Special Issue "RNA Bioinformatics: Tools, Resources, and Databases for RNA Research"

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 October 2022 | Viewed by 713

Special Issue Editor

Prof. Dr. Andrey Mironov
E-Mail Website
Guest Editor
Department of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
Interests: RNA-DNA interactions; bioinformatics; noncoding RNA; nuclear proteome; RNA sequencing

Special Issue Information

Dear Colleagues,

RNA research has become a more popular direction in molecular biology. Recently, many new RNA biotypes and functions have been discovered, and new experimental protocols for RNA research have been developed. The analysis of new data and prediction of the function of different RNAs require bioinformatic support. This issue aims to collect bioinformatic tools and resources for RNA analysis.

This Special Issue aims to publish articles in the following areas: tools for predicting RNA structures, including those with pseudoknots, as well as approaches based on comparative and evolutionary analysis; tools and resources for analyzing experimental data on RNA structure; search and analysis of RNA motives; analysis of 3D structures of 3D RNA motifs; methods for the analysis of RNA interactions. It is also expected to publish articles on RNA databases, particularly databases on 3D motives of RNA structures and RNA interactomes. Review articles and articles devoted to the comparative analysis and benchmarking of RNA analysis tools are of particular interest.

Prof. Dr. Andrey Mironov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • RNA structure prediction
  • RNA 3D structure analysis
  • RNA folding
  • RNA motifs prediction and search
  • RNA function analysis
  • RNA interactions
  • machine learning in RNA analysis

Published Papers (1 paper)

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Research

Article
CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction
Biomolecules 2022, 12(3), 409; https://doi.org/10.3390/biom12030409 - 07 Mar 2022
Viewed by 518
Abstract
As the third generation gene editing technology, Crispr/Cas9 has a wide range of applications. The success of Crispr depends on the editing of the target gene via a functional complex of sgRNA and Cas9 proteins. Therefore, highly specific and high on-target cleavage efficiency [...] Read more.
As the third generation gene editing technology, Crispr/Cas9 has a wide range of applications. The success of Crispr depends on the editing of the target gene via a functional complex of sgRNA and Cas9 proteins. Therefore, highly specific and high on-target cleavage efficiency sgRNA can make this process more accurate and efficient. Although there are already many sophisticated machine learning or deep learning models to predict the on-target cleavage efficiency of sgRNA, prediction accuracy remains to be improved. XGBoost is good at classification as the ensemble model could overcome the deficiency of a single classifier to classify, and we would like to improve the prediction efficiency for sgRNA on-target activity by introducing XGBoost into the model. We present a novel machine learning framework which combines a convolutional neural network (CNN) and XGBoost to predict sgRNA on-target knockout efficacy. Our framework, called CNN-XG, is mainly composed of two parts: a feature extractor CNN is used to automatically extract features from sequences and predictor XGBoost is applied to predict features extracted after convolution. Experiments on commonly used datasets show that CNN-XG performed significantly better than other existing frameworks in the predicted classification mode. Full article
(This article belongs to the Special Issue RNA Bioinformatics: Tools, Resources, and Databases for RNA Research)
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