Improving gene function predictions using independent transcriptional components

Carlos G. Urzúa-Traslaviña, Vincent C. Leeuwenburgh, Arkajyoti Bhattacharya, Stefan Loipfinger, Marcel A. T. M. Vugt, Elisabeth G. E. Vries and Rudolf S. N. Fehrmann ()
Additional contact information
Carlos G. Urzúa-Traslaviña: University of Groningen
Vincent C. Leeuwenburgh: University of Groningen
Arkajyoti Bhattacharya: University of Groningen
Stefan Loipfinger: University of Groningen
Marcel A. T. M. Vugt: University of Groningen
Elisabeth G. E. Vries: University of Groningen
Rudolf S. N. Fehrmann: University of Groningen

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract 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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-021-21671-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21671-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-21671-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().