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RNAlysis

Analyze your RNA sequencing data without writing a single line of code

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/learn @GuyTeichman/RNAlysis
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0/100

Supported Platforms

Universal

README

.. image:: https://raw.githubusercontent.com/GuyTeichman/RNAlysis/master/docs/source/logo.png :target: https://guyteichman.github.io/RNAlysis :width: 250 :align: center :alt: logo

Documentation <https://guyteichman.github.io/RNAlysis>_ | Source code <https://github.com/GuyTeichman/RNAlysis>_ | Bug reports <https://github.com/GuyTeichman/RNAlysis/issues>_ | |pipimage| | |versionssupported| | |githubactions| | |coveralls| | |downloads|

🧬 What is RNAlysis?

RNAlysis is a powerful, user-friendly software that allows you to analyze your RNA sequencing data without writing a single line of code. This Python-based tool offers a complete solution for your RNA-seq analysis needs, from raw data processing to advanced statistical analyses and beautiful visualizations, all through an intuitive graphical interface.

🎥 See RNAlysis in Action

|quicksearch|

Instantly access gene information from various biological databases with a simple right-click.

|setops|

Perform advanced set operations to extract and analyze specific subsets of your data.

|autoreport|

Easily generate comprehensive and intuitive analysis reports to promote reproducibility. Track the entire analysis path with just a click!

🚀 Key Features

  • Code-Free Analysis: Perform complex analyses with just a few clicks.
  • Comprehensive Analysis Pipeline: From data import to enrichment analysis, all in one place.
  • Interactive Visualizations: Explore your data with dynamic, publication-ready graphs.
  • Customizable Workflows: Build and share analysis pipelines tailored to your research questions.
  • Integration with Popular Tools: Seamless compatibility with DESeq2, kallisto, bowtie2, and more.
  • Rapid Gene Information Lookup: Instantly access gene information from various biological databases.
  • Advanced Set Operations: Easily extract and analyze specific subsets of your data.
  • Reproducible Research: Generate comprehensive, interactive analysis reports with a single click.

To get an overview of what RNAlysis can do, read the tutorial <https://guyteichman.github.io/RNAlysis/build/tutorial.html>_ and the user guide <https://guyteichman.github.io/RNAlysis/build/user_guide.html>_.

🛠️ How do I install it?

You can either install RNAlysis as a stand-alone app, or via PyPI. To learn how to install RNAlysis, visit the Installation page <https://guyteichman.github.io/RNAlysis/build/installation.html>_.

📊 How do I use it?

If you installed RNAlysis as a stand-alone app, simply open the app ("RNAlysis.exe" on Windows, "RNAlysis.dmg" on MacOS) and wait for it to load (it may take a minute or two, so be patient!).

If you installed RNAlysis from PyPi, you can launch RNAlysis by typing the following command::

rnalysis-gui

Or through a python console::

>>> from rnalysis import gui
>>> gui.run_gui()

In addition, you can write Python code that uses RNAlysis functions as described in the programmatic interface user guide <https://guyteichman.github.io/RNAlysis/build/user_guide.html>_.

Dependencies

All of RNAlysis's dependencies can be installed automatically via PyPI.

  • numpy <https://numpy.org/>_
  • polars <https://pola.rs/>_
  • pandas <https://pandas.pydata.org/>_
  • scipy <https://www.scipy.org/>_
  • matplotlib <https://matplotlib.org/>_
  • numba <http://numba.pydata.org/>_
  • requests <https://github.com/psf/requests/>_
  • scikit-learn <https://scikit-learn.org/>_
  • kmedoids <https://github.com/kno10/python-kmedoids>_
  • hdbscan <https://github.com/scikit-learn-contrib/hdbscan>_
  • seaborn <https://seaborn.pydata.org/>_
  • statsmodels <https://www.statsmodels.org/>_
  • joblib <https://joblib.readthedocs.io/en/latest/>_
  • tqdm <https://github.com/tqdm/tqdm>_
  • appdirs <https://github.com/ActiveState/appdirs>_
  • grid_strategy <https://github.com/matplotlib/grid-strategy>_
  • pyyaml <https://github.com/yaml/pyyaml>_
  • UpSetPlot <https://github.com/jnothman/UpSetPlot>_
  • matplotlib-venn <https://github.com/konstantint/matplotlib-venn>_
  • xlmhglite <https://github.com/GuyTeichman/xlmhglite>_
  • pairwisedist <https://github.com/GuyTeichman/pairwisedist/>_
  • typing_extensions <https://github.com/python/typing_extensions>_
  • PyQt6 <https://www.riverbankcomputing.com/software/pyqt/>_
  • qdarkstyle <https://github.com/ColinDuquesnoy/QDarkStyleSheet>_
  • defusedxml <https://https://github.com/tiran/defusedxml>_
  • cutadapt <https://github.com/marcelm/cutadapt>_
  • aiohttp <https://docs.aiohttp.org/>_
  • aiodns <https://github.com/saghul/aiodns>_
  • aiolimiter <https://aiolimiter.readthedocs.io/>_
  • Brotli <https://github.com/google/brotli>_
  • networkx <https://networkx.org>_
  • pyvis <https://github.com/WestHealth/pyvis>_
  • tenacity <https://github.com/jd/tenacity>_

Credits

How do I cite RNAlysis?

