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Analysis of nanopore sequencing data.

Project Description
## Summary
This package provides tools for the analysis of raw nanopore sequencing data, including correction of basecalls and visualization.

## Full Documentation
Full documentation available at [Read the Docs](https://nanoraw.readthedocs.io)

## Installation
Install nanoraw without plotting dependencies (base genome_resquiggle algorithm and text outputs: wig and fasta)
```
pip install nanoraw
```

Install nanoraw with plotting dependencies (requires separate installation of R packages ggplot2 and cowplot)
```
pip install nanoraw[plot]
```

Install bleeding edge via github
```
pip install git+https://github.com/marcus1487/nanoraw.git
```

## Usage

```
nanoraw -h
nanoraw [command] [options]
```

#### Main Comand (Must be run before any other commands):
genome_resquiggle Re-annotate raw signal with genomic aignement of existing basecalls.

#### Genome Anchored Plotting Commands:
plot_max_coverage Plot signal in regions with the maximum coverage.
plot_genome_location Plot signal at defined genomic locations.
plot_motif_centered Plot signal at regions centered on a specific motif.
plot_max_difference Plot signal where signal differs the most between two groups.
plot_most_significant Plot signal where signal differs the most significantly between two groups.
plot_motif_with_stats Plot signal from several regions and test statistics centered on a motif of interst.

#### Sequencing Time Anchored Plotting Commands:
plot_correction Plot segmentation before and after correction.
plot_multi_correction Plot multiple raw signals anchored by genomic location.

#### Other Plotting Commands:
cluster_most_significant Clustering traces at bases with significant differences.
plot_kmer Plot signal quantiles acorss kmers.

#### Auxiliary Commands:
write_most_significant Write sequence where signal differs the most significantly between two groups.
write_wiggles Write wiggle files for nanopore signal values, coverage, and statistics.

> Get additional help for subcommands with `nanoraw [command] -h`

## Requirements

- HDF5 (<http://micro.stanford.edu/wiki/Install_HDF5#Install>)
- graphmap (<https://github.com/isovic/graphmap>)
OR
- BWA-MEM (<http://bio-bwa.sourceforge.net/>)

#### python Requirements (handled by pip):
- numpy
- scipy
- h5py

#### Optional packages for plotting (install R packages with `install.packages([package_name])` from an R prompt):
- rpy2 (along with an R installation)
- ggplot2 (required for any plotting subcommands)
- cowplot (required for plot_motif_with_stats subcommand)

## Citation
Stoiber, M.H. et al. De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing. bioRxiv (2016).

http://biorxiv.org/content/early/2017/04/10/094672

## Legal
nanoraw v.1 Copyright (c) 2016, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Innovation and Partnerships department at IPO@lbl.gov referring to " nanoraw v.1 (2016-199)."

NOTICE. This software was developed under funding from the U.S. Department of Energy. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, prepare derivative works, and perform publicly and display publicly. Beginning five (5) years after the date permission to assert copyright is obtained from the U.S. Department of Energy, and subject to any subsequent five (5) year renewals, the U.S. Government is granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.
Release History

Release History

This version
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0.5

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0.4.2

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0.4.1

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0.4

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0.3.1

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0.3

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0.2

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0.1

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