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Atwan QS, Al-Ogaidi I. Enhancing the therapeutic potential of curcumin: a novel nanoformulation for targeted anticancer therapy to colorectal cancer with reduced miR20a and miR21 expression. Biomed Mater 2024; 19:025020. [PMID: 38215475 DOI: 10.1088/1748-605x/ad1dfc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/12/2024] [Indexed: 01/14/2024]
Abstract
Curcumin (Cur) possesses remarkable pharmacological properties, including cardioprotective, neuroprotective, antimicrobial, and anticancer activities. However, the utilization of Cur in pharmaceuticals faces constraints owing to its inadequate water solubility and limited bioavailability. To overcome these hurdles, there has been notable focus on exploring innovative formulations, with nanobiotechnology emerging as a promising avenue to enhance the therapeutic effectiveness of these complex compounds. We report a novel safe, effective method for improving the incorporation of anticancer curcumin to induce apoptosis by reducing the expression levels of miR20a and miR21. The established method features three aspects that, to our knowledge, have not been formally verified: (1) use of a novel formula to incorporate curcumin, (2) use of all biocompatible biodegradable materials to produce this formula without leaving harmful residues, and (3) an incorporation process at temperatures of approximately 50 °C. The formula was prepared from lecithin (LE), and chitosan (CH) with an eco-friendly emulsifying agent and olive oil as the curcumin solvent. The formula was converted to nanoscale through ultrasonication and probe sonication at a frequency of 20 kHz. Transmission electron microscopy showed that the nano formula was spherical in shape with sizes ranging between 49.7 nm in diameter and negative zeta potentials ranging from 28 to 34 mV. Primers miR20a and miR21 were designed for molecular studies. Nearly complete curcumin with an encapsulation efficiency of 91.1% was established using a straight-line equation. The nano formula incorporated with curcumin was used to prepare formulations that exhibited anticancer activities. The apoptosis pathway in cancer cells was activated by the minimum inhibitory concentration of the nano formula. These findings suggest the potential of this nanoformulation as an effective and selective cancer treatment that does not affect the normal cells.
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Affiliation(s)
- Qusay S Atwan
- Department of Biotechnology, College of Science, University of Baghdad, Baghdad, Iraq
| | - Israa Al-Ogaidi
- Department of Biotechnology, College of Science, University of Baghdad, Baghdad, Iraq
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Atwan QS, Al-Ogaidi I. Improving the targeted delivery of curcumin to esophageal cancer cells via a novel formulation of biodegradable lecithin/chitosan nanoparticles with downregulated miR-20a and miR-21 expression. NANOTECHNOLOGY 2024; 35:135103. [PMID: 38096580 DOI: 10.1088/1361-6528/ad15b9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024]
Abstract
Nanoencapsulation, employing safe materials, holds substantial promise for enhancing bioactive compounds' delivery, stability, and bioactivity. In this study, we present an innovative and safe methodology for augmenting the incorporation of the anticancer agent, curcumin, thereby inducing apoptosis by downregulating miR20a and miR21 expression. Our established methodology introduces three pivotal elements that, to our knowledge, have not undergone formal validation: (1) Novel formulation: We introduce a unique formula for curcumin incorporation. (2) Biocompatibility and biodegradability: our formulation exclusively consists of biocompatible and biodegradable constituents, ensuring the absence of detrimental residues or undesirable reactions under varying conditions. (3) Low-temperature incorporation: Curcumin is incorporated into the formulation at temperatures approximating 50 °C. The formulation comprises lecithin (LE), chitosan (CH), an eco-friendly emulsifying agent, and olive oil as the solvent for curcumin. Nanoscale conversion is achieved through ultrasonication and probe sonication (20 kHz). Transmission electron microscopy (TEM) reveals spherical nanoparticles with diameters ranging from 29.33 nm and negative zeta potentials within the -28 to -34 mV range. Molecular studies involve the design of primers for miR20a and miR21. Our findings showcase a remarkable encapsulation efficiency of 91.1% for curcumin, as determined through a linear equation. The curcumin-loaded nanoformulation demonstrates potent anticancer activity, effectively activating the apoptosis pathway in cancer cells at the minimum inhibitory concentration. These results underscore the potential of our nanoformulation as a compelling, cancer-selective treatment strategy, preserving the integrity of normal cells, and thus, warranting further exploration in the field of cancer therapy.
