GuyTeichman/RNAlysis

Useful links: Documentation | Source code | Bug reports | | | | |

What is RNAlysis?

RNAlysis is a Python-based software for analyzing RNA sequencing data. RNAlysis allows you to build customized analysis pipelines suiting your specific research questions, going all the way from exploratory data analysis and data visualization through clustering analysis and gene-set enrichment analysis.

What can I do with RNAlysis?

• Filter your gene expression matrices, differential expression tables, fold change data, and tabular data in general.
• Normalize your gene expression matrices
• Visualise, explore and describe your sequencing data
• Find global relationships between sample expression profiles with clustering analyses and dimensionality reduction
• Create and share analysis pipelines
• Perform enrichment analysis with pre-determined Gene Ontology terms/KEGG pathways, or with used-defined attributes
• Perform enrichment analysis on a single ranked list of genes, instead of a test set and a background set

To get an overview of what RNAlysis can do, read the tutorial and the user guide.

RNAlysis supports gene expression matrices and differential expression tables in general, and integrates in particular with Python's HTSeq-count and R's DESeq2.

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.

• numpy
• pandas
• scipy
• matplotlib
• numba
• requests
• scikit-learn
• scikit-learn-extra
• hdbscan
• seaborn
• statsmodels
• joblib
• tqdm
• appdirs
• grid_strategy
• pyyaml
• UpSetPlot
• matplotlib-venn
• xlmhglite
• pairwisedist
• typing_extensions
• PyQt5
• qdarkstyle
• defusedxml
• cutadapt
• aiohttp
• aiodns
• aiolimiter
• Brotli
• networkx
• pyvis

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 In:
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

Development Lead

• Guy Teichman: [email protected]

Contributors

• 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.