Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.8
2.4
Journals
MPs
Special Issues
RNA-Seq: Data Analysis Methods and Applications
share announcement

RNA-Seq: Data Analysis Methods and Applications

A special issue of Methods and Protocols (ISSN 2409-9279).

Deadline for manuscript submissions: closed (25 October 2022) | Viewed by 18312

Special Issue Editors

Dr. Heleni Loutrari

E-Mail Website
Guest Editor
Evangelismos Hospital, 1st Department of Critical Care Medicine & Pulmonary Services, School of Medicine, National and Kapodistrian, University of Athens 3 Ploutarchou Street, 10675 Athens, Greece
Interests: angiogenesis; cancer biology; drug development; multi-omics methodologies; medical biotechnology
Mr. Panagiotis Agioutantis

E-Mail Website
Guest Editor
1. School of Chemical Engineering, National Technical University of Athens, Biotechnology Laboratory, Athens, Greece
2. Evangelismos Hospital, 1st Department of Critical Care Medicine & Pulmonary Services, School of Medicine, National and Kapodistrian, University of Athens, 3 Ploutarchou Street, 10675 Athens,Greece
Interests: computational biology; bioinformatics

Special Issue Information

Dear Colleagues,

Over the past decade, RNA-Seq has become the method of choice in a wide variety of life science applications because of the feasibility to combine in a specific high-throughput sequencing assay, both to identify transcripts and quantify their expression. Furthermore, as next generation sequencing technologies evolve, RNA-Seq offers more exciting opportunities involving, but not limited to, the ability to explore gene expression at a single cell resolution (single cell RNA-seq). Despite the extensive popularity of RNA-seq approaches, several challenges are associated with the followed workflow in data exploration from the initial experimental design, quality control, data normalization and correction, transcriptome assembly and read alignment/mapping to downstream statistical and bioinformatics data analysis involved in cell-level gene expression quantification, clustering and dimension reduction, differential transcript/gene expression, and functional analysis or integration of RNA-seq data with data from other multi-omics methodologies.

This Special Issue on "RNA-Seq: Data Analysis Methods and Applications" aims to attract original contributions that address these challenges in the context of different application purposes and provide the latest achievements, advancements, or improvements in the state-of-the-art-relevant to this active field of system biology. Literature reviews, as well as innovative studies and protocols, on bulk and single-cell RNA-Seq topics will be considered for publication.

We look forward to receiving your article.

Dr. Heleni Loutrari
Mr. Panagiotis Agioutantis
Guest Editors

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. Methods and Protocols is an international peer-reviewed open access semimonthly 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 1800 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

  • next-generation sequencing
  • bulk and single cell RNA-seq
  • transcriptome
  • metatranscriptome
  • data processing and analysis
  • statistical and bioinformatics tools
  • differential transcript/gene expression
  • functional analysis
  • integration of -omics data

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

20 pages, 5541 KiB  
Article
Interactive Analysis, Exploration, and Visualization of RNA-Seq Data with SeqCVIBE
by Efthimios Bothos, Pantelis Hatzis and Panagiotis Moulos
Methods Protoc. 2022, 5(2), 27; https://doi.org/10.3390/mps5020027 - 18 Mar 2022
Cited by 1 | Viewed by 3749
Abstract
The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields of current biological research. The large data sets and the continually expanding applications for which they can be mined, such as the [...] Read more.
The rise of modern gene expression profiling techniques, such as RNA-Seq, has generated a wealth of high-quality datasets spanning all fields of current biological research. The large data sets and the continually expanding applications for which they can be mined, such as the investigation of alternative splicing and others, have created novel challenges for data management, exploration, analysis, and visualization. Although a large variety of RNA-Seq data analysis software packages has emerged, both open-source and commercial, most fail to simultaneously address the above challenges, while they lack obvious functionalities, such as estimating RNA abundance over non-annotated genomic regions of interest in real time. We have developed SeqCVIBE, an R Shiny web application for the interactive exploration, analysis, visualization, and genome browsing of large RNA-Seq datasets. SeqCVIBE allows for multiple on-the-fly visualizations and calculations, such as differential expression analysis, averaging genomic signals over specific regions of the genome, and calculating RNA abundances over custom, potentially non-annotated regions, such as novel long non-coding RNAs. In addition, SeqCVIBE comprises a database for pre-analyzed data, where users can navigate and explore results, as well as perform a variety of basic on-the-fly analyses and export the outcomes. Finally, we demonstrate the value of SeqCVIBE in the elucidation of the interplay of a novel lincRNA, WiNTRLINC1, and Wnt signaling in colon cancer. Full article
(This article belongs to the Special Issue RNA-Seq: Data Analysis Methods and Applications)
►▼ Show Figures

