Novel perspectives for SARS-CoV-2 genome browsing

Abstract SARS-CoV-2 has spread worldwide and caused social, economic, and health turmoil. The first genome assembly of SARS-CoV-2 was produced in Wuhan, and it is widely used as a reference. Subsequently, more than a hundred additional SARS-CoV-2 genomes have been sequenced. While the genomes appear to be mostly identical, there are variations. Therefore, an alignment of all available genomes and the derived consensus sequence could be used as a reference, better serving the science community. Variations are significant, but representing them in a genome browser can become, especially if their sequences are largely identical. Here we summarize the variation in one track. Other information not currently found in genome browsers for SARS-CoV-2, such as predicted miRNAs and predicted TRS as well as secondary structure information, were also added as tracks to the consensus genome. We believe that a genome browser based on the consensus sequence is better suited when considering worldwide effects and can become a valuable resource in the combating of COVID-19. The genome browser is available at http://cov.iaba.online.

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riboCIRC: a comprehensive database of translatable circRNAs

Genome Biology ◽

10.1186/s13059-021-02300-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Huihui Li ◽

Mingzhe Xie ◽

Yan Wang ◽

Ludong Yang ◽

Zhi Xie ◽

...

Keyword(s):

De Novo ◽

Species Conservation ◽

Structure And Function ◽

Research Community ◽

Genome Browser ◽

Valuable Resource ◽

Sequence Structure ◽

A Genome ◽

Context Specific ◽

And Function

AbstractriboCIRC is a translatome data-oriented circRNA database specifically designed for hosting, exploring, analyzing, and visualizing translatable circRNAs from multi-species. The database provides a comprehensive repository of computationally predicted ribosome-associated circRNAs; a manually curated collection of experimentally verified translated circRNAs; an evaluation of cross-species conservation of translatable circRNAs; a systematic de novo annotation of putative circRNA-encoded peptides, including sequence, structure, and function; and a genome browser to visualize the context-specific occupant footprints of circRNAs. It represents a valuable resource for the circRNA research community and is publicly available at http://www.ribocirc.com.

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riboCIRC: a comprehensive database of translatable circRNAs

10.1101/2021.03.03.433665 ◽

2021 ◽

Author(s):

Huihui Li ◽

Mingzhe Xie ◽

Yan Wang ◽

Ludong Yang ◽

Zhi Xie ◽

...

Keyword(s):

De Novo ◽

Species Conservation ◽

Structure And Function ◽

Research Community ◽

Genome Browser ◽

Valuable Resource ◽

Sequence Structure ◽

A Genome ◽

Context Specific ◽

And Function

riboCIRC is a translatome data-oriented circRNA database specifically designed for hosting, exploring, analyzing, and visualizing translatable circRNAs from multi-species. The database provides a comprehensive repository of computationally predicted ribosome-associated circRNAs, a manually curated collection of experimentally verified translated circRNAs, an evaluation of cross-species conservation of translatable circRNAs, a systematic de novo annotation of putative circRNA-encoded peptides, including sequence, structure, and function, and a genome browser to visualize the context-specific occupant footprints of circRNAs. It represents a valuable resource for the circRNA research community and is publicly available at http://www.ribocirc.com.

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Deep learning predicts short non-coding RNA functions from only raw sequence data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008415 ◽

2020 ◽

Vol 16 (11) ◽

pp. e1008415

Author(s):

Teresa Maria Rosaria Noviello ◽

Francesco Ceccarelli ◽

Michele Ceccarelli ◽

Luigi Cerulo

Keyword(s):

Secondary Structure ◽

Sequence Data ◽

Computational Cost ◽

Large Data ◽

Sequence Information ◽

Structure Information ◽

Non Coding Rna ◽

A Genome ◽

Data Volume ◽

Biological Functionality

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at: https://github.com/bioinformatics-sannio/ncrna-deep.

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On the computation of the tertiary structure of globular proteins—IV. Use of secondary structure information

Bulletin of Mathematical Biology ◽

10.1007/bf02459922 ◽

1985 ◽

Vol 47 (3) ◽

pp. 367-407

Author(s):

Peter Cariani ◽

Narendra S. Goel

Keyword(s):

Secondary Structure ◽

Tertiary Structure ◽

Globular Proteins ◽

Secondary Structure Information ◽

Structure Information

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RNA Secondary Structure as a First Step for Rational Design of the Oligonucleotides towards Inhibition of Influenza A Virus Replication

Pathogens ◽

10.3390/pathogens9110925 ◽

2020 ◽

Vol 9 (11) ◽

pp. 925 ◽

Cited By ~ 1

Author(s):

Marta Szabat ◽

Dagny Lorent ◽

Tomasz Czapik ◽

Maria Tomaszewska ◽

Elzbieta Kierzek ◽

...

Keyword(s):

Secondary Structure ◽

Influenza A Virus ◽

Influenza A ◽

Rational Design ◽

Rna Virus ◽

Viral Rna ◽

Structure Information ◽

A Genome ◽

Function Relationship ◽

Interfering Rna

Influenza is an important research subject around the world because of its threat to humanity. Influenza A virus (IAV) causes seasonal epidemics and sporadic, but dangerous pandemics. A rapid antigen changes and recombination of the viral RNA genome contribute to the reduced effectiveness of vaccination and anti-influenza drugs. Hence, there is a necessity to develop new antiviral drugs and strategies to limit the influenza spread. IAV is a single-stranded negative sense RNA virus with a genome (viral RNA—vRNA) consisting of eight segments. Segments within influenza virion are assembled into viral ribonucleoprotein (vRNP) complexes that are independent transcription-replication units. Each step in the influenza life cycle is regulated by the RNA and is dependent on its interplay and dynamics. Therefore, viral RNA can be a proper target to design novel therapeutics. Here, we briefly described examples of anti-influenza strategies based on the antisense oligonucleotide (ASO), small interfering RNA (siRNA), microRNA (miRNA) and catalytic nucleic acids. In particular we focused on the vRNA structure-function relationship as well as presented the advantages of using secondary structure information in predicting therapeutic targets and the potential future of this field.

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