Skip to main content

GenSkew is an application for computing and plotting nucleotide skew data.

For a more detailed description and the online version of genskew take a look at the website: genskew.csb.univie.ac.at

GenSkew calculates the incremental and the cumulative skew of two selectable nucleotides for a given sequence according to this formula: Skew = (nucleotide1 - nucleotide2) / (nucleotide1 + nucleotide2)

The results are provided as data table and as graphical plot. The global minimum and maximum are displayed in the cumulative graph. The minimum and maximum of a GC-skew can be used to predict the origin of replication (minimum) and the terminus location (maximum) in prokaryotic genomes.

There are three versions of this Program: Genskew_univiecube (the python library described below), Genskew_cc (a commandline client) and GUIskew (the Graphical Version of Genskew).

Installing the program with the command you can copy above will install all three of them. The Graphical Interface can be started via the command python -m GUIskew. By calling python3 -m genskew -h you will see a detailed description how to use the commandline interface. It can analyze multiple sequences in one command.

For using the library you first have to specify the sequence as an object:

import genskew_univiecube as gs

sequence = "GATCCTAGATTAAGC"

name = gs.Object(sequence, "G", "C", stepsize, windowsize)

In this example the sequence is a string and the first nucleotide is G and the second is C. Stepsize and Windowsize don't have to be specified, if they are not specified they will be automatically calculated to best fit the Graph. This is usefull if multiple sequences are processed after another.

After the Object is defined, we need to generate the results:

import genskew_univiecube as gs

sequence = gs.gen_sequence(filelocation) name = gs.Object(sequence, "G", "C", stepsize, windowsize) result = gs.Object.gen_results(name)

In this example the sequence is generated by calling gen_sequence, this takes a fasta or genbank file and outputs a string with the sequence in it. The results can be retrieved as follows:

import genskew_univiecube as gs

sequence = gs.gen_sequence(filelocation) name = gs.Object(sequence, "G", "C", stepsize, windowsize) result = gs.Object.gen_results(name)

print(result.skew) gs.plot_sequence(result, filelocation, outputfolder, output_filetype, dpi, skewi)

There are different results: .skew (which will output the skew as a listof y values), .x (which will output the corresponding x values), .cumulative (which will output the cumulative skew as y values), .max_cm_position and .min_cm_position (outputs the x value of the max / min cumulative), .stepsize and .windowsize (outputs as integer), .nuc_1 and .nuc_2 (outputs the first and second nucleotide as a string)

plot_sequence plots and saves a graph of the skew. The arguments dpi, out_filetype and outputfolder are optional, the default output file type is png and the outputfolder is by default the folder in which the sequence file was (filelocation). The dpi is calculated according to the size of the graph.

The function input_files(file_locations) will check the given path for fasta , gb or .gz files and then returns everything in a list. file_location has to be a list and can contain direct paths or folders.

As of 0.1.0 the SkewIT¹ Index has been added. It can be activated by having the "skewi" parameter as "true".

Note, that in this example only one sequence can be analyzed at once.

Updatelog Genskew-univiecube

Version 0.1.2

fixed the SkewIT graph sometimes not displaying the right nucleotides on the axis label

Version 0.1.1

general Bug fix

updated the Readme

Version 0.1.0

Added the SkewIT¹ algorithm to GenSkew

Additional Parameter for plot_sequence

Version 0.0.9

First finnished release of Genskew

REFERENCES:

1: SkewIT, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008439 (06.04.2022), SkewIT: The Skew Index Test for large scale GC Skew analysis of bacterial genomes, Jennifer Lu, Steven L. Salzberg

Metadata

Release files for Genskew-univiecube 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for Genskew-univiecube 0.1.2
File Size Uploaded
Genskew-univiecube-0.1.2.tar.gz 5.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for Genskew-univiecube 0.1.2
File Interpreter ABI Platform
Genskew_univiecube-0.1.2-py3-none-any.whl Python 3 none any Details

Total release size: 11.0 kB

Release files / Genskew-univiecube-0.1.2.tar.gz

Download URL Genskew-univiecube-0.1.2.tar.gz
Size 5.4 kB
Tags Source
SHA-256 checksum
How to use checksums
3f206c61594d0ef480686d429be539d1c5d6ca139624d17a98aa49e32199fa70
BLAKE2b-256 checksum
How to use checksums
b53ff5cf117a7909afb0ce3f23d1455a1038bc55d3c463c7937247f3b50aad6e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/4.11.3 pkginfo/1.7.0 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.10

Release files / Genskew_univiecube-0.1.2-py3-none-any.whl

Download URL Genskew_univiecube-0.1.2-py3-none-any.whl
Size 5.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
ceefd379f6b84448aa964962ca2c76380119cfd1987aff5720f2eb59d3cb65da
BLAKE2b-256 checksum
How to use checksums
ee9dfb29fadebbbc0ba6dbfc4296780bf9d168f1c168ac95a741990e382af9a9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.1 importlib_metadata/4.11.3 pkginfo/1.7.0 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.10

Release history Release notifications | RSS feed

0.1.3

2 release files

This release

0.1.2 This release

2 release files

0.1.1

2 release files

0.1.0

2 release files

0.0.9

2 release files

0.0.8

2 release files

0.0.7

2 release files

0.0.6

2 release files

0.0.5

2 release files

0.0.4

2 release files

0.0.3

2 release files

0.0.2

2 release files

0.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page