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  4. Showcase to illustrate how the web-server iRNAMethyl is working

Showcase to illustrate how the web-server iRNAMethyl is working


DOI: 10.31038/JMG.2020322

Short Commentary

In 2015 a very powerful web-server predictor has been established for identifying N6-methyladenosine (m6A), which is one of the most abundant modifications in RNA [1].

To see how the web-server is working, please do the following.

Step 1: Open the web server at http://lin.uestc.edu.cn/server/iRNA-Methyl and you will see the top page of the iRNA-Methyl predictor on your computer screen, as shown in Fig.1. Click on the Read Me button to see a brief introduction about the predictor and the caveat when using it.

JMG 202-302_Kuo-Chen Chou_F_1

Figure 1. A semi screenshot for the top page of iRNA-Methyl (Adapted from [1] with permission).

Step 2: Either type or copy/paste the query RNA sequences into the input box at the center of Fig.1. The input sequence should be in FASTA format.  For the examples of RNA sequences in FASTA format, click the Example button right above the input box.

Step 3: Click on the Submit button to see the predicted result. For example, if you use the query RNA sequences in the Example window as the input, you will see the following shown on the screen of your computer. (1) RNA sequence-1 contains 5 “GAC” (with adenine at its middle) consensus motifs, of which only those at the sequence positions 128 is predicted to be the methylation sites or  site, and all the others are not. (2) RNA sequence-2 contains 8 “GAC” consensus motifs, of which only those at the sequence positions 332 is predicted to be the methylation sites, while all the others are not. All these results are fully consistent with the experimental observations.

Step 4: Click on the Data button to download the datasets used to train and test the model.

Step 5: Click on the Citation button to find the relevant paper that document the detailed development and algorithm of iRNA-Methyl.

It is instructive to point out that the web-server predictor has been developed by strictly observing the guidelines of “Chou’s 5-steps rule” and hence have the following notable merits (see, e.g., [2–14] and three comprehensive review papers [15–17]: (1) crystal clear in logic development, (2) completely transparent in operation, (3) easily to repeat the reported results by other investigators, (4) with high potential in stimulating other sequence-analyzing methods, and (5) very convenient to be used by the majority of experimental scientists.

Moreover, it has not escaped our notice that during the development of iRNA-Methyl web-server, the approach of general pseudo amino acid components [18] or PseAAC [19] had been utilized and hence its accuracy would be much higher than its counterparts, as concurred by many investigators [15, 18–266].

It is anticipated that iRNA-Methyl may become a useful high throughput tool for conducting genome analysis as well as drug development.

For the remarkable and awesome roles of the “5-steps rule” in driving proteome, genome analyses and drug development, see a series of recent papers [16, 17, 267–276] where the rule and its wide applications have been very impressively presented from various aspects or at different angles.

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  258. Khan YD, Rasool N, Hussain W, Khan SA, Chou KC (2018) iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Analytical Biochemistry 550: 109–116. [Crossref]
  259. Khan YD, Rasool N, Hussain W, Khan SA, Chou KC (2018) iPhosY-PseAAC: Identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol Biol Rep 45: 2501–2509. [Crossref]
  260. Krishnan MS (2018) Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. J Theor Biol 445: 62–74. [Crossref]
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  262. Mei J, Zhao J (2018) Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers. Sci Rep 8: 2359.
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  264. Mousavizadegan M, Mohabatkar H (2018) Computational prediction of antifungal peptides via Chou’s PseAAC and SVM. J Bioinform Comput Biol. [Crossref]
  265. Rahman SM, Shatabda S, Saha S, Kaykobad M, Sohel Rahman M (2018) DPP-PseAAC: A DNA-binding Protein Prediction model using Chou’s general PseAAC. J Theor Biol 452: 22–34. [Crossref]
  266. Sankari ES, Manimegalai DD (2018) Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC. J Theor Biol 455: 319–328.
  267. Chou KC (2019) Two kinds of metrics for computational biology. Genomics.
  268. Chou KC (2019) Proposing pseudo amino acid components is an important milestone for proteome and genome analyses. International Journal for Peptide Research and Therapeutics .
  269. Chou K.C (2019) An insightful recollection for predicting protein subcellular locations in multi-label systems. Genomics.
  270. Chou KC (2019) Progresses in predicting post-translational modification. International Journal of Peptide Research and Therapeutics .
  271. Chou KC (2019) Recent Progresses in Predicting Protein Subcellular Localization with Artificial Intelligence (AI) Tools Developed Via the 5-Steps Rule. Japanese Journal of Gastroenterology and Hepatology 2: 1–4.
  272. Chou KC (2019) An insightful recollection since the distorted key theory was born about 23 years ago. Genomics.
  273. Chou KC (2019) Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications. Trends in Artificial Inttelengence 3: 60–74.
  274. Chou KC (2020) Distorted Key Theory and Its Implication for Drug Development. Current Genomics.
  275. Chou KC (2019) An insightful recollection since the birth of Gordon Life Science Institute about 17 years ago. Advancement in Scientific and Engineering Research 4: 31–36.
  276. Chou KC (2019) Gordon Life Science Institute: Its philosophy, achievements, and perspective. Annals of Cancer Therapy and Pharmacology 2: 1–26.

Article Type

Short Communication

Publication history

Received: January 29, 2020
Accepted: February 10, 2019
Published: February 20, 2020

Citation

Kuo-Chen Chou, Chen W and Feng P (2020) Showcase to illustrate how the web-server iRNA-Methyl is working. J Mol Genet, Volume 3(1): 1–7. DOI: 10.31038/JMG.2020322

Corresponding author

Dr. Kuo-Chen Chou,
Gordon Life Science Institute,
Boston, Massachusetts 02478,
United States of America;
Email: kcchou@gordonlifescience.org

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