A Bioinformatics-Based Alternative mRNA Splicing Code that May Explain Some Disease Mutations Is Conserved in Animals
Bibliographic record
Abstract
Deep sequencing of cDNAs made from spliced mRNAs indicates that most coding genes in many animals and plants have pre-mRNA transcripts that are alternatively spliced. In pre-mRNAs, in addition to invariant exons that are present in almost all mature mRNA products, there are at least 6 additional types of exons, such as exons from alternative promoters or with alternative polyA sites, mutually exclusive exons, skipped exons, or exons with alternative 5' or 3' splice sites. Our bioinformatics-based hypothesis is that, in analogy to the genetic code, there is an "alternative-splicing code" in introns and flanking exon sequences, analogous to the genetic code, that directs alternative splicing of many of the 36 types of introns. In humans, we identified 42 different consensus sequences that are each present in at least 100 human introns. 37 of the 42 top consensus sequences are significantly enriched or depleted in at least one of the 36 types of introns. We further supported our hypothesis by showing that 96 out of 96 analyzed human disease mutations that affect RNA splicing, and change alternative splicing from one class to another, can be partially explained by a mutation altering a consensus sequence from one type of intron to that of another type of intron. Some of the alternative splicing consensus sequences, and presumably their small-RNA or protein targets, are evolutionarily conserved from 50 plant to animal species. We also noticed the set of introns within a gene usually share the same splicing codes, thus arguing that one sub-type of splicesosome might process all (or most) of the introns in a given gene. Our work sheds new light on a possible mechanism for generating the tremendous diversity in protein structure by alternative splicing of pre-mRNAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".