Evidence map›Paper›PMID 42151675›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Transcriptomics in Space: A Basic R Pipeline for Analyzing Bulk RNA-Sequencing Data.

Gbolaga O Olanrewaju, Nathaniel J Szewczyk, Craig R G Willis

Abstract read
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Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Gbolaga O OlanrewajuDepartment of Biomedical Sciences, Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, USA.
Nathaniel J SzewczykDepartment of Biomedical Sciences, Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, USA.
Craig R G WillisResearch Software and Analytics, University of Exeter, Exeter, UK. c.willis2@exeter.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Space life sciences have been revolutionized by the paradigm shift of biology into a "big data" era. Spaceflight experiments are now routinely generating large-scale molecular ("omics") datasets across different tissues and organisms to help better understand the mechanisms of health decline in space. Large-scale study of gene expression ("transcriptomics") has proven to be a particularly useful for producing new insights into the signaling pathways and key molecules that underpin maladaptation to spaceflight. Technologically, bulk RNA-sequencing provides a simple yet powerful platform for studying transcriptome-wide dysregulation in space. This chapter outlines a basic pipeline for analyzing bulk RNA-sequencing data using R, a well-established programming tool for transcriptomics. The pipeline is demonstrated throughout via application to RNA-sequencing data from a real-life spaceflight experiment, and is provided as a single-resource code script (available at: https://github.com/williscrg/MiMB-Space-RNAseq-Pipeline ) that the reader can use to run the pipeline from start to finish "as is" or with modification if/as desired or required.

Indexed as

Computational BiologyGene Expression ProfilingSequence Analysis, RNASoftwareSpace FlightTranscriptomeBioinformaticsOmicsRRNA-seqRStudioSpaceflightTranscriptomics

Identifiers

PMID42151675

What OpenQuestion holds

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Registered trials

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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.