Comparison and Analysis of Computational Methods for Identifying N6 - methyladenosine Sites in Saccharomyces Cerevisiae

2020 ◽  
Vol 26 ◽  
Author(s):  
Pengmian Feng ◽  
Lijing Feng ◽  
Chaohui Tang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Computational Methods ◽  
Computational Analysis ◽  
Computational Prediction ◽  
Experimental Methods ◽  
Biological Processes ◽  
Biological Functions ◽  
Precise Location ◽  
Future Directions ◽  
The Past

Background and Purpose: N 6 -methyladenosine (m6A) plays critical roles in a broad set of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As good complements to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. Methods: In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to give a comprehensive review and comparison on existing methods. Results: Since researches on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progresses on computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites were presented. Conclusion: Taken together, we anticipate that this review will provide important guides for computational analysis of m 6A modifications.

scholarly journals MicroRNAs in Female Malignancies

2019 ◽  
Vol 18 ◽  
pp. 117693511982874 ◽  
Author(s):  
Themis Liolios ◽  
Stavroula Lila Kastora ◽  
Giorgia Colombo
Keyword(s):  
Gene Expression ◽  
Computational Analysis ◽  
Target Genes ◽  
Vulvar Cancer ◽  
Chemotherapeutic Agents ◽  
In Vitro Assays ◽  
Biological Processes ◽  
Potential Mechanism ◽  
Biological Functions

MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs that can play a fundamental regulatory role in the gene expression of various organisms. Current research suggests that miRNAs can assume pivotal roles in carcinogenesis. In this article, through bioinformatics mining and computational analysis, we determine a single miRNA commonly involved in the development of breast, cervical, endometrial, ovarian, and vulvar cancer, whereas we underline the existence of 7 more miRNAs common in all examined malignancies with the exception of vulvar cancer. Furthermore, we identify their target genes and encoded biological functions. We also analyze common biological processes on which all of the identified miRNAs act and we suggest a potential mechanism of action. In addition, we analyze exclusive miRNAs among the examined malignancies and bioinformatically explore their functionality. Collectively, our data can be employed in in vitro assays as a stepping stone in the identification of a universal machinery that is derailed in female malignancies, whereas exclusive miRNAs may be employed as putative targets for future chemotherapeutic agents or cancer-specific biomarkers.

A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae

2019 ◽  
Author(s):  
Xiaolei Zhu ◽  
Jingjing He ◽  
Shihao Zhao ◽  
Wei Tao ◽  
Yi Xiong ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Training Dataset ◽  
Biological Processes ◽  
Independent Dataset ◽  
The Past ◽  
Redundant Data ◽  
Position Bias ◽  
Bias Index ◽  
Comprehensive Comparison

Abstract N6-methyladenosine (m6A) modification, as one of the commonest post-transcription modifications in RNAs, has been reported to be highly related to many biological processes. Over the past decade, several tools for m6A sites prediction of Saccharomyces cerevisiae have been developed and are freely available online. However, the quality of predictions by these tools is difficult to quantify and compare. In this study, an independent dataset M6Atest6540 was compiled to systematically evaluate nine publicly available m6A prediction tools for S. cerevisiae. The experimental results indicate that RAM-ESVM achieved the best performance on M6Atest6540; however, most models performed substantially worse than their performances reported in the original papers. The benchmark dataset Met2614, which was used as the training dataset for the nine methods, were further analyzed by using a position bias index. The results demonstrated the significantly different bias of dataset Met2614 compared with the RNA segments around m6A sites recorded in RMBase. Moreover, newMet2614 was collected by randomly selecting RNA segments from non-redundant data recorded in RMBase, and three different kinds of features were extracted. The performances of the models built on Met2614 and newMet2614 with the features were compared, which shows the better generalization of models built on newMet2614. Our results also indicate the position-specific propensity-based features outperform other features, although they are also easily over-fitted on a biased dataset.

scholarly journals Circular RNAs in osteoarthritis: indispensable regulators and novel strategies in clinical implications

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Wenchao Zhang ◽  
Lin Qi ◽  
Ruiqi Chen ◽  
Jieyu He ◽  
Zhongyue Liu ◽  
...  
Keyword(s):  
Hot Spot ◽  
Circular Rnas ◽  
Clinical Implications ◽  
Biological Processes ◽  
Future Directions ◽  
The Past ◽  
Prevalent Disease ◽  
Post Traumatic ◽  
Socioeconomic Burden ◽  
Insight Into

