pyDNase is a suite of tools for analysing DNase-seq data - pyDNase comes with several analysis scripts covering several common use cases of DNase-seq analysis, and also an implementation of the Wellington, Wellington 1D, and Wellington-boostrap footprinting algorithms.
An easy-to-understand DNase-seq footprinting tutorial can be found here and full documentation can be accessed here
Many people currently analyzing DNase-seq data are using tools designed for ChIP-seq work, but may be inappropriate for DNase-seq data where one is less interested in the overlaps of sequenced fragments, but the site at which the cut occurs (the 5' most end of the aligned sequence fragment).
pyDNase has an underlying API to interface with a sorted and indexed BAM file from a DNase-seq experiment, allowing efficient and easy random access of DNase-seq cut data from any genomic location, e.g.
>>> import pyDNase >>> reads = pyDNase.BAMHandler(pyDNase.example_reads()) >>> reads["chr6,170863500,170863532,+"] {'+': [0,0,0,1,0,0,1,1,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1], '-': [0,10,1,0,1,0,4,9,0,1,0,2,1,0,0,0,0,0,3,0,6,3,0,0,0,1,1,1,3,0,3,6]}
Querying the BAMHandler object returns a dictionary containing lists with DNase cut counts on the positive reference strand (+), and cuts on the negative reference strand (-). pyDNase efficiently caches the cut data queried, so that multiple requests from the same genomic locations do not require repeated lookups from the BAM file (this can be disabled). See the full documentation for full details.
to install pyDNase, run:
$ pip install pyDNase
for full documentation go to: http://pythonhosted.org/pyDNase/
If you're having any troubles, please send an email to [email protected] and I'll do my best to help you out. If you notice any bugs, then please raise an issue over at the github repo. If you require more formal training on the analysis of DNase-seq or ATAC-seq data, I am available for consultancy. Likewise, if you are a commercial entity looking for a support contract, please get in touch.
I highly encourage contributions! This is my first software development project - send any pull requests this way. I'm particularly interested in cool analysis scripts that anyone has written.
Note
If you use pyDNase or the Wellington algorithm in your work, please cite the following papers.
Piper et al. 2013. Wellington: A novel method for the accurate identification of digital genomic footprints from DNase-seq data, Nucleic Acids Research 2013; doi: 10.1093/nar/gkt850
Piper et al. 2015. Wellington-bootstrap: differential DNase-seq footprinting identifies cell-type determining transcription factors, BMC Genomics 2015; doi:10.1186/s12864-015-2081-4
Copyright (C) 2015 Jason Piper. This work is licensed under the MIT license, see LICENCE.TXT for details. If you require the use of this software under a difference license, please email me at [email protected].