Long-range ribonucleic acid (RNA)–RNA interactions (RRI) are prevalent in positive-strand RNA viruses, including Beta-coronaviruses, and these take part in regulatory roles, including the regulation of sub-genomic RNA production rates. Crosslinking of interacting RNAs and short read-based deep sequencing of resulting RNA–RNA hybrids have shown that these long-range structures exist in severe acute respiratory syndrome coronavirus (SARS-CoV)-2 on both genomic and sub-genomic levels and in dynamic topologies. Furthermore, co-evolution of coronaviruses with their hosts is navigated by genetic variations made possible by its large genome, high recombination frequency and a high mutation rate. SARS-CoV-2’s mutations are known to occur spontaneously during replication, and thousands of aggregate mutations have been reported since the emergence of the virus. Although many long-range RRIs have been experimentally identified using high-throughput methods for the wild-type SARS-CoV-2 strain, evolutionary trajectory of these RRIs across variants, impact of mutations on RRIs and interaction of SARS-CoV-2 RNAs with the host have been largely open questions in the field. In this review, we summarize recent computational tools and experimental methods that have been enabling the mapping of RRIs in viral genomes, with a specific focus on SARS-CoV-2. We also present available informatics resources to navigate the RRI maps and shed light on the impact of mutations on the RRI space in viral genomes. Investigating the evolution of long-range RNA interactions and that of virus–host interactions can contribute to the understanding of new and emerging variants as well as aid in developing improved RNA therapeutics critical for combating future outbreaks.
Rescuing low frequency variants within intra-host viral populations directly from Oxford Nanopore sequencing data
Abstract Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost. Tracking low frequency intra-host variants provides important insights with respect to elucidating within-host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable computational solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluate Variabel on both synthetic data (SARS-CoV-2) and patient derived datasets (Ebola virus, norovirus, SARS-CoV-2); our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel .
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- Award ID(s):
- 2126387
- NSF-PAR ID:
- 10378505
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Summary Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together with different and sometimes ad hoc, analytical procedures for generating genome sequences. A fully integrated analytical process for raw sequence to consensus genome determination, suited to outbreaks such as the ongoing COVID-19 pandemic, is critical to provide a solid genomic basis for epidemiological analyses and well-informed decision making. We have developed a web-based platform and integrated bioinformatic workflows that help to provide consistent high-quality analysis of SARS-CoV-2 sequencing data generated with either the Illumina or Oxford Nanopore Technologies (ONT). Using an intuitive web-based interface, this workflow automates data quality control, SARS-CoV-2 reference-based genome variant and consensus calling, lineage determination and provides the ability to submit the consensus sequence and necessary metadata to GenBank, GISAID and INSDC raw data repositories. We tested workflow usability using real world data and validated the accuracy of variant and lineage analysis using several test datasets, and further performed detailed comparisons with results from the COVID-19 Galaxy Project workflow. Our analyses indicate that EC-19 workflows generate high-quality SARS-CoV-2 genomes. Finally, we share a perspective on patterns and impact observed with Illumina versus ONT technologies on workflow congruence and differences.
Availability and implementation https://edge-covid19.edgebioinformatics.org, and https://github.com/LANL-Bioinformatics/EDGE/tree/SARS-CoV2.
Supplementary information Supplementary data are available at Bioinformatics online.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41467-022-28852-1
