Variant analysis of RNA sequences in severe equine asthma
Bibliographic record
Abstract
Background. Severe equine asthma is a chronic inflammatory disease of the lung in horses similar to low-Th2 late-onset asthma in humans. The disease in horses has complex inheritance including both dominant and recessive patterns that are ill defined. This study aimed to determine the utility of RNA-Seq to call gene variants and identify mutations potentially linked to disease. Methods. RNA-Seq data were generated from endobronchial biopsies collected from 6 asthmatic and 7 non-asthmatic horses before and after challenge (26 samples total). Sequences were aligned to the equine genome with Spliced Transcripts Alignment to Reference software. Read preparation for variant calling was performed with Picard tools and Genome Analysis Toolkit (GATK). Coverage was visualized using Integrative Genomic Viewer software and variants were called and filtered using GATK and Ensembl Variant Effect Predictor (VEP) tools. Novel variant selection by VEP was based on score of <0.01 predicted with Sorting Intolerant From Tolerant (SIFT) software, missense nature, location within the protein coding sequence and presence in all asthmatic individuals. For selected mutations, the effect of predicted variants on protein function was assessed with Polymorphism Phenotyping (PolyPhen) 2 and Screening for Non-Acceptable Polymorphism (SNAP) 2 softwares. RNA-Seq predicted variants were confirmed in all horses, and investigated in an additional 4 asthmatic and 7 non-asthmatic individuals with PCR and Sanger sequencing. Gene alignment and 3D protein structures were predicted with Geneious software. Results. Level of expression across the genome was similar in all individuals. RNA-Seq variant calling and filtering identified with highest confidence mutations in PACRG and RTTN . Sanger sequencing confirmed that the PACRG variant was appropriately identified in all 26 samples while the RTTN variant was identified correctly by RNA-Seq in 24 of 26 samples. SIFT and PolyPhen2 indicated both mutations would result in loss of function, and SNAP2 that they would be non-neutral. Amino acid substitutions projected no change of hydrophobicity and isoelectric point in PACRG, a change in both for RTTN; and a slight change in 3D structure for PACRG and RTTN. For PACRG, samples from additional individuals confirmed higher frequency of the heterozygous genotype in asthmatics, while the RTTN homozygous mutant phenotype was more prevalent in the asthmatic compared to non-asthmatic group. Discussion. RNA-Seq was sensitive and specific for calling gene variants in this disease model. Even moderate coverage (<10-20 cpm) yielded correct identification in 92% of samples, suggesting RNA-Seq may be suitable to detect variants in low coverage samples. The impact of amino acid alterations in PACRG and RTTN proteins are unknown at this point, but their role in structure and function of cilia may warrant further investigation.
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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".