If you use RNAlysis in your research, please cite::

Teichman, G., Cohen, D., Ganon, O., Dunsky, N., Shani, S., Gingold, H., and Rechavi, O. (2023).
RNAlysis: analyze your RNA sequencing data without writing a single line of code. BMC Biology, 21, 74.
https://doi.org/10.1186/s12915-023-01574-6

If you use the CutAdapt adapter trimming tool in your research, please cite::

Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads.
EMBnet.journal, 17(1), pp. 10-12.
https://doi.org/10.14806/ej.17.1.200

If you use the kallisto RNA sequencing quantification tool in your research, please cite::

Bray, N., Pimentel, H., Melsted, P. et al.
Near-optimal probabilistic RNA-seq quantification.
Nat Biotechnol 34, 525–527 (2016).
https://doi.org/10.1038/nbt.3519

If you use the bowtie2 aligner in your research, please cite::

Langmead, B., and Salzberg, S.L. (2012).
Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012 94 9, 357–359.
https://doi.org/10.1038/nmeth.1923

If you use the ShortStack aligner in your research, please cite::

Axtell, MJ. (2013).
ShortStack: Comprehensive annotation and quantification of small RNA genes. RNA 19:740-751.
https://doi.org/10.1261/rna.035279.112

If you use the DESeq2 differential expression tool in your research, please cite::

Love MI, Huber W, Anders S (2014).
“Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.”
Genome Biology, 15, 550.
https://doi.org/10.1186/s13059-014-0550-8

If you use the Limma-Voom differential expression pipeline in your research, please cite::

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015).
limma powers differential expression analyses for RNA-sequencing and microarray studies.
Nucleic Acids Res. 43, e47–e47.
https://doi.org/10.1093/nar/gkv007

Law, C.W., Chen, Y., Shi, W., and Smyth, G.K. (2014).
Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.
Genome Biol. 15, 1–17.
https://doi.org/10.1186/gb-2014-15-2-r29

If you use the HDBSCAN clustering feature in your research, please cite::

 L. McInnes, J. Healy, S. Astels, hdbscan: Hierarchical density based clustering
Journal of Open Source Software, The Open Journal, volume 2, number 11. 2017
https://doi.org/10.1371/journal.pcbi.0030039

If you use the XL-mHG single-set enrichment test in your research, please cite::

Eden, E., Lipson, D., Yogev, S., and Yakhini, Z. (2007).
 Discovering Motifs in Ranked Lists of DNA Sequences. PLOS Comput. Biol. 3, e39.
https://doi.org/10.1371/journal.pcbi.0030039>doi.org/10.1371/journal.pcbi.0030039</a>

Wagner, F. (2017). The XL-mHG test for gene set enrichment. ArXiv.
https://doi.org/10.48550/arXiv.1507.07905

If you use the Ensembl database in your research, please cite::

Martin FJ, Amode MR, Aneja A, Austine-Orimoloye O, Azov AG, Barnes I, et al.
Ensembl 2023. Nucleic Acids Res [Internet]. 2023 Jan 6;51(D1):D933–41.
doi.org/10.1093/nar/gkac958

If you use the PANTHER database in your research, please cite::

Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H.
PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci [Internet]. 2022 Jan 1;31(1):8–22.
doi.org/10.1002/pro.4218

If you use the OrthoInspector database in your research, please cite::

Nevers Y, Kress A, Defosset A, Ripp R, Linard B, Thompson JD, et al.
OrthoInspector 3.0: open portal for comparative genomics. Nucleic Acids Res [Internet]. 2019 Jan 8;47(D1):D411–8.
doi.org/10.1093/nar/gky1068

If you use the PhylomeDB database in your research, please cite::

Fuentes D, Molina M, Chorostecki U, Capella-Gutiérrez S, Marcet-Houben M, Gabaldón T.
PhylomeDB V5: an expanding repository for genome-wide catalogues of annotated gene phylogenies. Nucleic Acids Res [Internet]. 2022 Jan 7;50(D1):D1062–8.
doi.org/10.1093/nar/gkab966

If you use the UniProt gene ID mapping service in your research, please cite::

The UniProt Consortium.
UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res [Internet]. 2023 Jan 6;51(D1):D523–31.
doi.org/10.1093/nar/gkac1052

Development Lead

  • Guy Teichman: guyteichman@gmail.com

Contributors

  • Dror Cohen
  • Or Ganon
  • Netta Dunsky
  • Shachar Shani
  • Mintxoklet <https://github.com/Mintxoklet>_
  • Bipin Kumar <https://github.com/kbipinkumar>_
  • Matthias Wilm
  • sandyl27 <https://github.com/sandyl27>_
  • clockgene <https://github.com/clockgene>_
  • NeuroRookie <https://github.com/NeuroRookie>_

This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookie

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GuyTeichman/RNAlysis

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Python

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Audited on Feb 18, 2026

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