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RNA Research and Splicing
Research Guide
What is RNA Research and Splicing?
RNA research and splicing is the study of how RNA molecules are produced, quantified, and processed—especially how precursor messenger RNA (pre-mRNA) is edited by removing introns and joining exons to generate mature transcripts and distinct isoforms.
The literature on RNA research and splicing spans experimental quantification (e.g., RT-qPCR and RNA-seq) and computational inference of transcript abundance and isoform usage from sequencing data. The provided corpus contains 104,696 works, indicating a large and methodologically diverse research area. Widely used analysis foundations include RT-qPCR normalization and comparative Ct quantification (Livak and Schmittgen (2001); Schmittgen and Livak (2008)) and RNA-seq differential expression frameworks (Anders and Huber (2010); Love et al. (2014); Ritchie et al. (2015)).
Research Sub-Topics
Alternative Splicing Regulation
This sub-topic examines the molecular mechanisms and regulatory factors controlling alternative splicing patterns in eukaryotic cells. Researchers study splicing factors, enhancers, silencers, and their roles in isoform diversity across development and disease.
RNA Splicing Machinery
This area focuses on the spliceosome complex, including snRNPs and protein components essential for intron removal. Researchers investigate spliceosome assembly, catalysis, and dynamics using structural biology and biochemistry.
Aberrant Splicing in Disease
Researchers analyze splicing mutations and dysregulation contributing to pathologies such as spinal muscular atrophy and frontotemporal dementia. Studies explore therapeutic splicing modulation using antisense oligonucleotides.
Computational Splicing Prediction
This sub-topic develops algorithms and machine learning models to predict splice sites, isoform structures, and splicing efficiency from genomic sequences. Researchers benchmark tools against RNA-seq data for accuracy.
Co-transcriptional Splicing
Studies investigate how RNA polymerase II transcription kinetics influences splice site choice and intron removal timing. Researchers use nascent RNA sequencing to map splicing progression.
Why It Matters
Splicing-aware RNA measurement and analysis directly supports disease biology, biomarker discovery, and therapeutic development by enabling reliable detection of transcript changes and isoform shifts. For example, RNA-seq can reveal isoform switching during differentiation, a core splicing-related phenotype that would be missed by gene-level summaries alone: "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation" (2010) explicitly reported unannotated transcripts and isoform switching, motivating splicing-centric experimental design and downstream validation. In applied biotechnology, the provided news items show substantial capital formation around splicing-based genetic medicines and RNA-therapeutics programs, including $135 million Series B financing for a protein-splicing genetic medicines company ("SpliceBio Secures $135 Million Series B Financing to ..." (2025)) and $25 million Series B financing for an RNA splicing therapeutics firm ("CFB Client Envisagenics Secures $25 Million Series B ..." (2025)). These translational efforts depend on robust quantification and normalization practices (e.g., "Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes" (2002)) and statistically principled RNA-seq modeling (e.g., "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" (2014)) to avoid false positives and to prioritize clinically actionable splicing events.
Reading Guide
Where to Start
Start with "Mapping and quantifying mammalian transcriptomes by RNA-Seq" (2008) because it frames how RNA-seq measurements are generated and interpreted, which is prerequisite knowledge for any splicing-aware transcriptomic analysis.
Key Papers Explained
A practical workflow can be read as a stack. "Mapping and quantifying mammalian transcriptomes by RNA-Seq" (2008) establishes RNA-seq-based transcriptome quantification; "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation" (2010) builds on this by assembling transcripts and emphasizing isoform switching as a biological signal. For statistical inference on RNA-seq counts, "Differential expression analysis for sequence count data" (2010) provides a foundational count model, while "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" (2014) extends this with moderated estimation of fold change and dispersion, and "limma powers differential expression analyses for RNA-sequencing and microarray studies" (2015) emphasizes integrated analysis and complex experimental designs. For orthogonal validation and targeted quantification, "Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method" (2001) and "Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes" (2002) specify widely used RT-qPCR quantification and normalization practices.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Current directions in the provided materials emphasize scaling splicing analysis and connecting it to therapeutics and genetic medicine development, as reflected by large financing rounds for splicing-focused companies ("SpliceBio Secures $135 Million Series B Financing to ..." (2025); "CFB Client Envisagenics Secures $25 Million Series B ..." (2025)). Methodologically, the core frontier implied by the top papers is improving isoform-resolution ("Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation" (2010)) while maintaining rigorous normalization and inference ("Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes" (2002); "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" (2014); "limma powers differential expression analyses for RNA-sequencing and microarray studies" (2015)).