AbstractOver the past decades, circular RNAs (circRNAs) have emerged as a hot spot and sparked intensive interest. Initially considered as the transcriptional noises, further studies have indicated that circRNAs are crucial regulators in multiple cellular biological processes, and thus engage in the development and progression of many diseases including osteoarthritis (OA). OA is a prevalent disease that mainly affects those aging, obese and post-traumatic population, posing as a major source of socioeconomic burden. Recently, numerous circRNAs have been found aberrantly expressed in OA tissues compared with counterparts. More importantly, circRNAs have been demonstrated to interplay with components in OA microenvironments, such as chondrocytes, synoviocytes and macrophages, by regulation of their proliferation, apoptosis, autophagy, inflammation, or extracellular matrix reorganization. Herein, in this review, we extensively summarize the roles of circRNAs in OA microenvironment, progression, and putative treatment, as well as envision the future directions for circRNAs research in OA, with the aim to provide a novel insight into this field.

Identification of long noncoding RNAs with machine learning methods: a review

2021 ◽  
Author(s):  
Lei Xu ◽  
Shihu Jiao ◽  
Dandan Zhang ◽  
Song Wu ◽  
Haihong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Noncoding Rnas ◽  
Experimental Methods ◽  
Long Noncoding Rnas ◽  
Biological Functions ◽  
Prediction Tools ◽  
Machine Learning Methods ◽  
High Resource ◽  
Future Work

Abstract Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.

Evaluation of different computational methods on 5-methylcytosine sites identification

2019 ◽  
Vol 21 (3) ◽  
pp. 982-995 ◽  
Author(s):  
Hao Lv ◽  
Zi-Mei Zhang ◽  
Shi-Hao Li ◽  
Jiu-Xin Tan ◽  
Wei Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Homo Sapiens ◽  
Great Increase ◽  
Computational Prediction ◽  
Extraction Methods ◽  
Biological Functions ◽  
Accurate Identification ◽  
The Past ◽  
Reliable Model ◽  
Multiple Species

Abstract 5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

Recent Advances of Computational Methods for Identifying Bacteriophage Virion Proteins

2020 ◽  
Vol 27 (4) ◽  
pp. 259-264 ◽  
Author(s):  
Wei Chen ◽  
Fulei Nie ◽  
Hui Ding
Keyword(s):  
Experimental Method ◽  
Computational Methods ◽  
Biological Processes ◽  
Future Perspectives ◽  
Accurate Identification ◽  
Independent Dataset ◽  
Computational Approaches ◽  
Comprehensive Review ◽  
The Past ◽  
Mechanism Of Interaction

Phage Virion Proteins (PVP) are essential materials of bacteriophage, which participate in a series of biological processes. Accurate identification of phage virion proteins is helpful to understand the mechanism of interaction between the phage and its host bacteria. Since experimental method is labor intensive and time-consuming, in the past few years, many computational approaches have been proposed to identify phage virion proteins. In order to facilitate researchers to select appropriate methods, it is necessary to give a comprehensive review and comparison on existing computational methods on identifying phage virion proteins. In this review, we summarized the existing computational methods for identifying phage virion proteins and also assessed their performances on an independent dataset. Finally, challenges and future perspectives for identifying phage virion proteins were presented. Taken together, we hope that this review could provide clues to researches on the study of phage virion proteins.

scholarly journals The cell biology of inflammasomes: Mechanisms of inflammasome activation and regulation

2016 ◽  
Vol 213 (6) ◽  
pp. 617-629 ◽  
Author(s):  
Deepika Sharma ◽  
Thirumala-Devi Kanneganti
Keyword(s):  
Cell Death ◽  
Inflammatory Response ◽  
Cell Biology ◽  
Inflammasome Activation ◽  
Biological Processes ◽  
Biological Functions ◽  
Cellular Processes ◽  
The Past ◽  
Functional Output ◽  
Made In

Over the past decade, numerous advances have been made in the role and regulation of inflammasomes during pathogenic and sterile insults. An inflammasome complex comprises a sensor, an adaptor, and a zymogen procaspase-1. The functional output of inflammasome activation includes secretion of cytokines, IL-1β and IL-18, and induction of an inflammatory form of cell death called pyroptosis. Recent studies have highlighted the intersection of this inflammatory response with fundamental cellular processes. Novel modulators and functions of inflammasome activation conventionally associated with the maintenance of homeostatic biological functions have been uncovered. In this review, we discuss the biological processes involved in the activation and regulation of the inflammasome.

