= Solution
\b[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 $3'$ 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 fraction
$$
\psi=\frac{I}{I+S},
$$
where $I$ 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.<Deciphering the splicing code by Yoseph Barash and others>[Abstract and Results: combinations of RNA features predicting tissue-dependent exon inclusion]{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.<An RNA map predicting Nova-dependent splicing regulation by Jernej Ule and others>[Abstract; Figures 1–4 and 6: positional RNA map and effects on spliceosome assembly]{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. \b[The resulting code is a tested, context-dependent set of regulatory rules, with an explicit domain of validity.]
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