Short Communication

In 2016 a very powerful AI (artificial intelligence) tool has been established for identifying RNA pseudouridine sites, which is one of the important post modifications in RNA [1]. To see how the webserver is working, please do the following.

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

Step 2. Select the organism or species by checking on the corresponding open circle. Either type or copy/paste the query RNA sequences into the input box at the center of Figure 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 using the three query RNA sequences from the H. sapiens species in the Example window as the input and checking on the H. sapiens button, after clicking the Submit button, you will see the following shown on the screen of your computer.

a. The 1st query sequence includes 5 U (uridine) residues, of which the one at position 11 can be modified to be of pseudouridine (Ψ site).

b. The 2nd query sequence includes 3U residues, of which none can be modified to be of pseudouridine.

c. The 3rd query sequence includes 7 U residues, of which the one at position 21 can be modified to be of pseudouridine. All these results are fully consistent with the experimental observations.

Note: to get the anticipated prediction accuracy, the species button must be consistent with the source of query sequences: if the query sequences are from H. sapiens, check on the H. sapiens button; from M. musculus, check on the M. musculus button; from S. cerevisiae, check on the S. cerevisiae button.

Step 4. Click on the Data button to download the datasets used to train and test the iRNA-PseU predictor.

Step 5. Click on the Citation button to find the relevant papers that document the detailed development and algorithm of iRNAPseU.

It is instructive to point out that the AI tool has been developed by strictly observing the guidelines of “Chou’s 5-steps rule” and hence have the following notable merits (see, e.g., [2-29] and three comprehensive review papers [30-32]):

a. Crystal clear in logic development,

b. Completely transparent in operation,

c. Easily to repeat the reported results by other investigators,

d. With high potential in stimulating other sequenceanalyzing methods, and

e. Very convenient to be used by most experimental scientists.

Also, it has not escaped our notice that during the development of iRNA-PseU AI tool, the approach of general pseudo amino acid components [33] or PseAAC [34] had been applied and hence its accuracy would be much higher than its counterparts, as concurred by many investigators [30, 33-281]. It is anticipated that iRNA-PseU 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 [31, 32, 282-292] where the rule and its wide applications have been very impressively presented from various aspects or at different angles.

References

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269. Ghauri AW, Khan YD, Rasool N, Khan SA, Chou KC (2018) pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou's general PseAAC. Curr Pharm Des 24(34): 4034-4043.
270. Javed F, Hayat M (2018) Predicting subcellular localizations of multi-label proteins by incorporating the sequence features into Chou's PseAAC. Genomics 111(6): 1325-1332.
271. Ju Z, Wang SY (2018) Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition. Gene 664: 78-83.
272. 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.
273. 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(6): 2501-2509.
274. 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.
275. Liang Y, Zhang S (2018) Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence. J Theor Biol 454: 22-29.
276. Mei J, Y. Fu, Zhao J (2018) Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition. J Theor Biol 456: 41-48.
277. 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(1): 2359.
278. Mei J, Zhao J (2018) Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features. J Theor Biol 427: 147-153.
279. Mousavizadegan M, Mohabatkar H (2018) Computational prediction of antifungal peptides via Chou's PseAAC and SVM. J Bioinform Comput Biol 16(4): 1850016.
280. 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.
281. 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.
282. Chou KC (2019) Two kinds of metrics for computational biology. Genomics.
283. Chou KC (2019) Proposing pseudo amino acid components is an important milestone for proteome and genome analyses. International Journal for Peptide Research and Therapeutics (IJPRT).
284. Chou KC (2019) An insightful recollection for predicting protein subcellular locations in multi-label systems. Genomics.
285. Chou KC (2019) Progresses in predicting post-translational modification. International Journal of Peptide Research and Therapeutics (IJPRT).
286. 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.
287. Chou KC (2019) An insightful recollection since the distorted key theory was born about 23 years ago. Genomics.
288. Chou KC (2019) Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications. Trends in Artificial Inttelengence (TIA) 3: 60-74.
289. Chou KC (2020) Distorted Key Theory and Its Implication for Drug Development. Current Genomics.
290. Chou KC (2019) An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule. Current Pharmaceutical Design 25(4): 4223-4234.
291. 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.
292. Chou KC (2019) Gordon Life Science Institute: Its philosophy, achievements, and perspective. Annals of Cancer Therapy and Pharmacology 2: 01-26.

Abstract

In 2016 a very powerful AI (artificial intelligence) tool has been established for identifying RNA pseudouridine sites, which is one of the important post modifications in RNA [1]. To see how the webserver is working, please do the following.