RNAlysis: analyze your RNA sequencing data without writing a single line of code

Guy Teichman, Dror Cohen, Or Ganon, Netta Dunsky, Shachar Shani, Hila Gingold, Oded Rechavi

Research output: Contribution to journalArticlepeer-review


Background: Among the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code since most available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results. Results: We have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files (adapter trimming, alignment, and feature counting), through exploratory data analysis and data visualization, clustering analysis, and gene set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA sequencing data from different studies using C.elegans nematodes. We note that the software applies equally to data obtained from any organism with an existing reference genome. Conclusions: RNAlysis is suitable for investigating various biological questions, allowing researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses.

Original languageEnglish
Article number74
JournalBMC Biology
Issue number1
StatePublished - Dec 2023


  • Clustering analysis
  • Computational analysis
  • Data visualization
  • Differential expression
  • Gene set enrichment analysis
  • Graphical interface
  • Pipeline
  • RNA sequencing


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