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Affiliation(s)
- Qusay S Atwan
- Department of Biotechnology, College of Science, University of Baghdad, Baghdad, Iraq
| | - Israa Al-Ogaidi
- Department of Biotechnology, College of Science, University of Baghdad, Baghdad, Iraq
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Yan C, Ding C, Duan G. PMMS: Predicting essential miRNAs based on multi-head self-attention mechanism and sequences. Front Med (Lausanne) 2022; 9:1015278. [DOI: 10.3389/fmed.2022.1015278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022] Open
Abstract
Increasing evidence has proved that miRNA plays a significant role in biological progress. In order to understand the etiology and mechanisms of various diseases, it is necessary to identify the essential miRNAs. However, it is time-consuming and expensive to identify essential miRNAs by using traditional biological experiments. It is critical to develop computational methods to predict potential essential miRNAs. In this study, we provided a new computational method (called PMMS) to identify essential miRNAs by using multi-head self-attention and sequences. First, PMMS computes the statistic and structure features and extracts the static feature by concatenating them. Second, PMMS extracts the deep learning original feature (BiLSTM-based feature) by using bi-directional long short-term memory (BiLSTM) and pre-miRNA sequences. In addition, we further obtained the multi-head self-attention feature (MS-based feature) based on BiLSTM-based feature and multi-head self-attention mechanism. By considering the importance of the subsequence of pre-miRNA to the static feature of miRNA, we obtained the deep learning final feature (WA-based feature) based on the weighted attention mechanism. Finally, we concatenated WA-based feature and static feature as an input to the multilayer perceptron) model to predict essential miRNAs. We conducted five-fold cross-validation to evaluate the prediction performance of PMMS. The areas under the ROC curves (AUC), the F1-score, and accuracy (ACC) are used as performance metrics. From the experimental results, PMMS obtained best prediction performances (AUC: 0.9556, F1-score: 0.9030, and ACC: 0.9097). It also outperformed other compared methods. The experimental results also illustrated that PMMS is an effective method to identify essential miRNA.
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Ivanova Z, Minkov G, Gisel A, Yahubyan G, Minkov I, Toneva V, Baev V. The Multiverse of Plant Small RNAs: How Can We Explore It?
. Int J Mol Sci 2022; 23:ijms23073979. [PMID: 35409340 PMCID: PMC8999349 DOI: 10.3390/ijms23073979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/28/2022] [Accepted: 03/31/2022] [Indexed: 12/22/2022] Open
Abstract
Plant small RNAs (sRNAs) are a heterogeneous group of noncoding RNAs with a length of 20–24 nucleotides that are widely studied due to their importance as major regulators in various biological processes. sRNAs are divided into two main classes—microRNAs (miRNAs) and small interfering RNAs (siRNAs)—which differ in their biogenesis and functional pathways. Their identification and enrichment with new structural variants would not be possible without the use of various high-throughput sequencing (NGS) techniques, allowing for the detection of the total population of sRNAs in plants. Classifying sRNAs and predicting their functional role based on such high-performance datasets is a nontrivial bioinformatics task, as plants can generate millions of sRNAs from a variety of biosynthetic pathways. Over the years, many computing tools have been developed to meet this challenge. Here, we review more than 35 tools developed specifically for plant sRNAs over the past few years and explore some of their basic algorithms for performing tasks related to predicting, identifying, categorizing, and quantifying individual sRNAs in plant samples, as well as visualizing the results of these analyzes. We believe that this review will be practical for biologists who want to analyze their plant sRNA datasets but are overwhelmed by the number of tools available, thus answering the basic question of how to choose the right one for a particular study.
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Affiliation(s)
- Zdravka Ivanova
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
| | - Georgi Minkov
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Andreas Gisel
- Institute of Biomedical Technologies (ITB), CNR, 70126 Bari, Italy;
| | - Galina Yahubyan
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Ivan Minkov
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
- Center of Plant System Biology and Biotechnology, 4000 Plovdiv, Bulgaria
| | - Valentina Toneva
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Vesselin Baev
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria;
- Correspondence:
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Kang Q, Meng J, Luan Y. RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction. Brief Bioinform 2022; 23:6555402. [PMID: 35352114 DOI: 10.1093/bib/bbac107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/22/2022] [Accepted: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.
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Affiliation(s)
- Qiang Kang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116024, China
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6
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Chao H, Hu Y, Zhao L, Xin S, Ni Q, Zhang P, Chen M. Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants. Int J Mol Sci 2022; 23:ijms23073695. [PMID: 35409060 PMCID: PMC8998614 DOI: 10.3390/ijms23073695] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 12/14/2022] Open
Abstract
Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.
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Affiliation(s)
| | | | | | | | | | - Peijing Zhang
- Correspondence: (P.Z.); (M.C.); Tel./Fax: +86-(0)571-88206612 (M.C.)
| | - Ming Chen
- Correspondence: (P.Z.); (M.C.); Tel./Fax: +86-(0)571-88206612 (M.C.)