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Analysis of Relative Gene Expression Data Using Real-Time Quan... | 2001 | Methods | 175.8K | ✕ |
| 2 | Moderated estimation of fold change and dispersion for RNA-seq... | 2014 | Genome biology | 93.4K | ✓ |
| 3 | limma powers differential expression analyses for RNA-sequenci... | 2015 | Nucleic Acids Research | 40.3K | ✓ |
| 4 | An introduction to probability theory and its applications | 1958 | Journal of the Frankli... | 29.7K | ✕ |
| 5 | Analyzing real-time PCR data by the comparative CT method | 2008 | Nature Protocols | 25.9K | ✕ |
| 6 | Accurate normalization of real-time quantitative RT-PCR data b... | 2002 | Genome biology | 19.7K | ✓ |
| 7 | Transcript assembly and quantification by RNA-Seq reveals unan... | 2010 | Nature Biotechnology | 16.1K | ✕ |
| 8 | Differential expression analysis for sequence count data | 2010 | Genome biology | 16.0K | ✓ |
| 9 | GSVA: gene set variation analysis for microarray and RNA-Seq data | 2013 | BMC Bioinformatics | 15.4K | ✓ |
| 10 | Mapping and quantifying mammalian transcriptomes by RNA-Seq | 2008 | Nature Methods | 14.0K | ✕ |
In the News
CFB Client Envisagenics Secures $25 Million Series B ...
Envisagenics , an AI-enabled biotechnology firm specializing in RNA splicing therapeutics, has announced the completion of its Series B funding round. This round included investments from existing ...
SpliceBio Secures $135 Million Series B Financing to ...
SpliceBio is a clinical-stage genetic medicines company pioneering Protein Splicing to address diseases caused by mutations in large genes. The Company’s lead program, SB-007, targets the root caus...
RAGE Biotech raises $29 million and appoints new ...
Australian biotech company RAGE Biotech has raised $29 million in Series A funding to accelerate its lead precision RNA therapy into clinical development, while announcing two senior leadership app...
Breakthrough in RNA Research Could Lead to Treatment ...
Their work was funded by the National Institutes of Health, the National Science Foundation, and the DSF Charitable Foundation. ## Work That Matters Researchers at CMU are working on real world sol...
UChicago researchers receive grant from Mathers Foundation ...
Two researchers from the University of Chicago recently received a $700,000 grant from the G. Harold and Leila Y. Mathers Foundation to study RNA splicing machinery in healthy and diseased cells. C...
Code & Tools
SpliceTransformer(SpTransformer) is a deep learning tool to predict tissue specific splicing site from pre-mRNA sequence ### License Apache-2.0 ...
**splicekit**is a modular platform for splicing analysis from short-read RNA-seq datasets. The platform also integrates an JBrowse2 instance, pybio...
## About rnasplice is a bioinformatics pipeline for RNA-seq alternative splicing analysis nf-co.re/rnasplice ### Topics
It can be used to study RNA splicing and other biological problems related to RNA sequence.
This package annotates genetic variants with their predicted effect on splicing, as described in Jaganathan*et al*, Cell 2019 in press . The annota...
Recent Preprints
RNA Splicing Errors Drive Leukemia - InventUM
In healthy cells, splicing trims and joins RNA segments to produce accurate instructions for making proteins. Think of it as cutting and pasting dialogueto makethe movie make sense. The SRSF2 gene ...
RNA splicing Archives
RNA therapeutics pioneer CSHL Professor Adrian Krainer has discovered a link between two important regulator proteins, SRSF1 and DDX23.Read the story » ] [ image of Katalin Karikó, Bruce Stillman...
Five questions on alternative splicing with Omar El Garwany
What is alternative splicing, and why does it matter? Omar El Garwany , Postdoctoral Fellow at the Wellcome Sanger Institute, is exploring how splicing differences can contribute to disease risk. T...
An RNA splicing system that excises DNA transposons from animal mRNAs
All genomes have mobile genetic segments called transposable elements (TEs) 1 . Here we describe a system, which we term SOS splicing, that protects*Caenorhabditis elegans*and human genes against D...
RNA splicing articles from across Nature Portfolio
RNA splicing is a modification of the nascent pre-messenger RNA (pre-mRNA) transcript in which introns are removed and exons are joined prior to translation. For many eukaryotic introns, splicing i...
