Rnalysis
Description
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.
Environment Modules
Run module spider rnalysis to find out what environment modules are available for this application.
System Variables
- HPC_RNALYSIS_DIR - installation directory
- HPC_RNALYSIS_BIN - executable directory
Citation
If you publish research that uses rnalysis you have to cite it as follows:
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