microRPM: a microRNA prediction model based only on plant small RNA sequencing data

Tseng, Kuan Chieh; Chiang-Hsieh, Yi Fan; Pai, Hsuan; Chow, Chi Nga; Lee, Shu‐Chuan; Zheng, Han; Kuo, Po Li; Li, Guan Zhen; Hung, Yu Cheng; Lin, Na; Chang, Wen Chi

doi:10.1093/bioinformatics/btx725

Cited by 23 publications

(15 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meng et al (2014) used both pre-miRNA and mature miRNA as the inputs to develop an integrated model for both miRNA and pre-miRNA prediction. Methods such as (Tseng et al, 2018;Douglass et al, 2016;Breakfield et al, 2012) used small-RNA sequencing data for their models. These methods still output the predicted miRNAs.…”

Section: Resultsmentioning

confidence: 99%

“…Of the 20 studies evaluated in this systematic review, only four (Tseng et al, 2018;Breakfield et al, 2012;Douglass et al, 2016;Sunkar et al, 2008) experimentally validated the presence of the novel miRNAs predicted by their machine learning methods. The most popular method was stem-loop PCR, employed by Tseng et al (2018), Breakfield et al (2012) and Douglass et al, 2016). Tseng et al (2018) additionally utilized qPCR and (Sunkar et al, 2008) employed Northern blot analysis and small RNA blots.…”

Section: Resultsmentioning

confidence: 99%

“…The most popular method was stem-loop PCR, employed by Tseng et al (2018), Breakfield et al (2012) and Douglass et al, 2016). Tseng et al (2018) additionally utilized qPCR and (Sunkar et al, 2008) employed Northern blot analysis and small RNA blots. (Tseng et al, 2018) confirmed 18 out of 21 predicted miRNAs to be real miRNAs while (Sunkar et al, 2008) has tested and confirmed seven out of 13 predicted miRNAs.…”

Section: Resultsmentioning

confidence: 99%

“…Tseng et al (2018) additionally utilized qPCR and (Sunkar et al, 2008) employed Northern blot analysis and small RNA blots. (Tseng et al, 2018) confirmed 18 out of 21 predicted miRNAs to be real miRNAs while (Sunkar et al, 2008) has tested and confirmed seven out of 13 predicted miRNAs. Breakfield et al (2012) and Douglass et al (2016) experimentally validated 8 of their predictions each to be true miRNAs.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Computational methods for the ab initio identification of novel microRNA in plants: a systematic review

Manuweera

Reynolds

Kahanda

2019

PeerJ Computer Science

View full text Add to dashboard Cite

Background MicroRNAs (miRNAs) play a vital role as post-transcriptional regulators in gene expression. Experimental determination of miRNA sequence and structure is both expensive and time consuming. The next-generation sequencing revolution, which facilitated the rapid accumulation of biological data has brought biology into the “big data” domain. As such, developing computational methods to predict miRNAs has become an active area of inter-disciplinary research. Objective The objective of this systematic review is to focus on the developments of ab initio plant miRNA identification methods over the last decade. Data sources Five databases were searched for relevant articles, according to a well-defined review protocol. Study selection The search results were further filtered using the selection criteria that only included studies on novel plant miRNA identification using machine learning. Data extraction Relevant data from each study were extracted in order to carry out an analysis on their methodologies and findings. Results Results depict that in the last decade, there were 20 articles published on novel miRNA identification methods in plants of which only 11 of them were primarily focused on plant microRNA identification. Our findings suggest a need for more stringent plant-focused miRNA identification studies. Conclusion Overall, the study accuracies are of a satisfactory level, although they may generate a considerable number of false negatives. In future, attention must be paid to the biological plausibility of computationally identified miRNAs to prevent further propagation of biologically questionable miRNA sequences.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Computational methods for the ab initio identification of novel microRNA in plants: a systematic review

Manuweera

Reynolds

Kahanda

2019

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…We use the module RNAlib of Vienna RNA Package to intrinsic folding quantitative measures P(S), nP(S), Q(s), nQ(s), D(s) and nD(s) [62]. These structure features and Vienna RNA Package have been broadly used in both miRNA prediction and pre-miRNA prediction [63][64][65]. As a result, our method consists of 38 features.…”

Section: Feature Setmentioning

confidence: 99%

PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences

Cheng

Wang

et al. 2020

BMC Bioinformatics

View full text Add to dashboard Cite

Background: MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result: In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion: The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.

show abstract

Progress Toward Deep Sequencing-Based Discovery of Stress-Related MicroRNA in Plants and Available Bioinformatics Tools

et al. 2018

View full text Add to dashboard Cite

microRPM: a microRNA prediction model based only on plant small RNA sequencing data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 23 publications

References 32 publications

Computational methods for the ab initio identification of novel microRNA in plants: a systematic review

Computational methods for the ab initio identification of novel microRNA in plants: a systematic review

PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences

Progress Toward Deep Sequencing-Based Discovery of Stress-Related MicroRNA in Plants and Available Bioinformatics Tools

Contact Info

Product

Resources

About