Understanding the Function of Mammalian Sirtuins and Protein Lysine Acylation

2021 ◽  
Vol 90 (1) ◽  
Author(s):  
Miao Wang ◽  
Hening Lin
Keyword(s):  
Posttranslational Modification ◽  
Neurological Diseases ◽  
Annual Review ◽  
Publication Date ◽  
Lysine Acetylation ◽  
Biological Processes ◽  
Biological Functions ◽  
The Past ◽  
Fatty Acylation ◽  
Protein Lysine Acetylation

Protein lysine acetylation is an important posttranslational modification that regulates numerous biological processes. Targeting lysine acetylation regulatory factors, such as acetyltransferases, deacetylases, and acetyl-lysine recognition domains, has been shown to have potential for treating human diseases, including cancer and neurological diseases. Over the past decade, many other acyl-lysine modifications, such as succinylation, crotonylation, and long-chain fatty acylation, have also been investigated and shown to have interesting biological functions. Here, we provide an overview of the functions of different acyl-lysine modifications in mammals. We focus on lysine acetylation as it is well characterized, and principles learned from acetylation are useful for understanding the functions of other lysine acylations. We pay special attention to the sirtuins, given that the study of sirtuins has provided a great deal of information about the functions of lysine acylation. We emphasize the regulation of sirtuins to illustrate that their regulation enables cells to respond to various signals and stresses. Expected final online publication date for the Annual Review of Biochemistry, Volume 90 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Computational Prediction of MicroRNAs Encoded in Viral and Other Genomes

2006 ◽  
Vol 2006 ◽  
pp. 1-10 ◽  
Author(s):  
Gard O. S. Thomassen ◽  
Øystein Røsok ◽  
Torbjørn Rognes
Keyword(s):  
Computational Methods ◽  
Computational Prediction ◽  
Experimental Methods ◽  
Northern Blotting ◽  
False Positives ◽  
Small Study ◽  
Detection Methods ◽  
Microrna Detection ◽  
Mature Microrna ◽  
Mirna Detection

We present an overview of selected computational methods for microRNA prediction. It is especially aimed at viral miRNA detection. As the number of microRNAs increases and the range of genomes encoding miRNAs expands, it seems that these small regulators have a more important role than has been previously thought. Most microRNAs have been detected by cloning and Northern blotting, but experimental methods are biased towards abundant microRNAs as well as being time-consuming. Computational detection methods must therefore be refined to serve as a faster, better, and more affordable method for microRNA detection. We also present data from a small study investigating the problems of computational miRNA prediction. Our findings suggest that the prediction of microRNA precursor candidates is fairly easy, while excluding false positives as well as exact prediction of the mature microRNA is hard. Finally, we discuss possible improvements to computational microRNA detection.

scholarly journals Learning Analytics for Understanding and Supporting Collaboration

2020 ◽  
Author(s):  
Bodong Chen
Keyword(s):  
Collaborative Learning ◽  
Computational Methods ◽  
Learning Analytics ◽  
Data Analytics ◽  
Computational Analysis ◽  
Modern Society ◽  
Computer Supported Cooperative Work ◽  
Computer Supported Collaborative Learning ◽  
Future Directions ◽  
Collaborative Processes

Collaboration is an important competency in the modern society. To harness the intersection of learning, work, and collaboration with analytics, several fundamental challenges need to be addressed. This chapter about collaboration analytics aims to highlight these challenges for the learning analytics community. We first survey the conceptual landscape of collaboration and learning with a focus on the computer-supported collaborative learning (CSCL) literature while attending to perspectives from computer supported cooperative work (CSCW). Grounded in the conceptual exploration, we then distinguish two salient strands of collaboration analytics: (a) /computational analysis of collaboration/ that applies computational methods to examining collaborative processes; and (b) /analytics for collaboration/ which is primarily concerned with designing and deploying data analytics in authentic contexts to facilitate collaboration. Examples and cases representing different contexts for learning and analytical frames are presented, followed by a discussion of key challenges and future directions.

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