How to control minimap 2 parameters during uLTRA alignment about ultra HOT 6 CLOSED

Hi,

There are unfortunately no parameters to do this. I can add it to the next version. You can however do it "manually".

1. Locate the file prefilter_genomic_reads.py
   For example, do the following:

(ultra) [kris@rackham3 ~]$ which uLTRA 
/proj/snic2020-15-201/anaconda3/envs/ultra/bin/uLTRA

Then I have file prefilter_genomic_reads.py in

/proj/snic2020-15-201/anaconda3/envs/ultra/lib/python3.8/site-packages/modules/prefilter_genomic_reads.py

2. Change line 32 in prefilter_genomic_reads.py

You can manually edit the prefilter_genomic_reads.py file on line 32 (the call to minimap2).

For instance, if you want to set -w to 5 and k to 13, you can do:

subprocess.check_call([ 'minimap2',  '-ax', 'splice', '--eqx' , '-k', '13, '-w', '5',  '-t', str(nr_cores), refs_path, read_path], stdout=output_file, stderr=stderr_file)

from ultra.

Thanks for your quick response.

It would be great to be able to control the minimap2 flags in a future release of uLTRA.

I am a big fan of uLTRA by the way. We have done extensive benchmarking for aligning direct RNA reads and uLTRA outperforms minimap2 in all contexts we have tested. Cheers.

from ultra.

Ok, I will do that.

I'm really glad to hear that! Especially since we did not include dRNA sequencing in our evaluations.

from ultra.

Thanks, I had one last question.
It says that uLTRA compares mm2 and uLTRA CIGAR strings to decide on the 'best' alignment for a read.

I am wondering what parameters are used in this comparison? Does the algorithm filter for the simplest CIGAR? Or the most aligned bases?

from ultra.

Good question. The computation is currently simple:

Let ultra_matches and mm2_matches be all the matching nucleotides in the alignment of uLTRA and minimap2, respectively. Similarly, let ultra_diff and mm2_diff be the sum of all substitutions, deletions, and insertions in the alignment of uLTRA and minimap2, respectively.

The, the computation is as follows: S = (ultra_matches - ultra_diff) - (mm2_matches - mm2_diff). uLTRA chooses the minimap2 alignment if S is negative.

I should mention that softclips (at either 5' or 3' ends) are disregarded in the above computations. I observed that minimap2 is very good at softclipping at the right positions and may therefore get a better score according to the computation above, even though they are aligned to identical splice sites (uLTRA extends the alignment out fully in ends). The reason I mention this is that uLTRA prints a table at the end of the alignment stage that states how many alignments were preferred for either aligner. This table is not accurate for low score differences (less than 5-10 score difference) because of the above reason. There is certainly room to explore alternative decision-making on the final alignment.

from ultra.

Interesting, especially the last part about softclipping at read-ends.

When using direct RNA reads, one would expect that there is no need to softclip at the 3' end of the read (except for sometimes a few nt, representing sequencing/basecalling errors) since this represents the bona fide end of the RNA molecule.

Cheers!

from ultra.