..
   This file is part of khmer, https://github.com/dib-lab/khmer/, and is
   Copyright (C) 2010-2015 Michigan State University
   Copyright (C) 2015 The Regents of the University of California.
   It is licensed under the three-clause BSD license; see LICENSE.
   Contact: khmer-project@idyll.org
   
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******************************
khmer's command-line interface
******************************

The simplest way to use khmer's functionality is through the command
line scripts, located in the `scripts/
<https://github.com/dib-lab/khmer/tree/stable/scripts>`__ directory of the
khmer distribution.  Below is our documentation for these scripts.  Note
that all scripts can be given ``-h``/``--help`` which will print out
a list of arguments taken by that script.

Scripts that use k-mer counting tables or k-mer graphs take an
:option:`-M <load-into-counting.py -M>` parameter, which sets the maximum
memory usage in bytes. This should generally be set as high as possible; see
:doc:`choosing-table-sizes` for more information.

1. :ref:`scripts-counting`
2. :ref:`scripts-partitioning`
3. :ref:`scripts-diginorm`
4. :ref:`scripts-read-handling`

.. note::
 
   Almost all scripts take in either FASTA and FASTQ format, and
   output the same.

   Gzip and bzip2 compressed files are detected automatically. 

.. _scripts-counting:

k-mer counting and abundance filtering
======================================

.. autoprogram:: load-into-counting:get_parser()
        :prog: load-into-counting.py

.. autoprogram:: abundance-dist:get_parser()
        :prog: abundance-dist.py

.. autoprogram:: abundance-dist-single:get_parser()
        :prog: abundance-dist-single.py

.. autoprogram:: filter-abund:get_parser()
        :prog: filter-abund.py

.. autoprogram:: filter-abund-single:get_parser()
        :prog: filter-abund-single.py

.. autoprogram:: trim-low-abund:get_parser()
        :prog: trim-low-abund.py

.. autoprogram:: count-median:get_parser()
        :prog: count-median.py

.. autoprogram:: unique-kmers:get_parser()
        :prog: unique-kmers.py

.. _scripts-partitioning:

Partitioning
============

.. autoprogram:: do-partition:get_parser()
        :prog: do-partition.py

.. autoprogram:: load-graph:get_parser()
        :prog: load-graph.py

See :program:`extract-partitions.py` for a complete workflow.

.. autoprogram:: partition-graph:get_parser()
        :prog: partition-graph.py

See 'Artifact removal' to understand the stoptags argument.

.. autoprogram:: merge-partitions:get_parser()
        :prog: merge-partition.py

.. autoprogram:: annotate-partitions:get_parser()
        :prog: annotate-partitions.py

.. autoprogram:: extract-partitions:get_parser()
        :prog: extract-partitions.py
 
Artifact removal
----------------

The following scripts are specialized scripts for finding and removing
highly-connected k-mers (HCKs).  See :doc:`partitioning-big-data`.

.. autoprogram:: make-initial-stoptags:get_parser()
        :prog: make-initial-stoptags.py

.. autoprogram:: find-knots:get_parser()
        :prog: find-knots.py

.. autoprogram:: filter-stoptags:get_parser()
        :prog: filter-stoptags.py

.. _scripts-diginorm:

Digital normalization
=====================

.. autoprogram:: normalize-by-median:get_parser()
        :prog: normalize-by-median.py

.. _scripts-read-handling:

Read handling: interleaving, splitting, etc.
============================================

.. autoprogram:: extract-long-sequences:get_parser()
        :prog: extract-long-sequences.py

.. autoprogram:: extract-paired-reads:get_parser()
        :prog: extract-paired-reads.py

.. autoprogram:: fastq-to-fasta:get_parser()
        :prog: fastq-to-fasta.py

.. autoprogram:: interleave-reads:get_parser()
        :prog: interleave-reads.py

.. autoprogram:: readstats:get_parser()
        :prog: readstats.py

.. autoprogram:: sample-reads-randomly:get_parser()
        :prog: sample-reads-randomly.py

.. autoprogram:: split-paired-reads:get_parser()
        :prog: split-paired-reads.py