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Traceable Claims in Bioinformatics

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     Quick Analysis Plan


    Cryptic APA categories β†’ TDP-43 loss magnitude (event-count visualization)
    Using the extracted event counts from , you can immediately visualize the relative burden across APA subtypes and usage-threshold regimes."
    For TE-derived cryptic exonization (crypTEs), see the related long-read CrypTE-seq study for complementary evidence streams ()."



     Long Analysis Plan


    Visualize Data: Cryptic APA categories & TDP-43 loss event counts
    Goal: turn the reported cryptic APA subtype event counts into an auditable, reusable plotting + analysis workflow. We start with category-level event burden (what the paper explicitly reports), then outline how you’d extend to condition-level β€œTDP-43 loss vs control” counts once you have per-condition tables from the deposited resources.
    Data we can safely visualize from your provided research extracts
    From the Nature Neuroscience paper, the extracted event counts are: total cryptic APA events = 227, cryptic ALEs = 92, cryptic 3'UTR cryptics = 108, cryptic 3'UTR β€œ3'Ext” among cryptic 3'UTRs = 86, cryptic IPA events = 20, and cryptic-last-exon complexity/unassignable = 9. ()
    Figure 1 β€” Cryptic APA subtype burden (event counts)
    Figure 2 β€” Composition view (how much of cryptic APA is concentrated)
    Note: the categories are not guaranteed to be mutually exclusive at the β€œTotal” level (e.g., β€œCryptic 3'UTR cryptics” and β€œ3'Ext among cryptic 3'UTRs” are nested). This figure is therefore a visual concentration summary, not a strict partition. ()
    Figure 3 β€” Usage-threshold regimes (control baseline vs TDP-43 KD change)
    Two extracted regimes are: cryptic APAs consistently <10% usage in controls but >10% change after TDP-43 KD: 51 APAs; and cryptic APAs with significant increase after TDP-43 loss but baseline >10% in controls: 28 APAs. ()
    Bioinformatics / coding plan (what to implement next)
    1. Parse reported APA event tables (once you download the deposited minimal data / code). Target schema: (event_id, dataset, method, category{ALE|IPA|3UTR|3Ext|other}, control_usage, kd_usage, delta_usage, significance, annotation_confidence). The deposited analysis code and datasets for this cryptic APA study are indicated as available via ENA/GEO and Zenodo plus GitHub repos ().
    2. Standardize β€œcategory” mapping across methods (e.g., PAPA vs DaPars2 vs PEKA). The excerpt explicitly notes cross-method discrepancies and dependence on pipelines/annotation sets. ()
    3. Compute β€œevent burden” summaries by (i) category, (ii) method, (iii) dataset (i3Neurons vs postmortem ALS/FTD nuclei), and (iv) effect-size binning (e.g., baseline<10% β†’ >10% change vs baseline>10% β†’ significant). This mirrors the two extracted usage-threshold regimes ().
    4. Visualize with: (a) the subtype bar chart (already implemented above), (b) method-consensus agreement (Venn/UpSet) if you have per-event overlaps, and (c) volcano-like plots on delta usage (if per-event delta/SE is available). Keep Plotly figures at ≥400px height and label axes fully.
    5. Relate cryptic APA to disease relevance cautiously: the excerpt states that a substantial fraction of cryptic APA events are detectable in postmortem ALS/FTD tissues, supporting relevance. ()
      Confidence note: detectability in bulk/single-nucleus contexts does not by itself establish causality; you must check cellular composition and statistical controls.
    6. Optional orthogonal cross-check (crypTEs): if you want to test whether β€œcryptic isoform generation” generalizes from APA to TE-derived cryptic exons, you can integrate the CrypTE-seq outputs as a parallel evidence axis. ()
    7. Critical limitations / failure modes to encode as checks
      • Cryptic APA calling depends on sequencing depth, annotation sets, and pipelines; cross-method discrepancies exist. ()
      • Postmortem tissue heterogeneity and bulk RNA-seq detection noise can inflate/deflate apparent cryptic event counts. ()


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    Updated: April 24, 2026

     Top Data Sources ExportMCP


     Analysis Wizard



    It will parse deposited cryptic APA event tables, standardize category labels and method consensus, then generate Plotly subtype burden and condition-wise delta-usage regime plots for i3Neurons and ALS datasets.



     Hypothesis Graveyard


    β€œCryptic APA event totals are directly proportional to RNA-seq depth, so subtype composition is meaningless.” This is less compelling because the excerpt explicitly highlights pipeline/sequencing dependence but also reports disease-relevant detectability, implying some biological signal survives technical variation.


    β€œCryptic IPA/ALE/3'Ext are random noise artifacts with no structured enrichment across disease.” The provided counts and stated postmortem detectability argue against pure randomness, though quantification must be validated with deposited per-event statistics.

     Science Art


    Visualize Data: Cryptic APA categories TDP-43 loss counts Science Art

     Science Movie


    Make a narrated HD Science movie for this answer ($32 per minute)




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