NetSeekR: a network analysis pipeline for RNA-Seq time series data

Srivastava, Himangi; Ferrell, Drew; Popescu, George V.

doi:10.1186/s12859-021-04554-1

Cited by 11 publications

(3 citation statements)

References 28 publications

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“…However, it lacks the flexibility to choose from different preprocessing methods. Similarly, the tool described in [49] primarily focuses on network analysis of RNA-seq data but also offers the capability to perform GO overrepresentation analysis. Yet, it provides only limited options in terms of method combinations.…”

Section: Discussionmentioning

confidence: 99%

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Paton,

Gabor,

Flores

et al. 2023

Preprint

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Transcriptomics, and in particular RNA-Seq, has become a widely used approach to assess the molecular state of biological systems. To facilitate its analysis, many tools have been developed for different steps, such as filtering lowly expressed genes, normalisation, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis using enrichment of gene sets, such as pathways, which typically provides the basis for interpretation and follow-up experiments. To address this gap, we introduce FLOP (FunctionaL Omics Processing), a comprehensive nextflow-based workflow that combines various methods for preprocessing and downstream enrichment analysis, allowing users to perform end-to-end analyses of count level transcriptomic data. We illustrate FLOP capabilities on diverse datasets comprising samples from end-stage heart failure patients and cancer cell lines in both basal and drug-perturbed states. We found that the correlation between gene set enrichment analysis results can vary significantly for alternative pipelines. Additionally, we observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space, especially in settings with limited statistical power. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings.

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Section: Discussionmentioning

confidence: 99%

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Paton,

Gabor,

Flores

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Tools such as ARPIR (Spinozzi et al, 2020) and NetSeekR (Srivastava et al, 2022), can take users all the way from the alignment of reads and differential expression analysis through GO enrichment and other tertiary analyses such as gene network analysis. Other tools like ideal (Marini et al, 2020), PIVOT (Zhu et al, 2018), and DEBrowser (Kucukural et al, 2019) provide users with a graphical interface to perform differential expression analysis and enrichment analysis.…”

Section: Discussionmentioning

confidence: 99%

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

Teichman

Cohen²,

Ganon

et al. 2022

Preprint

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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.

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“…A network analysis was conducted to analyze the types of interventions used in the identified DCT cases [ 20 21 ]. We visualized how tokens, meaningful units of text, were connected within a sentence.…”

Section: Methodsmentioning

confidence: 99%

The landscape of decentralized clinical trials (DCTs): focusing on the FDA and EMA guidance

Park,

Huh,

Chung

et al. 2024

Transl Clin Pharmacol

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Decentralized clinical trials (DCTs) consist of off-site trial-related procedures referred to as decentralized elements. We aimed to provide an overview of the landscape of DCTs by comparing regulatory guidance reports and analyzing decentralized elements from clinical trial registries. Two guidance reports on DCTs published by the U.S. Food and Drug Administration and the European Medicines Agencies were summarized and analyzed. Both guidance publications commonly emphasized an assessment of the appropriateness of decentralized elements along 2 axes: patient safety and data integrity. DCT cases were identified from ClinicalTrials.gov by searching with 6 keywords: decentralized, remote, mobile, digital, virtual, and hybrid. Cases where the keyword was used in a non-DCT context, such as digital flexor tendon, were excluded by means of natural language processing. A total of 4,874 trials were identified as DCT cases, with annual increases, especially after 2020. The most common keywords were ‘mobile’ and ‘digital’ (36.2% and 24.8%, respectively). Interventions in the DCT cases were analyzed by means of a network analysis. Behavioral and technological tokens were frequently combined, such as ‘rehabilitation’ and ‘app.’ Drugs were used in only 1.8% of the DCT cases. Of these, most drugs had been approved previously (96.8%) and were in oral formulation (67.2%). Most of the DCT cases identified in this study involved simple interventions and low-risk drugs. These characteristics were in accordance with the common recommendations in the DCT guidance publications.

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NetSeekR: a network analysis pipeline for RNA-Seq time series data

Cited by 11 publications

References 28 publications

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

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

The landscape of decentralized clinical trials (DCTs): focusing on the FDA and EMA guidance

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