PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction

Fan, Dashuai; Yao, Yuangen; Yi, Ming

doi:10.3390/genes12081280

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Plant miRNA detection has also been demonstrated, achieving 97.54% identification accuracy [ 47 ]. Human mirtrons and canonical miRNAs have been classified using convolutional neural networks (CNN) and long short-term memory networks (LSTMN) with 94.3% accuracy and 92.5% F1 score [ 48 ].…”

Section: Related Workmentioning

confidence: 99%

Robust and efficient COVID-19 detection techniques: A machine learning approach

et al. 2022

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The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus.

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Section: Related Workmentioning

confidence: 99%

Robust and efficient COVID-19 detection techniques: A machine learning approach

et al. 2022

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“…In the field of plant microRNA prediction, several computational tools have been developed to aid researchers in identifying and characterizing these small regulatory molecules. Tools such as miRNAFinder [ 32 ], PlantMirP2 [ 33 ], mirMachine [ 34 ], PmiRDiscVali [ 35 ], miRDeep-P2 [ 36 ], SUmir [ 37 ], and miRNA Digger [ 38 ] have been instrumental in advancing our understanding of plant microRNAs. miRNAFinder utilizes machine learning algorithms to predict novel microRNAs from small RNA sequencing data.…”

Section: Introductionmentioning

confidence: 99%

“…iii ) Currently, most plant miRNA prediction pipelines ( e . g ., miRNAFinder [ 32 ], PlantMirP2 [ 33 ], mirMachine [ 34 ], PmiRDiscVali [ 35 ], miRDeep-P2 [ 36 ], SUmir [ 37 ], miRNA Digger [ 38 ], etc .) require high-throughput sequencing data (see S1 Table ).…”

Section: Introductionmentioning

confidence: 99%

AmiR-P3: An AI-based microRNA prediction pipeline in plants

Ataei,

Ahmadi,

Marashi

et al. 2024

PLoS ONE

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Background MicroRNAs (miRNAs) are small noncoding RNAs that play important post-transcriptional regulatory roles in animals and plants. Despite the importance of plant miRNAs, the inherent complexity of miRNA biogenesis in plants hampers the application of standard miRNA prediction tools, which are often optimized for animal sequences. Therefore, computational approaches to predict putative miRNAs (merely) from genomic sequences, regardless of their expression levels or tissue specificity, are of great interest. Results Here, we present AmiR-P3, a novel ab initio plant miRNA prediction pipeline that leverages the strengths of various utilities for its key computational steps. Users can readily adjust the prediction criteria based on the state-of-the-art biological knowledge of plant miRNA properties. The pipeline starts with finding the potential homologs of the known plant miRNAs in the input sequence(s) and ensures that they do not overlap with protein-coding regions. Then, by computing the secondary structure of the presumed RNA sequence based on the minimum free energy, a deep learning classification model is employed to predict potential pre-miRNA structures. Finally, a set of criteria is used to select the most likely miRNAs from the set of predicted miRNAs. We show that our method yields acceptable predictions in a variety of plant species. Conclusion AmiR-P3 does not (necessarily) require sequencing reads and/or assembled reference genomes, enabling it to identify conserved and novel putative miRNAs from any genomic or transcriptomic sequence. Therefore, AmiR-P3 is suitable for miRNA prediction even in less-studied plants, as it does not require any prior knowledge of the miRNA repertoire of the organism. AmiR-P3 is provided as a docker container, which is a portable and self-contained software package that can be readily installed and run on any platform and is freely available for non-commercial use from: https://hub.docker.com/r/micrornaproject/amir-p3

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