install

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wget https://github.com/mahulchak/quickmerge/archive/v0.2.tar.gz
tar zxf v0.2.tar.gz
bash make_merger.sh

Running

The simplest way to run ‘merger’ is to use the python wrapper ‘merge_wrapper_v2.py’:

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 merge_wrapper.py hybrid_assembly.fasta self_assembly.fasta

Which of my assemblies should I use as query and reference?

There is no golden rule for this. Quickmerge was developed with the idea that every genome is different and that you know best about the genome you are assembling. All you have to do is to keep in mind that quickmerge joins contigs in one assembly (query assembly) using the contigs from the reference assembly. So the merged assembly receives the most sequences from the query assembly, and the reference assembly provides only the sequences that bridge gaps in the query assembly. As a result, the merged assembly size and completeness (as evaluated by BUSCO or CEGMA metric) would be more like the query genome. If you believe that completeness and size of the assembly 1 is truer representation of the actual genome, then you may use that assembly as the query. Please see below for strategies on one and two rounds of quickmerge.

USAGE 1: One round of quickmerge

Use this when your PacBio assembly (PBcR/canu/Falcon) is much more fragmented than the hybrid/DBG2OLC assembly. Use your DBG2OLC assembly as the reference. This is more likely to happen when coverage is low (<50X). In this case, it is expected that you will run quickmerge only once. E.g.

  1. Choose your query and reference assembly. You can try one quickmerge run with assembly1 as the reference and another quickmerge run with assembly1 as the query.
  2. Choose all the parameters.
  3. Run quickmerge. Use the merged assembly with the better N50.

USAGE 2: One round merged assembly does not have both best N50 and correct assembly size

This is our preferred method, especially when the PacBio only assembly is much more contiguous than the DBG2OLC or hybrid assembly (more likely to happen when coverage is >50X).

  1. Choose assembly1 as query and assembly2 as reference.
  2. Run quickmerge and obtain N50, assembly size, etc.
  3. Set assembly2 as query and assembly1 as query.
  4. Run quickmerge and obtain N50, assembly size, etc.
  5. Does either of your merged assemblies (from 2 or 4) give you the best N50 and the correct genome size at the same time?
  6. If not, choose the merge assembly that is more contiguous and lets call it assembly3.
  7. Lets say that size and other metrics (e.g. BUSCO or CEGMA metric) of assembly1 (or assembly2) is more correct. Now run quickmerge with assembly1 (or assembly2) as the query and assembly3 as the reference. If you are not using the wrapper python script, you should rename the contigs in assembly 3 before running quickmerge.Simply dosed -i 's/^>/>N_/g' assembly3.fastabefore running quickmerge.
  8. Now your merged assembly has the best N50 without sacrificing on the other metrics.

Reference

https://github.com/mahulchak/quickmerge/wiki