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Special Issue "Small RNA Bioinformatics"

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A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 6350

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Special Issue Editors

Dr. Laurent Guyon

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Guest Editor

Biology and Biotechnologies for Health UMR_1292, Interdisciplinary Research Institute of Grenoble (IRIG), University Grenoble Alpes, INSERM, CEA, F-38000 Grenoble, France
Interests: microRNAs; small non-coding RNAs; bioinformatics; network analysis; survival data analysis; cancer; adrenocortical carcinoma

Dr. Matthias Zytnicki
E-Mail Website
Guest Editor

INRA, Toulouse, 31326 Castanet Tolosan, France
Interests: epigenetics; text algorithms for high-throughput data

Special Issue Information

Dear Colleagues,

The present Special Issue of Genes aims to gather research on the topic of small non-coding RNAs (sncRNAs) bioinformatics, including the analysis of large-scale data, novel methodology, and databases.

MicroRNAs (miRNAs) are near 22 nucleotide long non-coding RNAs which have been proven to be important regulators of many biological processes, both physiological and pathological. Discovered in 1993, miRNAs were recognized as a full class of molecules in 2000, and were proven to be shared by many eukaryote organisms. Since the early 2000s, bioinformatics has always accompanied all steps of miRNA research, including structure inference, profiling and target recognition. New-generation sequencing technologies have boosted the small non-coding RNA research and provide high-throughput datasets which strongly benefit from bioinformatics. miRNAs are part of a larger class of molecules called small non-coding RNAs (sncRNAs for short), which also includes (but is not limited to) piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs) and small interfering RNAs (siRNAs).

On the same topic, the small non-coding RNA bioinformatics club organizes free virtual seminars every two months. More information here: https://smallrna-bioinformatics.eu/.

Dr. Laurent Guyon
Dr. Matthias Zytnicki
Guest Editors

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Keywords

small non-coding RNAs (sncRNAs)
microRNAs (miRNAs)
bioinformatics
sncRNA datasets
microRNA targets
database
webserver

Published Papers (5 papers)

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Research

Open AccessArticle

MicroRNA Target Identification: Revisiting Accessibility and Seed Anchoring

Nicolas Homberg

Mariana Galvão Ferrarini

Christine Gaspin

and

Marie-France Sagot

Genes 2023, 14(3), 664; https://doi.org/10.3390/genes14030664 - 07 Mar 2023

Viewed by 604

Abstract

By pairing to messenger RNAs (mRNAs for short), microRNAs (miRNAs) regulate gene expression in animals and plants. Accurately identifying which mRNAs interact with a given miRNA and the precise location of the interaction sites is crucial to reaching a more complete view of the regulatory network of an organism. Only a few experimental approaches, however, allow the identification of both within a single experiment. Computational predictions of miRNA–mRNA interactions thus remain generally the first step used, despite their drawback of a high rate of false-positive predictions. The major computational approaches available rely on a diversity of features, among which anchoring the miRNA seed and measuring mRNA accessibility are the key ones, with the first being universally used, while the use of the second remains controversial. Revisiting the importance of each is the aim of this paper, which uses Cross-Linking, Ligation, And Sequencing of Hybrids (CLASH) datasets to achieve this goal. Contrary to what might be expected, the results are more ambiguous regarding the use of the seed match as a feature, while accessibility appears to be a feature worth considering, indicating that, at least under some conditions, it may favour anchoring by miRNAs. Full article

(This article belongs to the Special Issue Small RNA Bioinformatics)

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Open AccessArticle

Optimal microRNA Sequencing Depth to Predict Cancer Patient Survival with Random Forest and Cox Models

and

Genes 2022, 13(12), 2275; https://doi.org/10.3390/genes13122275 - 02 Dec 2022

Cited by 1 | Viewed by 754

Abstract

(1) Background: tumor profiling enables patient survival prediction. The two essential parameters to be calibrated when designing a study based on tumor profiles from a cohort are the sequencing depth of RNA-seq technology and the number of patients. This calibration is carried out under cost constraints, and a compromise has to be found. In the context of survival data, the goal of this work is to benchmark the impact of the number of patients and of the sequencing depth of miRNA-seq and mRNA-seq on the predictive capabilities for both the Cox model with elastic net penalty and random survival forest. (2) Results: we first show that the Cox model and random survival forest provide comparable prediction capabilities, with significant differences for some cancers. Second, we demonstrate that miRNA and/or mRNA data improve prediction over clinical data alone. mRNA-seq data leads to slightly better prediction than miRNA-seq, with the notable exception of lung adenocarcinoma for which the tumor miRNA profile shows higher predictive power. Third, we demonstrate that the sequencing depth of RNA-seq data can be reduced for most of the investigated cancers without degrading the prediction abilities, allowing the creation of independent validation sets at a lower cost. Finally, we show that the number of patients in the training dataset can be reduced for the Cox model and random survival forest, allowing the use of different models on different patient subgroups. Full article

