RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting

Abstract

RNA-Seq is increasingly being used to measure human RNA expression on a genome-wide scale. Expression profiles can be interrogated to identify and functionally characterize treatment-responsive genes. Ultimately, such controlled studies promise to reveal insights into molecular mechanisms of treatment effects, identify biomarkers, and realize personalized medicine. RNA-Seq Reports (RSEQREP) is a new open-source cloud-enabled framework that allows users to execute start-to-end gene-level RNA-Seq analysis on a preconfigured RSEQREP Amazon Virtual Machine Image (AMI) hosted by AWS or on their own Ubuntu Linux machine. The framework works with unstranded, stranded, and paired-end sequence FASTQ files stored locally, on Amazon Simple Storage Service (S3), or at the Sequence Read Archive (SRA). RSEQREP automatically executes a series of customizable steps including reference alignment, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially expressed genes, heatmaps, co-expressed gene clusters, enriched pathways, and a series of custom visualizations. The framework outputs a file collection that includes a dynamically generated PDF report using R, knitr, and LaTeX, as well as publication-ready table and figure files. A user-friendly configuration file handles sample metadata entry, processing, analysis, and reporting options. The configuration supports time series RNA-Seq experimental designs with at least one pre-and one post-treatment sample for each subject, as well as multiple treatment groups and specimen types. All RSEQREP analyses components are built using open-source R code and R/Bioconductor packages allowing for further customization. As a use case, we provide RSEQREP results for a trivalent influenza vaccine (TIV) RNA-Seq study that collected 1 pre-TIV and 10 post-TIV vaccination samples (days 1-10) for 5 subjects and two specimen types (peripheral blood mononuclear cells and B-cells).

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

“…To address intervention cause, we need to perform a sequence of established steps to pre-process the RNA-seq data, by performing quality control, alignment, read count calculation, filtering, normalization and correction. Several pipeline methods codify these steps to enable users to subsequently perform DE analysis and summarize uncovered regulatory mechanisms (Afgan et al, 2018;Torre et al, 2018;Ge et al, 2018;Cornwell et al, 2018;de Jong et al, 2015;Jensen et al, 2017;Kartashov and Barski, 2015;Spurney et al, 2020). However, there are many possible methods to choose from at each step in this process (STAR Methods), not all experimental designs are the same, and downstream results heavily depend on how the RNA-seq data are processed.…”

“…However, there are many possible methods to choose from at each step in this process (STAR Methods), not all experimental designs are the same, and downstream results heavily depend on how the RNA-seq data are processed. Most current pipeline methods do not allow users to compare the many possible methods, providing only one tool for each step (BioJupies (Torre et al, 2018); RSEQREP (Jensen et al, 2017)) even though it is recommended to consider methods for analyses such as DE (Spies et al, 2019). Furthermore, most only perform part of the analysis to infer altered regulatory mechanisms (VIPER (Cornwell et al, 2018); BioWardrobe (Kartashov and Barski, 2015)).…”

“…Importantly, time series DE methods algorithms are still in their infancy and are underutilized (Spies et al, 2019). The few DE methods that do consider temporal dynamics are not yet part of pipeline methods to infer GRNs and are therefore incomplete (Fischer et al, 2018;Jensen et al, 2017;Michna et al, 2016;Nueda et al, 2014;Spurney et al, 2020;Wani and Raza, 2019). For time series DE methods that do exist, some can only analyze specific experimental designs or require normalized and corrected data on input (Sahraeian et al, 2017;Spies and Ciaudo, 2015;Spies et al, 2019).…”

“…We compared these methods in terms of 14 features ( Figure S1). Some of these methods analyze RNA-seq data from raw .fastq files and perform gene ontology, claim to be able to analyze time series RNA-seq data, and/or report gene regulatory networks (Afgan et al, 2018;Torre et al, 2018;Ge et al, 2018;Cornwell et al, 2018;de Jong et al, 2015;Jensen et al, 2017;Kartashov and Barski, 2015;Spurney et al, 2020). TIMEOR fills a gap by providing at least twice as many features to uncover gene regulatory mechanisms from time series data compared to existing methods.…”

Reproducible big data science: A case study in continuous FAIRness

“…But 433 big data tools from outside biomedicine tend to focus on narrow aspects of the analytic 434 problem, leaving researchers on their own when it comes to managing the end-to-end 435 discovery process [49]. 436 Many approaches to reproducibility focus on using mechanisms such as 437 makefiles [50,51], open source software [30,31], specialized programming 438 environments [52], and virtual machines [53] to organize the code and/or data required 439 for a computation. These approaches work well for small data but face challenges when 440 computations must scale to terabytes and span sites.…”