Academic Journal

Synteny plot quality control with SyntenyQC.

Bibliographic Details
Title: Synteny plot quality control with SyntenyQC.
Authors: Kirkwood, Timothy D J, Connolly, Jack A, Ang, Ee Lui, Zhao, Huimin, Takano, Eriko, Breitling, Rainer
Source: Bioinformatics; Dec2025, Vol. 41 Issue 12, p1-5, 5p
Subject Terms: Genomics, Bioinformatics software, Gene mapping, Application software, Python programming language, Data scrubbing
Abstract: Summary SyntenyQC is a data pre-processing tool for the construction of synteny plots. It supports genomic data collection, annotation and dereplication to facilitate (and in some cases fundamentally enable) the construction of informative synteny plots. Availability and implementation SyntenyQC is a command line app developed using Python version 3.10 and tested using pytest. SyntenyQC is available on PyPI (https://pypi.org/project/SyntenyQC) under the MIT License, along with a detailed user tutorial. Package tests can be viewed at https://github.com/Tim-Kirkwood/SyntenyQC. [ABSTRACT FROM AUTHOR]
Copyright of Bioinformatics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  Data: Synteny plot quality control with SyntenyQC.
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  Data: <searchLink fieldCode="AR" term="%22Kirkwood%2C+Timothy+D+J%22">Kirkwood, Timothy D J</searchLink><br /><searchLink fieldCode="AR" term="%22Connolly%2C+Jack+A%22">Connolly, Jack A</searchLink><br /><searchLink fieldCode="AR" term="%22Ang%2C+Ee+Lui%22">Ang, Ee Lui</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Huimin%22">Zhao, Huimin</searchLink><br /><searchLink fieldCode="AR" term="%22Takano%2C+Eriko%22">Takano, Eriko</searchLink><br /><searchLink fieldCode="AR" term="%22Breitling%2C+Rainer%22">Breitling, Rainer</searchLink>
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  Data: Bioinformatics; Dec2025, Vol. 41 Issue 12, p1-5, 5p
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  Data: <searchLink fieldCode="DE" term="%22Genomics%22">Genomics</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics+software%22">Bioinformatics software</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+mapping%22">Gene mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Application+software%22">Application software</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Data+scrubbing%22">Data scrubbing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Summary SyntenyQC is a data pre-processing tool for the construction of synteny plots. It supports genomic data collection, annotation and dereplication to facilitate (and in some cases fundamentally enable) the construction of informative synteny plots. Availability and implementation SyntenyQC is a command line app developed using Python version 3.10 and tested using pytest. SyntenyQC is available on PyPI (https://pypi.org/project/SyntenyQC) under the MIT License, along with a detailed user tutorial. Package tests can be viewed at https://github.com/Tim-Kirkwood/SyntenyQC. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Bioinformatics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1093/bioinformatics/btaf626
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        Text: English
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              M: 12
              Text: Dec2025
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