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 avaialable at Read the Docs: https://nanoraw.readthedocs.io
Installation
Install nanoraw via pip
pip install nanoraw
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 k-mer of interst.
Sequencing Time Anchored Plotting Command:
plot_correction: Plot segmentation before and after correction.plot_multi_correction: Plot multiple raw signals anchored by genomic location.
Other Plotting Command:
plot_kmer: Plot signal quantiles acorss kmers.cluster_most_significant: Clustering traces at bases with significant differences.
Auxiliary Command:
write_most_significant_fasta: Write sequence where signal differs the most significantly between two groups.write_wiggles: Write wiggle files for various attribute of reads.Get additional help for subcommands with nanoraw [command] -h
Requirements
graphmap (https://github.com/isovic/graphmap)
python Requirements:
numpy
scipy
h5py
Optional plotting packages (install R packages with install.packages([package_name]) from an R prompt):
rpy2 (python package; with R installation)
changepoint (for using R’s changepoint package for re-segmentation)
ggplot2 (required for all 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).
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.
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