Latest Developments
Recent developments in RNA and splicing research include the discovery of new regulatory layers influencing RNA splicing, such as proteins that affect splicing of about half of all human introns, allowing for more complex gene regulation (MIT, 2025), and mechanistic insights into the spliceosome's regulatory functions through studies on its intricate workings and alternative splicing networks (Remix Therapeutics, 2024; Nature, 2025). Additionally, advances in cryo-electron microscopy and sequencing technologies have deepened understanding of the dynamic structural rearrangements of the spliceosome during intron removal and exon ligation (Nature Reviews Genetics, 2025; Nature, 2025).
Sources
Frequently Asked Questions
What is the difference between measuring gene expression and measuring splicing?
Gene expression measurement summarizes how much RNA is produced from a gene, while splicing measurement focuses on how that RNA is partitioned into different transcript isoforms. "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation" (2010) demonstrates why isoform-level analysis matters by reporting unannotated transcripts and isoform switching that are not guaranteed to be captured by gene-level counts.
How is RT-qPCR expression typically quantified using Ct values?
"Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method" (2001) describes relative quantification using the 2−ΔΔCT approach, which compares Ct differences between target and reference and between conditions. "Analyzing real-time PCR data by the comparative CT method" (2008) provides a protocol framing of the same comparative Ct logic for practical analysis workflows.
How should RT-qPCR data be normalized to reduce technical bias?
"Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes" (2002) proposes using the geometric mean of multiple internal control genes rather than relying on a single reference. The paper states that this normalization strategy is a prerequisite for accurate RT-PCR expression profiling and supports studying small expression differences.
Which statistical methods are commonly used for RNA-seq differential expression, and why do they matter for splicing studies?
"Differential expression analysis for sequence count data" (2010) and "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" (2014) model count-based RNA-seq measurements to estimate fold changes with uncertainty control. "limma powers differential expression analyses for RNA-sequencing and microarray studies" (2015) describes an integrated R/Bioconductor solution for complex experimental designs, which is relevant when splicing comparisons must adjust for covariates, batches, or multi-factor studies.
How are transcriptomes mapped and quantified from RNA-seq reads in a way that supports isoform analysis?
"Mapping and quantifying mammalian transcriptomes by RNA-Seq" (2008) describes using RNA-seq to map and quantify transcriptomes, establishing a basis for transcript-level abundance estimation. "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation" (2010) extends this direction by assembling transcripts and quantifying isoforms, explicitly connecting RNA-seq quantification to isoform switching.
Which tools or approaches help interpret RNA-seq results beyond single-gene testing?
"GSVA: gene set variation analysis for microarray and RNA-Seq data" (2013) describes gene set variation analysis as a way to summarize expression profiles into pathway or signature-level scores. This is commonly used to contextualize RNA changes in biological programs, complementing gene- or transcript-level testing when prioritizing mechanisms related to splicing regulation or downstream functional consequences.
Open Research Questions
- ? How can transcript assembly and quantification approaches best distinguish true isoform switching from artifacts caused by incomplete annotation or ambiguous read assignment, as highlighted by the presence of unannotated transcripts in "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation" (2010)?
- ? Which normalization and modeling choices (e.g., multi-gene geometric normalization in "Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes" (2002) versus RNA-seq dispersion shrinkage in "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" (2014)) most strongly influence reproducibility when splicing-related effects are subtle?
- ? How should multi-factor experimental designs be specified and evaluated to separate splicing-associated biological variation from confounding technical factors, given the emphasis on complex designs in "limma powers differential expression analyses for RNA-sequencing and microarray studies" (2015)?
- ? What is the most reliable way to integrate isoform-resolved RNA-seq quantification with pathway-level summarization such as "GSVA: gene set variation analysis for microarray and RNA-Seq data" (2013) without obscuring isoform-specific functional signals?
- ? How can comparative Ct-based validation ("Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method" (2001); "Analyzing real-time PCR data by the comparative CT method" (2008)) be designed so that it confirms isoform-level findings rather than only gene-level changes?
Recent Trends
The provided dataset indicates scale rather than a 5-year growth estimate: 104,696 works are associated with RNA research and splicing, while Growth (5yr) is listed as N/A. Methodologically, highly cited RNA-seq inference frameworks—"Differential expression analysis for sequence count data" , "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" (2014), and "limma powers differential expression analyses for RNA-sequencing and microarray studies" (2015)—remain central, while isoform-centric interpretation is reinforced by "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation" (2010).
2010Translationally, the provided news items show continued investment in splicing-related therapeutics and genetic medicines, including $25 million and $135 million Series B rounds ("CFB Client Envisagenics Secures $25 Million Series B ..." ; "SpliceBio Secures $135 Million Series B Financing to ..." (2025)), suggesting sustained demand for reliable splicing-aware measurement and analysis pipelines.
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