mdshw5 / pyfaidx

Efficient pythonic random access to fasta subsequences
https://pypi.python.org/pypi/pyfaidx
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Consolidate long name methods to all use `long_name` #109

Closed michaelbarton closed 7 years ago

michaelbarton commented 7 years ago

How is this? I think this is what you requested.

codecov-io commented 7 years ago

Codecov Report

Merging #109 into master will decrease coverage by 0.25%. The diff coverage is 33.33%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master     #109      +/-   ##
==========================================
- Coverage   76.72%   76.47%   -0.26%     
==========================================
  Files           2        2              
  Lines         795      799       +4     
==========================================
+ Hits          610      611       +1     
- Misses        185      188       +3
Impacted Files Coverage Δ
pyfaidx/cli.py 47.84% <0%> (ø) :arrow_up:
pyfaidx/__init__.py 88.18% <40%> (-0.45%) :arrow_down:

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mdshw5 commented 7 years ago

Hey @michaelbarton. This looks like a great contribution, and I have no problem merging it. Thanks for taking the time to make the change - I'll push out a new minor version!

michaelbarton commented 7 years ago

Thanks Matt for this library. I have been using it recently a project and I found it very useful. https://gitlab.com/michaelbarton/gaet/

mdshw5 commented 7 years ago

Looks like a pretty cool tool you've got there. The idea is the assess the quality of assemblies based on previous feature annotation? What's a common use case?

michaelbarton commented 7 years ago

Yes that's exactly it. If you have a reference assembly and you want to benchmark an assembler you can compare the generated assembly's annotations with that of the reference's annotation. We're using it to determine which assemblers produce complete rRNA operons and so forth.

For example we genomes for which we have both pacbio and illumina sequencing data. We use the pacbio assembly as the reference since they are generally very high quality, then benchmark various assemblers using the illumina data.