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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.
Instantly access gene information from various biological databases with a simple right-click.
Perform advanced set operations to extract and analyze specific subsets of your data.
Easily generate comprehensive and intuitive analysis reports to promote reproducibility. Track the entire analysis path with just a click!
- 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.
You can either install RNAlysis as a stand-alone app, or via PyPI. To learn how to install RNAlysis, visit the Installation page.
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.
All of RNAlysis's dependencies can be installed automatically via PyPI.
- numpy
- polars
- pandas
- scipy
- matplotlib
- numba
- requests
- scikit-learn
- kmedoids
- hdbscan
- seaborn
- statsmodels
- joblib
- tqdm
- appdirs
- grid_strategy
- pyyaml
- UpSetPlot
- matplotlib-venn
- xlmhglite
- pairwisedist
- typing_extensions
- PyQt6
- qdarkstyle
- defusedxml
- cutadapt
- aiohttp
- aiodns
- aiolimiter
- Brotli
- networkx
- pyvis
- tenacity
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
- Guy Teichman: [email protected]
- Dror Cohen
- Or Ganon
- Netta Dunsky
- Shachar Shani
- Mintxoklet
- Bipin Kumar
- Matthias Wilm
- sandyl27
- clockgene
- NeuroRookie
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.