https://github.com/nanoporetech/panga

https://github.com/nanoporetech/panga

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/nanoporetech/panga
• Owner: nanoporetech
• License: other
• Created: 2018-06-22T11:05:28.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2020-10-13T19:12:55.000Z (almost 6 years ago)
• Last Synced: 2025-04-06T08:02:15.312Z (over 1 year ago)
• Language: Python
• Size: 7.39 MB
• Stars: 2
• Watchers: 24
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
Read Builder

--------------

A somewhat generic system for classifying the channel state throughout the

course of an experiment.

Here, a *read* is a contiguous run of events/raw data points that correspond to an

identifiable channel state.

We follow the same three-step process used in previous implementations, but allow

for some useful additions.

 1. **Determine read boundaries:** Calculates start and end coordinates of

    reads, and outputs reads as raw or event data. Naively, neighbouring reads

    are ultimately expected to be assigned different classes.

 

 2. **Calculate metrics on reads:** Takes event (or raw) data provided by

    step 1. and calculates summary statistics (of arbitrary complexity). May

    optionally take exogenous data concerning the state of a channel, e.g.

    saturation status.

    

 3. **Classify reads:** Takes the matrix of metrics for a read (and possibly the

    metrics of surrounding reads) to output an additional *metric*: the

    classification.

The smart cookie notices that this system is rather *backwards*: one would hope

to label read boundaries by running a classifier over a channel and outputting

boundaries where there is a state change. The above does not exclude this;

step 1. could be arbitrarily complex. The framework requires only that it

output read data (and optionally key-value pairs of meta data).

The key differences between the current framework and previous incarnations are:

  * Clear separation of the three steps.

  * The ability to calculate classes based on metrics of surrounding reads.

  * Defined interface to each step such that the methodology used in each can

    be changed depending on the application.

  * Low memory use: reads are processed individually rather than storing all

    data for a channel in memory.

  * Multiprocessing across channels.

Installation

------------

Panga installs into a virtual environment. Installation should should be as simple as running  

    Make install 

On Ubuntu, you will need to install the virtualenv and python-dev packages.

    sudo apt-get install python-virtualenv python-dev 

Running read_builder

--------------------

Panga read_builder is installed into a virtual environment. To run it first activate your environment

    source panga_directory/venv/bin/activate 

To analyse Minknow reads use:

    read_builder --fast5 path_to_bulk_hdf --config $(panga_config_dir)/standard_minknow_classes.yml --outpath read_builder --summary_file read_summary.txt 

Example configs for more advanced analyses such as redetection of read boundaries, read metrics and classifications can be found in the panga config directory:

    ls $(panga_config_dir)