Filters








11,761 Hits in 4.2 sec

Modeling RNA Molecules [chapter]

Neocles Leontis, Eric Westhof
2012 Nucleic acids and molecular biology  
Acknowledgment NBL expresses his gratitude to Vassiliki Leontis for her support during the preparation of this book.  ...  This is especially the case for those approaches relying on a modular view of RNA architecture with the resulting assembly of RNA elements and modules (Jossinet et al. 2010; Westhof et al. 2011 ). 3D  ...  One is the use of modeling predictions, firstly for searching noncoding RNAs in genomes and secondly for choosing among genomic regions those that are susceptible to fold into architectural domains or  ... 
doi:10.1007/978-3-642-25740-7_2 fatcat:sh2eavinhzbefjzshqqmlweffa

circDeep: deep learning approach for circular RNA classification from other long non-coding RNA

2019 Bioinformatics  
However, the available tools are less than 80 percent accurate for distinguishing circular RNAs from other lncRNAs due to difficulty of classification.  ...  Therefore, the availability of a more accurate and fast machine learning method for the identification of circular RNAs, which considers the specific features of circular RNA, is essential to the development  ...  Acknowledgements We would like to thank Eric Rouchka and Julia Chariker for insightful discussions and comments.  ... 
doi:10.1093/bioinformatics/btz537 pmid:31268128 pmcid:PMC6956777 fatcat:7exee2gglbdmzkdmb6q6j74jda

Architecture and dynamics of overlapped RNA regulatory networks

Christopher P. Lapointe, Melanie A. Preston, Daniel Wilinski, Harriet A.J. Saunders, Zachary T. Campbell, Marvin Wickens
2017 RNA: A publication of the RNA Society  
The diverse interplay between overlapping RNA-protein networks provides versatile opportunities for regulation and evolution.  ...  Yet little is known about the architecture or dynamics of overlapped networks.  ...  Kimble (University of Wisconsin-Madison) for use of a computational server, and L. Vanderploeg of the Biochemistry Media Laboratory for help with the figures.  ... 
doi:10.1261/rna.062687.117 pmid:28768715 fatcat:4uccjamxorbkbgecyxouft6eri

RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

J. A. Cruz, M.-F. Blanchet, M. Boniecki, J. M. Bujnicki, S.-J. Chen, S. Cao, R. Das, F. Ding, N. V. Dokholyan, S. C. Flores, L. Huang, C. A. Lavender (+21 others)
2012 RNA: A publication of the RNA Society  
The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts  ...  We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.  ...  The Dokholyan group's DMD approach has been designed for fold refinement of relatively large RNAs with complex 3D architectures.  ... 
doi:10.1261/rna.031054.111 pmid:22361291 pmcid:PMC3312550 fatcat:tuhkhy5atzdblmevgb237nmhye

CoCoNet: Boosting RNA contact prediction by convolutional neural networks [article]

Mehari B Zerihun, Fabrizio Pucci, Alexander Schug
2020 bioRxiv   pre-print
Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins but not for RNAs.  ...  Here, we demonstrate how the small amount of data available for RNA can be used to significantly improve prediction of RNA contact maps.  ...  (www.gausscentre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC  ... 
doi:10.1101/2020.07.30.229484 fatcat:xq235m2r6nbyxfezwekvfe7ur4

EternaBrain: Automated RNA design through move sets from an Internet-scale RNA videogame [article]

Rohan Koodli, Benjamin Keep, Katherine R Coppess, Fernando Portela, Rhiju Das
2018 bioRxiv   pre-print
These applications require RNA sequences that fold into target base-pairing patterns, but computational algorithms generally remain inadequate for these RNA secondary structure design tasks.  ...  EternaBrain surpasses all six other prior algorithms that were not informed by Eterna strategies and suggests a path for automated RNA design to achieve human-competitive performance.  ...  , translation, and game-playing algorithms make powerful use of statistical pattern recognition through multi-layer artificial neural networks (12) .  ... 
doi:10.1101/326736 fatcat:sqezzwwalvhl5pa4mvlpr2nfx4

EternaBrain: Automated RNA design through move sets and strategies from an Internet-scale RNA videogame

Rohan V. Koodli, Benjamin Keep, Katherine R. Coppess, Fernando Portela, Rhiju Das, Eterna participants, Shi-Jie Chen
2019 PLoS Computational Biology  
Emerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these  ...  Our study provides useful lessons for future efforts to achieve human-competitive performance with automated RNA design algorithms.  ...  Nicol for expert technical assistance; J. Shi, M. Wu, P. Eastman, and B. Ramsundar for scientific discussions; and M. Gotrik for comments on the manuscript.  ... 
doi:10.1371/journal.pcbi.1007059 pmid:31247029 pmcid:PMC6597038 fatcat:nfq6naqsf5ggjhukq5kfusgfjq

