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cheny19 avatar cheny19 commented on May 30, 2024 1

Sorry, I don't know how I missed your comment and just see it now.

We are also trying to add the option to control error rate, but failed with some attempts. The thing is the error rate is controlled by the length and position of each error, both are determined empirically as Markov Model and statistical mixture models. We still don't have a clear relationship between the error rate and these factors.

As to the code you are pointing to, that is about the alignment rate, namely how many reads can be aligned in one dataset. It's not the error rate of aligned reads.

Thanks for you advice.

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Psy-Fer avatar Psy-Fer commented on May 30, 2024 1

Using numpy.random.choice
http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.choice.html

switching a base out for another, inserting or deleting but at lower probability.
Also use some list for loops for poly runs of AAAA or TTTT and add or remove a letter or 2.

Probably not all that representative of "real" error that the MinION generates. Not sure how to validate, But was looking at which point some tools "break" depending on how messy the data could get.

Kind of moved on to just using the event data and dynamic time warping for some other project. But will no doubt be using something like NanoSim in the future

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Psy-Fer avatar Psy-Fer commented on May 30, 2024

Hello,

Ahh yes you are correct. My mistake :)

Would a close approximation error model be useful, that could be applied to the output to give more error than it currently does. I have just been introducing noise into the reads NanoSim creates and using the original read alignment, compared to the new "noisy" read alignment to calculate error.

Not all that great, but it kinda works. haha.

Regards,

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cheny19 avatar cheny19 commented on May 30, 2024

For curiosity, how are you introducing noise to the output reads and how do you determine if it works or not?

Thanks!

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