Research method
RNA Sequencing (RNA-seq)
RNA sequencing inventories RNA molecules in a sample by converting them to a cDNA library and counting short reads. Depending on the protocol it can quantify gene expression, assemble a transcriptome de novo, or detect non-canonical RNAs such as circular back-splice junctions. The output is a table of counts or assembled transcripts, not a proof that any one transcript causes the phenotype that motivated the experiment.
Biologists reach for RNA-seq when they need an unbiased picture of what a tissue, infection, or mixed culture is expressing rather than a handful of qPCR targets. It answers 'which transcripts differ, and by how much, under this contrast?' Its main limitation is that differential expression and homology-based pathway labels are not functional proof, and library chemistry (poly(A) versus total RNA, mixed-species RNA) changes what can be seen.
Evidence
What the evidence shows
Drawn from 20 studies in this library. Each finding starts with a plain-language takeaway, then the denser detail. Supports means evidence for a finding; Challenges means evidence against a stated position; Qualifies marks scope with a short note on each study’s contribution. Challenged positions are labeled — they are not findings.
Protocol choice decides which molecules even appear. A computational pipeline on non-poly(A) RNA-seq was required to catalogue mammalian circular RNAs via back-splice junctions; most confident circRNAs used canonical GT-AG splice signals, and circular fractions varied by locus and cell type. A standard poly(A) mRNA-seq experiment would have missed them.
De novo assemblies turn RNA-seq into a catalogue for species without a finished genome. Illumina sequencing of Aloe vera root and leaf produced on the order of 43,000 CDS per tissue with annotations tied to secondary-metabolite pathways; bacteria-challenged sea bass head kidney and spleen yielded a large immune transcriptome including hepcidin, lysozyme and RAG annotations.
Study Role Design N Population Outcome Aloe vera medicinal pathway transcriptome Supports Computational / modellingIllumina RNA-seq de novo Trinity assembly of Aloe vera root and leaf transcriptomes Two tissues; ~43,443 root and ~43,178 leaf CDS — transcriptome resource, not a sample-N study Aloe vera root and leaf tissues Annotated transcriptome resources linked to secondary-metabolite pathways Immune transcriptome of bacteria-challenged sea bass Supports Animal / in-vitroRNA-seq of head kidney/spleen from bacteria-challenged vs mock-challenged sea bass Tissue RNA pooled from 15 fishes per preparation — no unpooled individual analytic N Lateolabrax japonicus (Japanese sea bass) Immune-related transcriptome after bacterial challenge Time-of-day and mixed-culture design change the biological contrast RNA-seq is actually making. Tomato–DC3000 resistance was highest at 08:00 and susceptibility at 20:00, with red light the most disease-suppressive wavelength among those tested; in a chemostat co-culture, L. thermotolerans contributed about 24% of transcripts anaerobically versus about 8% aerobically.
Study Role Design N Population Outcome Does red light help tomatoes fight bacteria? Supports Animal / in-vitroDiurnal/wavelength treatments of tomato–Pto DC3000 with RNA-seq of red-light effects N=12 · 12 single-end RNA-seq samples from four treatments Tomato plants challenged with Pseudomonas syringae pv. tomato DC3000 Red-light modulation of disease resistance and related transcriptomes What do co-fermenting yeasts express together? Supports Animal / in-vitroContinuous co-culture of S. cerevisiae and L. thermotolerans under anaerobic vs aerobic conditions Two biological replicates per fermentation condition; species held near equal abundance ~24 h Mixed Saccharomyces cerevisiae and Lachancea thermotolerans cultures Condition-dependent transcriptome shares and mixing-responsive genes RNA-seq can be joined to genetics and to metabolites, still without proving causation. In 77 Yoruba LCLs, inter-individual methylation tracked genotypes and correlated with RNA-seq expression; in Panax ginseng, MEP-pathway expression patterns were taken to contribute to ginsenoside biosynthesis with IspD predicted as a key enzyme.
Field disease RNA-seq separates states more cleanly than it names a pathogen. Hundreds of DEGs separated white-syndrome coral tissue from healthy tissue, while adjacent and healthy transcriptomes were more similar — expression differences, not a settled etiology.
Open questions
Tensions and limits
Some items are genuine disagreements on the same question. Others mark different assays, populations, or outcomes — limits on how far one study travels — not a forced fight between papers.
Reference-guided counting and de novo assembly are not interchangeable products. CircRNA detection needed non-poly(A) libraries and back-splice calling; Aloe and sea bass papers assemble transcripts because a complete reference is missing or incomplete; LCL and tomato papers quantify against known genes. Treating every RNA-seq paper as a gene-count table hides those design choices.