Figure 1

25 pages, 744 KiB  
Article
Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition
by Christos Andrikos, Evangelos Makris, Angelos Kolaitis, Georgios Rassias, Christos Pavlatos and Panayiotis Tsanakas
Methods Protoc. 2022, 5(1), 14; https://doi.org/10.3390/mps5010014 - 2 Feb 2022
Cited by 4 | Viewed by 3785
Abstract
Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA [...] Read more.
Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA secondary structure prediction”. As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA’s pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern’s context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo. Full article
(This article belongs to the Special Issue RNA-Seq: Data Analysis Methods and Applications)
►▼ Show Figures

Figure 1

Other

Jump to: Research

13 pages, 10359 KiB  
Protocol
Combining Multiple RNA-Seq Data Analysis Algorithms Using Machine Learning Improves Differential Isoform Expression Analysis
by Alexandros C. Dimopoulos, Konstantinos Koukoutegos, Fotis E. Psomopoulos and Panagiotis Moulos
Methods Protoc. 2021, 4(4), 68; https://doi.org/10.3390/mps4040068 - 27 Sep 2021
Cited by 3 | Viewed by 3358
Abstract
RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex molecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. An advantage of [...] Read more.
RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex molecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. An advantage of RNA sequencing is its ability to capture complex transcriptomic events such as alternative splicing which results in alternate isoform abundance. At the same time, this advantage remains algorithmically and computationally challenging, especially with the emergence of even higher resolution technologies such as single-cell RNA sequencing. Although several algorithms have been proposed for the effective detection of differential isoform expression from RNA-Seq data, no widely accepted golden standards have been established. This fact is further compounded by the significant differences in the output of different algorithms when applied on the same data. In addition, many of the proposed algorithms remain scarce and poorly maintained. Driven by these challenges, we developed a novel integrative approach that effectively combines the most widely used algorithms for differential transcript and isoform analysis using state-of-the-art machine learning techniques. We demonstrate its usability by applying it on simulated data based on several organisms, and using several performance metrics; we conclude that our strategy outperforms the application of the individual algorithms. Finally, our approach is implemented as an R Shiny application, with the underlying data analysis pipelines also available as docker containers. Full article
(This article belongs to the Special Issue RNA-Seq: Data Analysis Methods and Applications)
►▼ Show Figures

Figure 1

18 pages, 6923 KiB  
Protocol
A Streamlined Approach to Pathway Analysis from RNA-Sequencing Data
by Austin Bow
Methods Protoc. 2021, 4(1), 21; https://doi.org/10.3390/mps4010021 - 17 Mar 2021
Cited by 1 | Viewed by 5225
Abstract
The reduction in costs associated with performing RNA-sequencing has driven an increase in the application of this analytical technique; however, restrictive factors associated with this tool have now shifted from budgetary constraints to time required for data processing. The sheer scale of the [...] Read more.
The reduction in costs associated with performing RNA-sequencing has driven an increase in the application of this analytical technique; however, restrictive factors associated with this tool have now shifted from budgetary constraints to time required for data processing. The sheer scale of the raw data produced can present a formidable challenge for researchers aiming to glean vital information about samples. Though many of the companies that perform RNA-sequencing provide a basic report for the submitted samples, this may not adequately capture particular pathways of interest for sample comparisons. To further assess these data, it can therefore be necessary to utilize various enrichment and mapping software platforms to highlight specific relations. With the wide array of these software platforms available, this can also present a daunting task. The methodology described herein aims to enable researchers new to handling RNA-sequencing data with a streamlined approach to pathway analysis. Additionally, the implemented software platforms are readily available and free to utilize, making this approach viable, even for restrictive budgets. The resulting tables and nodal networks will provide valuable insight into samples and can be used to generate high-quality graphics for publications and presentations. Full article
(This article belongs to the Special Issue RNA-Seq: Data Analysis Methods and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop