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A sequence element acting on expression or processing of the molecule or locus carrying it. A binding motif in an intron can regulate nearby alternative splicing, with its effect depending on position and the available RNA-binding proteins.

A member of a neural RNA-binding protein family with position-dependent effects on alternative splicing. Binding-site position helps predict activation or repression of exon inclusion.^[ref]

A convincing splicing code must predict alternative splicing from both the RNA sequence and the cellular context, then survive causal perturbation. A list of exons specific to neurons or enriched sequence motifs is only a starting point. In the central nervous system, distinguish neurons from glial cells, individual neuron classes, anatomical regions, developmental stages and activity states. Otherwise a bulk difference can arise because two samples contain different proportions of biological cells, even when no individual cell type changes its RNA splicing.

Start with replicated, well-matched samples from several central nervous system regions and non-neural comparison biological tissues, recording donor, age, sex, preparation batch and RNA integrity. Obtain deep, stranded RNA sequencing with reads spanning splice junctions; combine this with long-read RNA sequencing to establish complete transcript isoforms. Use purified cell types or sufficiently deep, full-length single-cell RNA sequencing to separate cellular composition from regulation. Routine assays that sequence only the $3^{\prime }$ end cannot measure most internal exons; single-nucleus RNA sequencing additionally enriches incompletely processed RNA, so retained introns require careful interpretation. A matched bulk or purified-cell dataset supplies coverage that sparse individual-cell observations lack.

Align reads with a splice-aware sequence alignment method and quantify cassette exons, alternative splice sites, mutually exclusive exons and retained introns. Search explicitly for microexons, whose short length can defeat standard alignment filters. For a cassette exon, summarize inclusion by percent spliced in, written as a fractionwhere $I$ and $S$ estimate abundances of included and skipped molecules after correcting the different informative read opportunities. Adding the two inclusion-junction counts and comparing their sum directly with a single skipping-junction count would bias this estimate. Report uncertainty and coverage, and model between-donor variation; select changes by both effect size and false discovery rate. Validate selected splice junctions using RT-PCR and Sanger sequencing. Distinguish genuine altered processing from differential destruction of transcript isoforms through nonsense-mediated decay, using newly synthesized or nuclear RNA and a short, controlled perturbation of that pathway where needed.

Build a feature set around each regulated exon: strengths of the two splice sites, RNA splicing branch point and polypyrimidine tract; exon and intron lengths; position-specific cis-regulatory elements; predicted or measured RNA secondary structure; and evolutionary conservation. Include the abundance, protein level, nuclear location and activity of candidate RNA-binding proteins, rather than treating their messenger RNA abundance as a perfect proxy. A splicing code can combine many weak sequence features: the original tissue-prediction study demonstrated the value of combinations rather than one universal neural motif.^[ref]

Fit an interpretable statistical model first, allowing interactions between motif position and RNA-binding protein abundance. Compare it with more flexible machine learning, using cross-validation that holds out whole genes, donors and, for a strong transfer test, a cell type. Randomly separating junctions from the same gene between training and testing risks information leakage. Compare against sequence-only, context-only and expression-matched baseline predictors. Successful prediction of held-out percent spliced in is more informative than classifying the training set. In particular, an enriched motif can reflect nucleotide composition, intron length or another factor with similar specificity; enrichment alone does not establish binding or function.

Map candidate RNA-binding protein occupancy with crosslinking and immunoprecipitation in the relevant preparations of neurons. Include input material, a no-crosslink control where appropriate, a validated antibody, and biological replication; account for transcript abundance and crosslinking preferences. Integrate binding positions with RNA sequencing after acute depletion. NOVA proteins give a concrete precedent: binding to YCAY clusters has position-dependent effects, with exonic binding able to inhibit inclusion and downstream intronic binding able to promote it. Here Y denotes a pyrimidine nucleobase. Thus the same motif can support opposite predictions at different positions.^[ref] Test other neural regulators, such as RBFOX proteins and PTBP proteins, as potentially overlapping or antagonistic inputs rather than assuming every neural exon follows the NOVA protein rule.