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7
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Lokuge S, Jayasundara S, Ihalagedara P, Kahanda I, Herath D. miRNAFinder: A comprehensive web resource for plant Pre-microRNA classification. Biosystems 2022; 215-216:104662. [DOI: 10.1016/j.biosystems.2022.104662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 01/08/2022] [Accepted: 02/28/2022] [Indexed: 12/14/2022]
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8
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Zhao Y, Kuang Z, Wang Y, Li L, Yang X. MicroRNA annotation in plants: current status and challenges. Brief Bioinform 2021; 22:6180404. [PMID: 33754625 DOI: 10.1093/bib/bbab075] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/01/2021] [Accepted: 02/15/2021] [Indexed: 11/14/2022] Open
Abstract
Last two decades, the studies on microRNAs (miRNAs) and the numbers of annotated miRNAs in plants and animals have surged. Herein, we reviewed the current progress and challenges of miRNA annotation in plants. Via the comparison of plant and animal miRNAs, we pinpointed out the difficulties on plant miRNA annotation and proposed potential solutions. In terms of recalling the history of methods and criteria in plant miRNA annotation, we detailed how the major progresses made and evolved. By collecting and categorizing bioinformatics tools for plant miRNA annotation, we surveyed their advantages and disadvantages, especially for ones with the principle of mimicking the miRNA biogenesis pathway by parsing deeply sequenced small RNA (sRNA) libraries. In addition, we summarized all available databases hosting plant miRNAs, and posted the potential optimization solutions such as how to increase the signal-to-noise ratio (SNR) in these databases. Finally, we discussed the challenges and perspectives of plant miRNA annotations, and indicated the possibilities offered by an all-in-one tool and platform according to the integration of artificial intelligence.
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Affiliation(s)
- Yongxin Zhao
- Beijing Academy of Agriculture and Forestry Sciences, China
| | - Zheng Kuang
- Peking University and Beijing Academy of Agriculture and Forestry Sciences, China
| | | | - Lei Li
- School of Advanced Agricultural Sciences and School of Life Sciences at the Peking University, China
| | - Xiaozeng Yang
- Beijing Academy of Agriculture and Forestry Sciences, China
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9
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Ayachit G, Shaikh I, Pandya H, Das J. Salient Features, Data and Algorithms for MicroRNA Screening from Plants: A Review on the Gains and Pitfalls of Machine Learning Techniques. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200601121756] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The era of big data and high-throughput genomic technology has enabled scientists to
have a clear view of plant genomic profiles. However, it has also led to a massive need for
computational tools and strategies to interpret this data. In this scenario of huge data inflow,
machine learning (ML) approaches are emerging to be the most promising for analysing
heterogeneous and unstructured biological datasets. Extending its application to healthcare and
agriculture, ML approaches are being useful for microRNA (miRNA) screening as well.
Identification of miRNAs is a crucial step towards understanding post-transcriptional gene
regulation and miRNA-related pathology. The use of ML tools is becoming indispensable in
analysing such data and identifying species-specific, non-conserved miRNA. However, these
techniques have their own benefits and lacunas. In this review, we will discuss the current scenario
and pitfalls of ML-based tools for plant miRNA identification and provide some insights into the
important features, the need for deep learning models and direction in which studies are needed.
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Affiliation(s)
- Garima Ayachit
- Department of Botany, Bioinformatics and Climate Change, University School of Sciences, Gujarat University, Navrangpura, Ahmedabad – 380009, India
| | - Inayatullah Shaikh
- Gujarat State Biotechnology Mission, Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat 382011, India
| | - Himanshu Pandya
- Department of Botany, Bioinformatics and Climate Change, University School of Sciences, Gujarat University, Navrangpura, Ahmedabad – 380009, India
| | - Jayashankar Das
- Gujarat State Biotechnology Mission, Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat 382011, India
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10
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Vivek AT, Kumar S. Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq. Brief Bioinform 2020; 22:6041165. [PMID: 33333550 DOI: 10.1093/bib/bbaa322] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022] Open
Abstract
Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of ncRNAs using RNA-seq. We also provide a collection of relevant software packages and databases to present a comprehensive bioinformatics toolbox for plant ncRNA researchers. We assume that the discussions in this review will provide a rationale for the discovery of all major categories of plant ncRNAs.
