breakpointgraph.graphTOcomponents(bg)

nxcomponents is a list of graphs (networkx package) and the gray edges of every graph comprise a subsest of SVs introduced by the same mutation..

[Bo-UT]

Hi Pijus,

Thanks a lot! I am not quite sure if your assumption is satisfied in my case. The arrangements are the subset of the reference partitions, and they are formed by the combination from same reference partitions but with different direction and individual partitions. I paste a figure to show what are the arrangements.

Thanks

[PijusSimonaitis]

Terminology in this area is quite obscure. I am still unsure of what do you exactly mean by "reference partition" and "rearrangement". If you are only interested in so called "simple rearrangements" such as deletions, inversions etc., then there should be much better tools out there for you to work with. However, if you actually observe something like your second example "deletion+inversion" in your human data, then, depending on how you construct the colored segments, this might point towards a complex rearrangement that transformed the reference genome in one go as opposed to a sequence of simple rearrangements. MICRO then could be used to identify SVs that were introduced by this event ({2b, 1a}, {1b,4b}).