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Atavistic processing of metagenomics data.

License: MIT License

Python 92.22% Perl 7.16% Shell 0.61%
bioinformatics bioinformatics-pipeline metagenomics metagenomics-binning metagenomics-counts metagenomics-toolkit

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atavide's Issues

Enhance assembly profiles

At the moment we have a simple assembly approach:

  1. Assemble each sample separately
  2. Map reads
  3. Identify unassembled reads
  4. Repeat

There is another approach we might consider:

  1. Assemble x% of all reads (e.g. 0.1% or 1% depending on how big the data sets are..could this be dynamic?)
  2. Map reads and find unassembled reads
  3. Double the % of reads assembled and go to step 1
  4. Keep iterating this until we have assembled everything

Each approach has its pros and cons, of course, but the 2nd approach might remove all the redundant stuff in all the libraries quite quickly.

ctime/atime/mtime

Creating a link to a file modifies its time of last data modification, human-readable

(to test, check stat -c '%w %x %y %z' filename)

and thus snakemake deems necessary to rerun the files because earlier files appear older than subsequent files.

Solution: make all links immediately after creation?

Super-focus taxonomy

We need to revive the super-focus taxonomy because it is based on protein sequence similarity.

migrate renumber to seqtk

There are a couple of places where we use scripts/renumber_merge_fasta.py and rename fasta/q files. We could do this more efficiently with seqtk rename and we already have seqtk as a dependency.

Idenitfy strains

Use the mags and reads to separate out strain level resolution.

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