Dataset Information

SplicerEX: a tool for the automated detection and classification of mRNA changes from conventional and splice-sensitive microarray expression data.

ABSTRACT: The key postulate that one gene encodes one protein has been overhauled with the discovery that one gene can generate multiple RNA transcripts through alternative mRNA processing. In this study, we describe SplicerEX, a novel and uniquely motivated algorithm designed for experimental biologists that (1) detects widespread changes in mRNA isoforms from both conventional and splice sensitive microarray data, (2) automatically categorizes mechanistic changes in mRNA processing, and (3) mitigates known technological artifacts of exon array-based detection of alternative splicing resulting from 5' and 3' signal attenuation, background detection limits, and saturation of probe set signal intensity. In this study, we used SplicerEX to compare conventional and exon-based Affymetrix microarray data in a model of EBV transformation of primary human B cells. We demonstrated superior detection of 3'-located changes in mRNA processing by the Affymetrix U133 GeneChip relative to the Human Exon Array. SplicerEX-identified exon-level changes in the EBV infection model were confirmed by RT-PCR and revealed a novel set of EBV-regulated mRNA isoform changes in caspases 6, 7, and 8. Finally, SplicerEX as compared with MiDAS analysis of publicly available microarray data provided more efficiently categorized mRNA isoform changes with a significantly higher proportion of hits supported by previously annotated alternative processing events. Therefore, SplicerEX provides an important tool for the biologist interested in studying changes in mRNA isoform usage from conventional or splice-sensitive microarray platforms, especially considering the expansive amount of archival microarray data generated over the past decade. SplicerEX is freely available upon request.

SUBMITTER: Robinson TJ

PROVIDER: S-EPMC3404365 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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SplicerEX: a tool for the automated detection and classification of mRNA changes from conventional and splice-sensitive microarray expression data.

Robinson Timothy J TJ Forte Eleonora E Salinas Raul E RE Puri Shaan S Marengo Matthew M Garcia-Blanco Mariano A MA Luftig Micah A MA

RNA (New York, N.Y.) 20120626 8

The key postulate that one gene encodes one protein has been overhauled with the discovery that one gene can generate multiple RNA transcripts through alternative mRNA processing. In this study, we describe SplicerEX, a novel and uniquely motivated algorithm designed for experimental biologists that (1) detects widespread changes in mRNA isoforms from both conventional and splice sensitive microarray data, (2) automatically categorizes mechanistic changes in mRNA processing, and (3) mitigates kn ...[more]

PMID: 22736799

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Project description:The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.

Dataset Information

SplicerEX: a tool for the automated detection and classification of mRNA changes from conventional and splice-sensitive microarray expression data.

Publications

SplicerEX: a tool for the automated detection and classification of mRNA changes from conventional and splice-sensitive microarray expression data.

Similar Datasets

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