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Affiliation(s)
- A T Vivek
- National Institute of Plant Genome Research in New Delhi, India
| | - Shailesh Kumar
- National Institute of Plant Genome Research in New Delhi
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11
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Tseng KC, Chiang-Hsieh YF, Pai H, Wu NY, Zheng HQ, Chow CN, Lee TY, Chang SB, Lin NS, Chang WC. sRIS: A Small RNA Illustration System for Plant Next-Generation Sequencing Data Analysis. PLANT & CELL PHYSIOLOGY 2020; 61:1204-1212. [PMID: 32181856 DOI: 10.1093/pcp/pcaa034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/08/2020] [Indexed: 06/10/2023]
Abstract
Small RNA (sRNA), such as microRNA (miRNA) and short interfering RNA, are well-known to control gene expression based on degradation of target mRNA in plants. A considerable amount of research has applied next-generation sequencing (NGS) to reveal the regulatory pathways of plant sRNAs. Consequently, numerous bioinformatics tools have been developed for the purpose of analyzing sRNA NGS data. However, most methods focus on the study of sRNA expression profiles or novel miRNAs predictions. The analysis of sRNA target genes is usually not integrated into their pipelines. As a result, there is still no means available for identifying the interaction mechanisms between host and virus or the synergistic effects between two viruses. For the present study, a comprehensive system, called the Small RNA Illustration System (sRIS), has been developed. This system contains two main components. The first is for sRNA overview analysis and can be used not only to identify miRNA but also to investigate virus-derived small interfering RNA. The second component is for sRNA target prediction, and it employs both bioinformatics calculations and degradome sequencing data to enhance the accuracy of target prediction. In addition, this system has been designed so that figures and tables for the outputs of each analysis can be easily retrieved and accessed, making it easier for users to quickly identify and quantify their results. sRIS is available at http://sris.itps.ncku.edu.tw/.
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Affiliation(s)
- Kuan-Chieh Tseng
- Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Yi-Fan Chiang-Hsieh
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Hsuan Pai
- Institute of Plant and Microbial Biology, Academia Sinica, Nankang, Taipei 115, Taiwan
| | - Nai-Yun Wu
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Han-Qin Zheng
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Chi-Nga Chow
- College of Biosciences and Biotechnology, NCKU-AS Graduate Program in Translational Agricultural Sciences, National Cheng Kung University, Tainan 70101, Taiwan
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
| | - Tzong-Yi Lee
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
| | - Song-Bin Chang
- Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Na-Sheng Lin
- Institute of Plant and Microbial Biology, Academia Sinica, Nankang, Taipei 115, Taiwan
| | - Wen-Chi Chang
- Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
- College of Biosciences and Biotechnology, NCKU-AS Graduate Program in Translational Agricultural Sciences, National Cheng Kung University, Tainan 70101, Taiwan
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12
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Yan C, Wu FX, Wang J, Duan G. PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences. BMC Bioinformatics 2020; 21:111. [PMID: 32183740 PMCID: PMC7079416 DOI: 10.1186/s12859-020-3426-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/21/2020] [Indexed: 11/16/2022] Open
Abstract
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.
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Affiliation(s)
- Cheng Yan
- Hunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China.,School of Computer and Information,Qiannan Normal University for Nationalities, Longshan Road, DuYun, 558000, China
| | - Fang-Xiang Wu
- Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China
| | - Guihua Duan
- Hunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China.
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13
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Raad J, Stegmayer G, Milone DH. Complexity measures of the mature miRNA for improving pre-miRNAs prediction. Bioinformatics 2019; 36:2319-2327. [DOI: 10.1093/bioinformatics/btz940] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 12/10/2019] [Accepted: 12/17/2019] [Indexed: 12/20/2022] Open
Abstract
AbstractMotivationThe discovery of microRNA (miRNA) in the last decade has certainly changed the understanding of gene regulation in the cell. Although a large number of algorithms with different features have been proposed, they still predict an impractical amount of false positives. Most of the proposed features are based on the structure of precursors of the miRNA only, not considering the important and relevant information contained in the mature miRNA. Such new kind of features could certainly improve the performance of the predictors of new miRNAs.ResultsThis paper presents three new features that are based on the sequence information contained in the mature miRNA. We will show how these new features, when used by a classical supervised machine learning approach as well as by more recent proposals based on deep learning, improve the prediction performance in a significant way. Moreover, several experimental conditions were defined and tested to evaluate the novel features impact in situations close to genome-wide analysis. The results show that the incorporation of new features based on the mature miRNA allows to improve the detection of new miRNAs independently of the classifier used.Availability and implementationhttps://sourceforge.net/projects/sourcesinc/files/cplxmirna/.Supplementary informationSupplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jonathan Raad
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
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14
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Manuweera B, Reynolds G, Kahanda I. Computational methods for the ab initio identification of novel microRNA in plants: a systematic review. PeerJ Comput Sci 2019; 5:e233. [PMID: 33816886 PMCID: PMC7924660 DOI: 10.7717/peerj-cs.233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 10/14/2019] [Indexed: 06/12/2023]
Abstract
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.
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Affiliation(s)
- Buwani Manuweera
- Gianforte School of Computing, Montana State University, Bozeman, MT, United States of America
| | - Gillian Reynolds
- Gianforte School of Computing, Montana State University, Bozeman, MT, United States of America
- Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, United States of America
| | - Indika Kahanda
- Gianforte School of Computing, Montana State University, Bozeman, MT, United States of America
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