How bioinformatics resources work with G4 RNAs

Miskiewicz, Joanna; Sarzyñska, Joanna; Szachniuk, Marta

doi:10.1093/bib/bbaa201

Cited by 13 publications

(7 citation statements)

References 80 publications

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“…The |score| filters were set to 20 and 40 to allow us the study of both the medium (PQS score ≥ 20; PiMS score ≤ -20) and the high probability candidates (PQS score ≥ 40; PiMS score ≤ -40; Fig 1B ) within the results. These score filters are important because they qualify the sequences and grant specificity to the results of GiG’s extremely flexible search engine (which was designed solely for sensitivity), as highlighted by the results of a recent review [ 60 ].…”

Section: Methodsmentioning

confidence: 99%

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Potential G-quadruplexes and i-Motifs in the SARS-CoV-2

et al. 2021

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Quadruplex structures have been identified in a plethora of organisms where they play important functions in the regulation of molecular processes, and hence have been proposed as therapeutic targets for many diseases. In this paper we report the extensive bioinformatic analysis of the SARS-CoV-2 genome and related viruses using an upgraded version of the open-source algorithm G4-iM Grinder. This version improves the functionality of the software, including an easy way to determine the potential biological features affected by the candidates found. The quadruplex definitions of the algorithm were optimized for SARS-CoV-2. Using a lax quadruplex definition ruleset, which accepts amongst other parameters two residue G- and C-tracks, 512 potential quadruplex candidates were discovered. These sequences were evaluated by their in vitro formation probability, their position in the viral RNA, their uniqueness and their conservation rates (calculated in over seventeen thousand different COVID-19 clinical cases and sequenced at different times and locations during the ongoing pandemic). These results were then compared subsequently to other Coronaviridae members, other Group IV (+)ssRNA viruses and the entire viral realm. Sequences found in common with other viral species were further analyzed and characterized. Sequences with high scores unique to the SARS-CoV-2 were studied to investigate the variations amongst similar species. Quadruplex formation of the best candidates were then confirmed experimentally. Using NMR and CD spectroscopy, we found several highly stable RNA quadruplexes that may be suitable therapeutic targets for the SARS-CoV-2.

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Section: Methodsmentioning

confidence: 99%

“…flexible search engine (which was designed solely for sensitivity), as highlighted by the results of a recent review [60].…”

Section: Plos Onementioning

confidence: 99%

Potential G-quadruplexes and i-Motifs in the SARS-CoV-2

et al. 2021

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“…Following the Human Genome project [16], computational algorithms were developed to predict the location of G4 sequence motifs in the human genome [17,18]. First algorithms consisted in finding all occurrences of the canonical motif G 3+ N 1−7 G 3+ N 1−7 G 3+ N 1−7 G 3+ , or the corresponding C-rich motif (quadparser algorithm) [19,20].…”

Section: Introductionmentioning

confidence: 99%

DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions

et al. 2021

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DNA is a complex molecule carrying the instructions an organism needs to develop, live and reproduce. In 1953, Watson and Crick discovered that DNA is composed of two chains forming a double-helix. Later on, other structures of DNA were discovered and shown to play important roles in the cell, in particular G-quadruplex (G4). Following genome sequencing, several bioinformatic algorithms were developed to map G4s in vitro based on a canonical sequence motif, G-richness and G-skewness or alternatively sequence features including k-mers, and more recently machine/deep learning. Recently, new sequencing techniques were developed to map G4s in vitro (G4-seq) and G4s in vivo (G4 ChIP-seq) at few hundred base resolution. Here, we propose a novel convolutional neural network (DeepG4) to map cell-type specific active G4 regions (e.g. regions within which G4s form both in vitro and in vivo). DeepG4 is very accurate to predict active G4 regions in different cell types. Moreover, DeepG4 identifies key DNA motifs that are predictive of G4 region activity. We found that such motifs do not follow a very flexible sequence pattern as current algorithms seek for. Instead, active G4 regions are determined by numerous specific motifs. Moreover, among those motifs, we identified known transcription factors (TFs) which could play important roles in G4 activity by contributing either directly to G4 structures themselves or indirectly by participating in G4 formation in the vicinity. In addition, we used DeepG4 to predict active G4 regions in a large number of tissues and cancers, thereby providing a comprehensive resource for researchers. Availability: https://github.com/morphos30/DeepG4.

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“…Meanwhile, the latter is subjected to various studies, aiming - among others - to associate the motif’s conformation with its function, find the relationship between its sequence and the higher-level structure, cluster and classify quadruplexes, learn about and fully describe their properties. We already know that tetrads can form from guanine as well as non-guanine nucleotides ( 15 ). Their spatial arrangement to their stacking neighbours is diverse, defined by the rise and twist parameters.…”

Section: Introductionmentioning

confidence: 99%

ONQUADRO: a database of experimentally determined quadruplex structures

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Kraszewska

Miskiewicz

et al. 2021

Nucleic Acids Research

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ONQUADRO is an advanced database system that supports the study of the structures of canonical and non-canonical quadruplexes. It combines a relational database that collects comprehensive information on tetrads, quadruplexes, and G4-helices; programs to compute structure parameters and visualise the data; scripts for statistical analysis; automatic updates and newsletter modules; and a web application that provides a user interface. The database is a self-updating resource, with new information arriving once a week. The preliminary data are downloaded from the Protein Data Bank, processed, annotated, and completed. As of August 2021, ONQUADRO contains 1,661 tetrads, 518 quadruplexes, and 30 G4-helices found in 467 experimentally determined 3D structures of nucleic acids. Users can view and download their description: sequence, secondary structure (dot-bracket, classical diagram, arc diagram), tertiary structure (ball-and-stick, surface or vdw-ball model, layer diagram), planarity, twist, rise, chi angle (value and type), loop characteristics, strand directionality, metal ions, ONZ, and Webba da Silva classification (the latter by loop topology and tetrad combination), origin structure ID, assembly ID, experimental method, and molecule type. The database is freely available at https://onquadro.cs.put.poznan.pl/. It can be used on both desktop computers and mobile devices.

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How bioinformatics resources work with G4 RNAs

Cited by 13 publications

References 80 publications

Potential G-quadruplexes and i-Motifs in the SARS-CoV-2

Potential G-quadruplexes and i-Motifs in the SARS-CoV-2

DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions

ONQUADRO: a database of experimentally determined quadruplex structures

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