RNA sequencing continues to grow in popularity as an investigative tool for biologists. 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. RNAlysis is suitable for investigating various biological questions, allowing researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. We note that the software applies equally to data obtained from any organism with an existing reference genome. We demonstrate the use of RNAlysis by analyzing RNA sequencing data from different studies using C. ![]() ![]() RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. 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. We have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. ![]() Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results. These hurdles are further heightened for researchers who are not experienced in writing computer code since most available analysis tools require programming skills. 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.
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