Modelling ?evo-devo? with RNA

Walter Fontana
2002 Bioessays  
The folding of RNA sequences into secondary structures is a simple yet biophysically grounded model of a genotype-phenotype map.  ...  Its computational and mathematical analysis has uncovered a surprisingly rich statistical structure characterized by shape space covering, neutral networks and plastogenetic congruence.  ...  folding into the shape of rank r.  ... 
doi:10.1002/bies.10190 pmid:12447981 fatcat:j2oxjhk3wvggtjlkxkl6khwvl4

Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data [article]

Almas Jabeen, Nadeem Ahmad, Khalid Raza
2017 bioRxiv   pre-print
Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data  ...  In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation.  ...  ing based classification, would provide powerful toolboxes for class fying transcriptome information available through RNA-Seq data.  ... 
doi:10.1101/120592 fatcat:frdzqa4awvbuddkp4vxmyvyo2q

Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs

Yu Guo, Katherine Alexander, Andrew G. Clark, Andrew Grimson, Haiyuan Yu
2016 RNA: A publication of the RNA Society  
Analysis of transcription regulatory networks has revealed many principal features that govern gene expression regulation.  ...  These results demonstrate that although TFs and miRNAs both regulate gene expression, they occupy distinct niches in the overall regulatory network within the cell.  ...  ACKNOWLEDGMENTS We thank Anders Skanderup for insightful scientific discussions.  ... 
doi:10.1261/rna.048025.114 pmid:27604961 pmcid:PMC5066619 fatcat:r5kqlopisnbybjd43oqlrq37nq

Anti-CRISPR RNAs: designing universal riboregulators with deep learning of Csy4-mediated RNA processing [article]

Haotian Guo, Xiaohu Song, Ariel B Lindner
2020 bioRxiv   pre-print
RNA-based regulation offers a promising alternative of protein-based transcriptional networks.  ...  Lacking discernible sequence-structural commonality among processable pre-crRNAs, we trained a neural network for accurate classification (f1-score ≈ 0.93).  ...  Vectors were later computed through 1-dimensional convolutions and fully-connected neural networks. The detailed architecture of neural networks is shown in Fig.  ... 
doi:10.1101/2020.11.15.384107 fatcat:gzfn5xol3vgjfn5y3mb5zjg5sq

The architecture of the human RNA-binding protein regulatory network [article]

Alessandro Quattrone, Erik Dassi
2016 bioRxiv   pre-print
RNA-binding proteins (RBPs) are key players of post-transcriptional regulation of gene expression.  ...  Our analysis identified two features defining the structure of the RBP-RBP regulatory network.  ...  D) shows link density for families of RNA-binding proteins found in the 248 network.  ... 
doi:10.1101/041426 fatcat:bv43tygih5ce3pcxw4xvbr3hze

A Deep Learning Approach for Learning Intrinsic Protein-RNA Binding Preferences [article]

Ilan Ben-Bassat, Benny Chor, Yaron Orenstein
2018 bioRxiv   pre-print
We present two different network architectures: a convolutional neural network (CNN), and a recurrent neural network (RNN).  ...  Results: We developed DLPRB, a new deep neural network (DNN) approach for learning protein-RNA binding preferences and predicting novel interactions.  ...  rank-sum test).  ... 
doi:10.1101/328633 fatcat:6ruj2ip6bncgle2f3h5odrfgxe

CoCoNet—boosting RNA contact prediction by convolutional neural networks

Mehari B Zerihun, Fabrizio Pucci, Alexander Schug
2021 Nucleic Acids Research  
Here, we demonstrate how the available smaller data for RNA can be used to improve prediction of RNA contact maps.  ...  Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins.  ...  (www.gauss-centre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at J ülich Supercomputing Centre (JSC  ... 
doi:10.1093/nar/gkab1144 pmid:34871451 pmcid:PMC8682773 fatcat:iug2itktabdahh4re325x4on5y

Deep neural networks for interpreting RNA binding protein target preferences [article]

Mahsa Ghanbari, Uwe Ohler
2019 bioRxiv   pre-print
We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP binding preferences.  ...  Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple  ...  Acknowledgements The authors would like to acknowledge Neelanjan Mukherjee for providing the processed PAR-CLIP data.  ... 
doi:10.1101/518191 fatcat:qcemv276qjdn5fyh5q6rpq4v34
« Previous Showing results 1 — 15 out of 11,761 results