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.

Background: Amongst 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 the majority of 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, 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 data from different studies using C. elegans nematodes. We note that the software is equally applicable to data obtained from any organism. Conclusions: RNAlysis is suitable for investigating a variety of biological questions, and allows 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.

Gliomas are the most frequent primary tumors of the brain. Glioma progression is regulated by the tumor microenvironment, which is mainly composed of tumor‐associated microglia (TA‐MG) and monocyte‐derived macrophages (MDM). Recent studies have highlighted the distinct properties of these cells in glioma progression. However, their spatiotemporal alteration during tumor progression has not been fully explored. Using a genetic lineage tracing approach, we show that TA‐MG and MDMs differ in their spatiotemporal distribution and interaction with other components of the glioma microenvironment. MDM were present only inside the tumor, whereas TA‐MG accumulated both outside and inside the tumor. However, TA‐MG was eliminated from the tumor mass as the tumor progressed. Depletion of MDM led to enhanced occupancy of TA‐MG in the tumor core, indicating that TA‐MG elimination was regulated by MDM. TA‐MG and MDM are heterogeneous cell populations whose compositions and properties can change during tumor progression. Finally, MG, TA‐MG and MDM were enriched in the perivascular area (PVA) compared to more distal blood vessel‐associated areas. However, inside the tumor, the MDM enrichment in PVA was higher than that in TA‐MG. Collectively, we established that TA‐MG and MDM exhibit different spatiotemporal features in glioma, suggesting distinctive roles during tumor progression.

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