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🧬 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

Rapid Gene Information Lookup

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

Set Operations

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

Interactive Analysis Report

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 and the user guide.

🛠️ 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.

📊 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.

Dependencies

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

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

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Contributors

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