- Cataloging mammalian circular RNAs
- Aloe vera medicinal pathway transcriptome
- Genetics shapes methylation and expression
Study Role Design N Population Outcome Cataloging mammalian circular RNAs Supports Computational / modellingComputational detection of back-splice junctions in non-poly(A) RNA-seq Multi-sample circRNA discovery pipeline — no single primary analytic N in stored summary Mammalian non-poly(A) RNA-seq libraries Expanded catalogue and splice features of circular RNAs Aloe vera medicinal pathway transcriptome Supports Computational / modellingIllumina RNA-seq de novo Trinity assembly of Aloe vera root and leaf transcriptomes Two tissues; ~43,443 root and ~43,178 leaf CDS — transcriptome resource, not a sample-N study Aloe vera root and leaf tissues Annotated transcriptome resources linked to secondary-metabolite pathways Genetics shapes methylation and expression Supports Cross-sectionalIllumina 27K promoter methylation in Yoruba HapMap LCLs linked to genotypes and RNA-seq N=77 · 77 LCLs; RNA-seq available for 69 HapMap Yoruba lymphoblastoid cell lines Genetic and expression correlates of inter-individual DNA methylation How far a transcriptome may be generalised is itself in dispute in these papers. Tomato red-light findings are not automatically all crops; mixed-yeast dilution rates are experimental; LCL methylation–expression links may not hold in primary tissues; coral DEGs do not settle field etiology.
- Does red light help tomatoes fight bacteria?
- What do co-fermenting yeasts express together?
- Genetics shapes methylation and expression
- Coral gene expression in white syndromes
Study Role Design N Population Outcome Does red light help tomatoes fight bacteria? Supports Animal / in-vitroDiurnal/wavelength treatments of tomato–Pto DC3000 with RNA-seq of red-light effects N=12 · 12 single-end RNA-seq samples from four treatments Tomato plants challenged with Pseudomonas syringae pv. tomato DC3000 Red-light modulation of disease resistance and related transcriptomes What do co-fermenting yeasts express together? Supports Animal / in-vitroContinuous co-culture of S. cerevisiae and L. thermotolerans under anaerobic vs aerobic conditions Two biological replicates per fermentation condition; species held near equal abundance ~24 h Mixed Saccharomyces cerevisiae and Lachancea thermotolerans cultures Condition-dependent transcriptome shares and mixing-responsive genes Genetics shapes methylation and expression Supports Cross-sectionalIllumina 27K promoter methylation in Yoruba HapMap LCLs linked to genotypes and RNA-seq N=77 · 77 LCLs; RNA-seq available for 69 HapMap Yoruba lymphoblastoid cell lines Genetic and expression correlates of inter-individual DNA methylation Coral gene expression in white syndromes Supports OtherRNA-seq of white-syndrome disease, adjacent, and healthy Acropora hyacinthus tissues N=16 · Fragments from 16 colonies; analyses with n=8 per tissue group Reef-building coral Acropora hyacinthus colonies in Palau Differentially expressed genes separating diseased from healthy coral tissue
Common misconceptions
RNA-seq measures all RNA in a cell equally.
Library chemistry filters the pool. Poly(A) selection misses many circular RNAs that a non-poly(A) pipeline was built to find, and mixed-species co-cultures only recover the scarcer partner when its transcript share is high enough (about 24% anaerobic versus 8% aerobic for L. thermotolerans).
If a pathway is annotated in the assembly, those enzymes have been shown to run that pathway in the organism.
Aloe vera CDS counts are homology annotations, not enzyme assays; ginseng MEP conclusions still need functional tests; sea bass immune labels are homology-based, not a vaccine trial.
Differentially expressed genes in diseased tissue identify the cause of the disease.
Coral white-syndrome RNA-seq shows hundreds of DEGs and that adjacent tissue looks more like healthy tissue; the authors do not treat that as proven causation, and field etiology remains debated.
Exam-style questions
Short-answer questions that ask you to explain or compare, not recall.
Why would a poly(A)-selected mRNA-seq experiment be the wrong design for the circular-RNA catalogue paper, and what did the authors do instead?
Many circRNAs lack a poly(A) tail and are detected via back-splice junctions. The authors used non-poly(A) RNA-seq and a computational pipeline aimed at those junctions, finding mostly canonical GT-AG splice signals and cell-type-dependent circular fractions.
