RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis
Authors
Keywords
-
Journal
BIOINFORMATICS
Volume -, Issue -, Pages -
Publisher
Oxford University Press (OUP)
Online
2021-05-18
DOI
10.1093/bioinformatics/btab391
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Improved RNA Secondary Structure and Tertiary Base-pairing Prediction Using Evolutionary Profile, Mutational Coupling and Two-dimensional Transfer Learning
- (2021) Jaswinder Singh et al. BIOINFORMATICS
- Estimation of model accuracy in CASP13
- (2019) Jianlin Cheng et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning
- (2019) Jaswinder Singh et al. Nature Communications
- Accurate inference of the full base-pairing structure of RNA by deep mutational scanning and covariation-induced deviation of activity
- (2019) Zhe Zhang et al. NUCLEIC ACIDS RESEARCH
- bpRNA: large-scale automated annotation and analysis of RNA secondary structure
- (2018) Padideh Danaee et al. NUCLEIC ACIDS RESEARCH
- Enhanced prediction of RNA solvent accessibility with long short-term memory neural networks and improved sequence profiles
- (2018) Saisai Sun et al. BIOINFORMATICS
- RNAcentral: a hub of information for non-coding RNA sequences
- (2018) et al. NUCLEIC ACIDS RESEARCH
- B -factor profile prediction for RNA flexibility using support vector machines
- (2017) Ivantha Guruge et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide–nucleotide interactions from direct coupling analysis
- (2017) Jian Wang et al. NUCLEIC ACIDS RESEARCH
- Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families
- (2017) Ioanna Kalvari et al. NUCLEIC ACIDS RESEARCH
- Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age
- (2017) Joerg Schaarschmidt et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme
- (2017) Zhichao Miao et al. RNA
- Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
- (2017) Sheng Wang et al. PLoS Computational Biology
- 3D RNA and Functional Interactions from Evolutionary Couplings
- (2016) Caleb Weinreb et al. CELL
- A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs
- (2016) Elena Rivas et al. NATURE METHODS
- Evaluation of free modeling targets in CASP11 and ROLL
- (2016) Lisa N. Kinch et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction
- (2016) Yuedong Yang et al. RNA
- RNA-PuzzlesRound II: assessment of RNA structure prediction programs applied to three large RNA structures
- (2015) Zhichao Miao et al. RNA
- MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins
- (2014) David T. Jones et al. BIOINFORMATICS
- RNAcentral: an international database of ncRNA sequences
- (2014) NUCLEIC ACIDS RESEARCH
- Infernal 1.1: 100-fold faster RNA homology searches
- (2013) E. P. Nawrocki et al. BIOINFORMATICS
- RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts
- (2013) Sarah Geisler et al. NATURE REVIEWS MOLECULAR CELL BIOLOGY
- Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models
- (2013) Magnus Ekeberg et al. PHYSICAL REVIEW E
- Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era
- (2013) H. Kamisetty et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas
- (2013) A. I. Petrov et al. RNA
- CD-HIT: accelerated for clustering the next-generation sequencing data
- (2012) Limin Fu et al. BIOINFORMATICS
- RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction
- (2012) J. A. Cruz et al. RNA
- ViennaRNA Package 2.0
- (2011) Ronny Lorenz et al. Algorithms for Molecular Biology
- PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments
- (2011) David T. Jones et al. BIOINFORMATICS
- HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
- (2011) Michael Remmert et al. NATURE METHODS
- Direct-coupling analysis of residue coevolution captures native contacts across many protein families
- (2011) F. Morcos et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments
- (2010) Lukas Burger et al. PLoS Computational Biology
- The tedious task of finding homologous noncoding RNA genes
- (2009) P. Menzel et al. RNA
- Fast and accurate search for non-coding RNA pseudoknot structures in genomes
- (2008) Zhibin Huang et al. BIOINFORMATICS
- RNAalifold: improved consensus structure prediction for RNA alignments
- (2008) Stephan H Bernhart et al. BMC BIOINFORMATICS
- PseudoBase++: an extension of PseudoBase for easy searching, formatting and visualization of pseudoknots
- (2008) M. Taufer et al. NUCLEIC ACIDS RESEARCH
- Identification of direct residue contacts in protein-protein interaction by message passing
- (2008) M. Weigt et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started