Variant analysis of RNA sequences in severe equine asthma
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».