(This article belongs to the Special Issue Small RNA Bioinformatics)

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Open AccessArticle

Further Mining and Characterization of miRNA Resource in Chinese Fir (Cunninghamia lanceolata)

and

Genes 2022, 13(11), 2137; https://doi.org/10.3390/genes13112137 - 17 Nov 2022

Cited by 1 | Viewed by 694

Abstract

In this study, we aimed to expand the current miRNA data bank of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) regarding its potential value for further genetic and genomic use in this species. High-throughput small RNA sequencing successfully captured 140 miRNAs from a Chinese fir selfing family harboring vigor and depressed progeny. Strikingly, 75.7% (n = 106) of these miRNAs have not been documented previously, and most (n = 105) of them belong to the novel set with 6858 putative target genes. The new datasets were then integrated with the previous information to gain insight into miRNA genetic architecture in Chinese fir. Collectively, a relatively high proportion (62%, n = 110) of novel miRNAs were found. Furthermore, we identified one MIR536 family that has not been previously documented in this species and four overlapped miRNA families (MIR159, MIR164, MIR171_1, and MIR396) from new datasets. Regarding the stability, we calculated the secondary structure free energy and found a relatively low R² value (R² < 0.22) between low minimal folding free energy (MFE) of pre-miRNAs and MFE of its corresponding mature miRNAs in most datasets. When in view of the conservation aspect, the phylogenetic trees showed that MIR536 and MIR159 sequences were highly conserved in gymnosperms. Full article

(This article belongs to the Special Issue Small RNA Bioinformatics)

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Open AccessArticle

Haemolysis Detection in MicroRNA-Seq from Clinical Plasma Samples

Melanie D. Smith

Shalem Y. Leemaqz

Tanja Jankovic-Karasoulos

and

Genes 2022, 13(7), 1288; https://doi.org/10.3390/genes13071288 - 21 Jul 2022

Cited by 2 | Viewed by 1181

Abstract

The abundance of cell-free microRNA (miRNA) has been measured in blood plasma and proposed as a source of novel, minimally invasive biomarkers for several diseases. Despite improvements in quantification methods, there is no consensus regarding how haemolysis affects plasma miRNA content. We propose a method for haemolysis detection in miRNA high-throughput sequencing (HTS) data from libraries prepared using human plasma. To establish a miRNA haemolysis signature we tested differential miRNA abundance between plasma samples with known haemolysis status. Using these miRNAs with statistically significant higher abundance in our haemolysed group, we further refined the set to reveal high-confidence haemolysis association. Given our specific context, i.e., women of reproductive age, we also tested for significant differences between pregnant and non-pregnant groups. We report a novel 20-miRNA signature used to identify the presence of haemolysis in silico in HTS miRNA-sequencing data. Further, we validated the signature set using firstly an all-male cohort (prostate cancer) and secondly a mixed male and female cohort (radiographic knee osteoarthritis). Conclusion: Given the potential for haemolysis contamination, we recommend that assays for haemolysis detection become standard pre-analytical practice and provide here a simple method for haemolysis detection. Full article

(This article belongs to the Special Issue Small RNA Bioinformatics)

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Open AccessArticle

MiRNA-Regulated Pathways for Hypertrophic Cardiomyopathy: Network-Based Approach to Insight into Pathogenesis

and

Genes 2021, 12(12), 2016; https://doi.org/10.3390/genes12122016 - 18 Dec 2021

Cited by 2 | Viewed by 2050

Abstract

Hypertrophic cardiomyopathy (HCM) is the most common hereditary heart disease. The wide spread of high-throughput sequencing casts doubt on its monogenic nature, suggesting the presence of mechanisms of HCM development independent from mutations in sarcomeric genes. From this point of view, HCM may arise from the interactions of several HCM-associated genes, and from disturbance of regulation of their expression. We developed a bioinformatic workflow to study the involvement of signaling pathways in HCM development through analyzing data on human heart-specific gene expression, miRNA-target gene interactions, and protein–protein interactions, available in open databases. Genes regulated by a pool of miRNAs contributing to human cardiac hypertrophy, namely hsa-miR-1-3p, hsa-miR-19b-3p, hsa-miR-21-5p, hsa-miR-29a-3p, hsa-miR-93-5p, hsa-miR-133a-3p, hsa-miR-155-5p, hsa-miR-199a-3p, hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-451a, and hsa-miR-497-5p, were considered. As a result, we pinpointed a module of TGFβ-mediated SMAD signaling pathways, enriched by targets of the selected miRNAs, that may contribute to the cardiac remodeling in HCM. We suggest that the developed network-based approach could be useful in providing a more accurate glimpse on pathological processes in the disease pathogenesis. Full article

(This article belongs to the Special Issue Small RNA Bioinformatics)

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