Evidence map›Paper›PMID 30026912›Full record

ArticleF1000Research2017

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

Travis L Jensen, Michael Frasketi, Kevin Conway, Leigh Villarroel, Heather Hill, Konstantinos Krampis, Johannes B Goll

Abstract read
In one paragraph

Article in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. F1000Research · 2021
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Travis L JensenVaccine and Infectious Disease Department , The Emmes Corporation, Rockville, MD, USA.ORCID https://orcid.org/0000-0002-0322-0469
Michael FrasketiIT Operations, The Emmes Corporation, Rockville, MD, USA.
Kevin ConwayIT Operations, The Emmes Corporation, Rockville, MD, USA.
Leigh VillarroelIT Operations, The Emmes Corporation, Rockville, MD, USA.
Heather HillVaccine and Infectious Disease Department , The Emmes Corporation, Rockville, MD, USA.
Konstantinos KrampisDepartment of Biological Sciences, Hunter College, City University of New York, New York, NY, USA.
Johannes B GollVaccine and Infectious Disease Department , The Emmes Corporation, Rockville, MD, USA.ORCID https://orcid.org/0000-0002-9968-4080

Funding

NIAID NIH HHS HHSN272200800013CNIAID NIH HHS HHSN272201500002C
6 · The paper itself

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 via a Docker container or installation script. 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).

Indexed as

cloud computingdifferential gene expressionpathway enrichmentreproducible researchRNA-SeqRSEQREPtranscriptomicstrivalent influenza vaccine

Identifiers

PMID30026912
PMCPMC6039931

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.