Finally, make the predictions experimentally vulnerable. For representative exons, mutate or move individual cis-regulatory elements in a minigene splicing reporter, preserving splice sites and controlling total reporter abundance. Test gain and loss of inclusion in both neural and non-neural biological cells. Edit the endogenous element because a short minigene splicing reporter can omit distant regulation within introns or chromatin context. Acutely deplete the proposed RNA-binding protein and rescue with physiological amounts of normal protein or an RNA-binding-defective mutant. Combined depletion tests redundancy; motif mutation plus factor depletion tests whether both act through the same element. A particularly strong result is the predicted reversal after moving a binding site from a repressive to an activating position. Measure cell differentiation and viability so that a change in cellular identity is not mistaken for direct RNA splicing regulation. The resulting code is a tested, context-dependent set of regulatory rules, with an explicit domain of validity.

The selfish-invasion hypothesis concerns the origin and spread of introns, whereas current function concerns the consequences of their presence today. A sequence can begin by improving its own transmission, later become dependent on host machinery, and subsequently acquire a host-beneficial function. Conversely, present-day persistence need not mean that every intron was retained because it helps the organism.

Group II introns provide a mechanistic model for mobile, self-splicing ancestors of spliceosomal introns. Their structural and catalytic similarities to the spliceosome support a historical relationship, although the detailed route and timing of eukaryotic intron origins remain reconstructed hypotheses. In ordinary spliceosomal introns, many activities needed for RNA processing are supplied in trans by a shared cellular machinery rather than by a complete mobile apparatus encoded in each intron. This dependence limits autonomy but does not demonstrate that all selfish proliferation has ended.

Purifying selection constrains the burden. Insertions that disrupt coding information, essential regulation or reliable splicing can reduce host fitness. Intron loss can remove transcriptional, processing and mutational costs, and selection preserves splice signals needed to produce functional RNA. The balance depends on effective population size, mutation and loss rates, gene expression and the effect of a particular insertion. Weakly deleterious introns can persist when drift overwhelms selection; a large genome-wide intron count is therefore insufficient evidence for either universal adaptation or unrestricted selfish spread.

The host has also harnessed intronic sequence and the processing step. Alternative splicing can connect one gene to several regulated transcripts, while exon shuffling can rearrange protein modules. Introns can contain enhancers, microRNAs and small nucleolar RNAs; splicing can be coupled to RNA export and surveillance. Some alternative events regulate transcript abundance through nonsense-mediated decay rather than producing a stable alternative protein. These examples establish possible functions, not that every alternative isoform or every intronic base has been selected for such a function.

Specific experiments demonstrate functions that an origin story alone cannot predict. Julie Parenteau and colleagues found that deleting introns from budding-yeast genes often impaired performance during nutrient depletion and connected intron presence with control of growth-related expression. Jeffrey T. Morgan and colleagues identified excised introns that become stable linear RNAs under particular growth-inhibitory conditions and contribute to growth regulation. These are evidence for regulated intron-derived activity in defined conditions, not a license to regard all excised introns as functional.^[ref][ref]

To distinguish function from a correlated consequence, an intron deletion must be assessed for effects on expression, local DNA regulation and splicing, with rescue or appropriate sequence controls. Comparative conservation, turnover and experimentally measured fitness costs help determine whether host-level selection maintains a given feature. The extent of containment is thus substantial but incomplete and lineage dependent: shared processing and host selection constrain the elements, while retained introns span functional, tolerated and potentially deleterious cases.

A member of a family of sequence-selective RNA-binding proteins involved in regulating alternative splicing.

A predictive set of rules connecting RNA sequence features and cellular regulators to alternative splicing outcomes. A useful code predicts the extent of exon inclusion in a specified cell type, rather than merely listing enriched motifs.