ArticleBMC genomics2016
QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 45 citations in OpenAlex.
- Transcriptomic and Proteomics Analysis of a Lipid-Loaded HepaRG Model for Steatosis Reveals Altered Regulation in Lipid and Xenobiotic Metabolism.Current drug metabolism · 2025Article
- A comprehensive workflow for optimizing RNA-seq data analysis.BMC genomics · 2024Article
- DNA methylation of insulin signaling pathways is associated with HOMA2-IR in primary myoblasts from older adults.Skeletal muscle · 2023Article
- transXpress: a Snakemake pipeline for streamlined de novo transcriptome assembly and annotation.BMC bioinformatics · 2023Article
- Molecular and Functional Characterization of Human Intestinal Organoids and Monolayers for Modeling Epithelial Barrier.Inflammatory bowel diseases · 2023Article
- Searchlight: automated bulk RNA-seq exploration and visualisation using dynamically generated R scripts.BMC bioinformatics · 2021Article
- A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.PLoS computational biology · 2021Article
- The Adipocyte Acquires a Fibroblast-Like Transcriptional Signature in Response to a High Fat Diet.Scientific reports · 2020Article
- BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation.BMC bioinformatics · 2019Article
- Article
- Dysregulated protocadherin-pathway activity as an intrinsic defect in induced pluripotent stem cell-derived cortical interneurons from subjects with schizophrenia.Nature neuroscience · 2019Article
- Article
- Mucosal Gene Expression in Pediatric and Adult Patients With Ulcerative Colitis Permits Modeling of Ideal Biopsy Collection Strategy for Transcriptomic Analysis.Inflammatory bowel diseases · 2018Article
- High-dimensional immune phenotyping and transcriptional analyses reveal robust recovery of viable human immune and epithelial cells from frozen gastrointestinal tissue.Mucosal immunology · 2018Article
- anexVis: visual analytics framework for analysis of RNA expression.Bioinformatics (Oxford, England) · 2018Article
- VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis.BMC bioinformatics · 2018Article
- Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion.Scientific reports · 2018Article
- QuickMIRSeq: a pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing.BMC bioinformatics · 2017Article
- B Cell-Intrinsic Role for IRF5 in TLR9/BCR-Induced Human B Cell Activation, Proliferation, and Plasmablast Differentiation.Frontiers in immunology · 2017Article
- On the study of microbial transcriptomes using second- and third-generation sequencing technologies.Journal of microbiology (Seoul, Korea) · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRNA sequencing (RNA-seq), a next-generation sequencing technique for transcriptome profiling, is being increasingly used, in part driven by the decreasing cost of sequencing. Nevertheless, the analysis of the massive amounts of data generated by large-scale RNA-seq remains a challenge. Multiple algorithms pertinent to basic analyses have been developed, and there is an increasing need to automate the use of these tools so as to obtain results in an efficient and user friendly manner. Increased automation and improved visualization of the results will help make the results and findings of the analyses readily available to experimental scientists.
resultsBy combing the best open source tools developed for RNA-seq data analyses and the most advanced web 2.0 technologies, we have implemented QuickRNASeq, a pipeline for large-scale RNA-seq data analyses and visualization. The QuickRNASeq workflow consists of three main steps. In Step #1, each individual sample is processed, including mapping RNA-seq reads to a reference genome, counting the numbers of mapped reads, quality control of the aligned reads, and SNP (single nucleotide polymorphism) calling. Step #1 is computationally intensive, and can be processed in parallel. In Step #2, the results from individual samples are merged, and an integrated and interactive project report is generated. All analyses results in the report are accessible via a single HTML entry webpage. Step #3 is the data interpretation and presentation step. The rich visualization features implemented here allow end users to interactively explore the results of RNA-seq data analyses, and to gain more insights into RNA-seq datasets. In addition, we used a real world dataset to demonstrate the simplicity and efficiency of QuickRNASeq in RNA-seq data analyses and interactive visualizations. The seamless integration of automated capabilites with interactive visualizations in QuickRNASeq is not available in other published RNA-seq pipelines.
conclusionThe high degree of automation and interactivity in QuickRNASeq leads to a substantial reduction in the time and effort required prior to further downstream analyses and interpretation of the analyses findings. QuickRNASeq advances primary RNA-seq data analyses to the next level of automation, and is mature for public release and adoption.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.