ArticlePeerJ2018
Sequence variant analysis of RNA sequences in severe equine asthma.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Article
- RNA-seq evaluation of equine alveolar macrophages and monocyte-derived macrophages exposed to an inflammatory stimulus (short communication).BMC veterinary research · 2026Article
- Article
- APOBEC1-Dependent RNA Eiting of TNF Signaling Orchestrates Ileal Villus Morphogenesis in Pigs: Integrative Transcriptomic and Editomic Insights.Animals : an open access journal from MDPI · 2025Article
- Article
- Insights into a Novel and Efficient Microbial Nest System for Treating Pig Farm Wastewater.Microorganisms · 2025Article
- Single-cell transcriptomics delineates the immune cell landscape in equine lower airways and reveals upregulation of FKBP5 in horses with asthma.Scientific reports · 2023Article
- Detection of potential functional variants based on systems-biology: the case of feed efficiency in beef cattle.BMC genomics · 2022Article
- Black soldier fly larvae vermicompost alters soil biochemistry and bacterial community composition.Applied microbiology and biotechnology · 2022Article
- The Immune Mechanisms of Severe Equine Asthma-Current Understanding and What Is Missing.Animals : an open access journal from MDPI · 2022Review
- Flow cytometric analysis of equine bronchoalveolar lavage fluid cells in horses with and without severe equine asthma.Veterinary pathology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSevere equine asthma is a chronic inflammatory disease of the lung in horses similar to low-Th2 late-onset asthma in humans. This study aimed to determine the utility of RNA-Seq to call gene sequence variants, and to identify sequence variants of potential relevance to the pathogenesis of asthma.
methodsRNA-Seq data were generated from endobronchial biopsies collected from six asthmatic and seven 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 sequence variant calling was performed with Picard tools and Genome Analysis Toolkit (GATK). Sequence variants were called and filtered using GATK and Ensembl Variant Effect Predictor (VEP) tools, and two RNA-Seq predicted sequence variants were investigated with both PCR and Sanger sequencing. Supplementary analysis of novel sequence variant selection with VEP was based on a score of <0.01 predicted with Sorting Intolerant from Tolerant software, missense nature, location within the protein coding sequence and presence in all asthmatic individuals. For select variants, effect on protein function was assessed with Polymorphism Phenotyping 2 and screening for non-acceptable polymorphism 2 software. Sequences were aligned and 3D protein structures predicted with Geneious software. Difference in allele frequency between the groups was assessed using a Pearson's Chi-squared test with Yates' continuity correction, and difference in genotype frequency was calculated using the Fisher's exact test for count data.
resultsRNA-Seq variant calling and filtering correctly identified substitution variants in DISCUSSION: RNA-Seq was sensitive and specific for calling gene sequence variants in this disease model. Even moderate coverage (<10-20 counts per million) yielded correct identification in 92% of samples, suggesting RNA-Seq may be suitable to detect sequence variants in low coverage samples. The impact of amino acid alterations in PACRG and RTTN proteins, and possible association of the sequence variants with asthma, is of uncertain significance, but their role in ciliary function may be of future interest.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.