Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 April 2020.
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#5,467,074
of 28,233,490 outputs
Outputs from Frontiers in Microbiology
#4,943
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Outputs of similar age
#98,606
of 459,734 outputs
Outputs of similar age from Frontiers in Microbiology
#160
of 526 outputs
Altmetric has tracked 28,233,490 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 31,914 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 459,734 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 526 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