A student claims RNA-seq showed that red light 'causes' tomato immunity. What did the experiment actually contrast, and what would be required to generalise?
It contrasted times of day and wavelengths in a tomato–DC3000 system: resistance peaked at 08:00, susceptibility at 20:00, and red light was the most disease-suppressive wavelength among those tested, tied to PAMP-triggered immunity timing. Generalising to other crops and pathogens needs further tests, which the paper does not provide.
How does the yeast co-culture paper show that 'we sequenced the mix' is not the same as 'we can analyse both species equally'?
Even with imposed equal biomass, L. thermotolerans transcript share was about 24% under anaerobic conditions and about 8% under aerobic conditions. Only the anaerobic depth was described as sufficient to recover mixing-responsive genes for the scarcer partner.
What is the difference between a de novo transcriptome (Aloe, sea bass) and a count table against a reference (LCLs), and why does that change what you can claim?
De novo assembly reconstructs CDS without a complete genome and then annotates by homology, so pathway labels are predictions. Reference quantification compares known genes across individuals or conditions, as in the LCL methylation–expression study. Neither design by itself chemically verifies metabolites or proves that a GWAS variant caused the expression change.
The studies
20 studies in this library bear on RNA Sequencing (RNA-seq), ordered by citations. The first 8 are shown.
- Cataloging mammalian circular RNAs
Thousands of mammalian circRNAs arise by back-splicing and can be quantified across ENCODE cell types.
- Plant gene duplicates after polyploidy cycles
Across plant genomes, WGD and other duplication modes leave distinct, lineage-biased duplicate landscapes.
- Genetics shapes methylation and expression
In HapMap LCLs, promoter methylation associates with genetic variants and transcript levels.
- What makes Apis cerana’s genome distinctive?
The Asian honey bee genome reveals expanded chemosensory receptors (Ors/Grs/Irs) central to chemical communication and colony life.
- What marks definitive endoderm progenitors in hESCs?
Single-cell RNA-seq separates definitive endoderm progenitors and links metabolism/hypoxia to DE differentiation.
- Selective bacterial BSH shifts host metabolism
Bacteroides bile salt hydrolase BT2086 selectively deconjugates bile acids and alters host metabolism.
- What extreme physiologies does the painted turtle genome encode?
The western painted turtle genome informs evolution of extreme anoxia and freeze tolerance in a slowly evolving vertebrate lineage.
- Coral gene expression in white syndromes
White-syndrome lesions in Acropora hyacinthus show distinct innate-immunity–related expression versus healthy tissue.
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- How do Sinocyclocheilus genomes record cave life?
Sinocyclocheilus cavefish genomes reveal troglomorphic adaptations such as eye degeneration and albinism, paralleling and differing from Astyanax.
- Immune transcriptome of bacteria-challenged sea bass
Deep RNA-seq of bacteria-challenged Lateolabrax japonicus reveals innate immune genes.
- Why do immune genes keep evolving across tetrapods?
Shared positive selection in birds and mammals concentrates on immune—especially antiviral—genes, implicating pathogens as a consistent selective pressure.
- Which genes does P. gingivalis need to grow?
A near-saturating Tn-seq screen finds ~463 (~22%) P. gingivalis genes putatively essential on blood agar.
- Histone acylations respond to plant stress
Rice histone butyrylation and crotonylation mark active chromatin and shift under starvation/submergence.
- Does red light help tomatoes fight bacteria?
Tomato resistance to DC3000 peaks at 8:00 AM, bottoms at 8:00 PM, and is strongly enhanced by red light.
- Pathogenicity factors in Naegleria fowleri
Whole-genome analysis of N. fowleri highlights candidate pathogenicity factors for brain infection.
- What can a wild Medicago genome teach about stress tolerance?
A highly complete Medicago ruthenica genome provides genetic resources for environmental-stress tolerance missing from yield-focused cultivated alfalfa.
- How does ZFP36 reshape metabolism after growth signals?
Growth factors induce ZFP36, which binds and decays metabolic enzyme/transporter mRNAs—especially Eno2—tuning glycolytic metabolism.
- Does the MEP pathway feed ginsenoside production?
Transcriptomics implicates the chloroplastic MEP pathway—highlighting IspD—in ginsenoside biosynthesis beyond the classic MVA route.
- What do co-fermenting yeasts express together?
In balanced continuous co-culture, L. thermotolerans contributes ~24% of anaerobic reads vs ~8% aerobically.
- Aloe vera medicinal pathway transcriptome
De novo root and leaf transcriptomes highlight saponin and anthraquinone metabolism genes.
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