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Improving gene function predictions using independent transcriptional components

The interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis an...

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Detalles Bibliográficos
Autores principales: Urzúa-Traslaviña, Carlos G., Leeuwenburgh, Vincent C., Bhattacharya, Arkajyoti, Loipfinger, Stefan, van Vugt, Marcel A. T. M., de Vries, Elisabeth G. E., Fehrmann, Rudolf S. N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935959/
https://www.ncbi.nlm.nih.gov/pubmed/33674610
http://dx.doi.org/10.1038/s41467-021-21671-w
Descripción
Sumario:The interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.