scholarly journals PhyloFold: Precise and Swift Prediction of RNA Secondary Structures to Incorporate Phylogeny among Homologs

2020 ◽  
Author(s):  
Masaki Tagashira
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Prediction Accuracy ◽  
Structural Alignment ◽  
Source Code ◽  
Secondary Structures ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Link Type ◽  
Structural Alignments

AbstractMotivationThe simultaneous consideration of sequence alignment and RNA secondary structure, or structural alignment, is known to help predict more accurate secondary structures of homologs. However, the consideration is heavy and can be done only roughly to decompose structural alignments.ResultsThe PhyloFold method, which predicts secondary structures of homologs considering likely pairwise structural alignments, was developed in this study. The method shows the best prediction accuracy while demanding comparable running time compared to conventional methods.AvailabilityThe source code of the programs implemented in this study is available on “https://github.com/heartsh/phylofold” and “https://github.com/heartsh/phyloalifold“.Contact“[email protected]”.Supplementary informationSupplementary data are available.

scholarly journals RNAfamProb Plus NeoFold: Estimations of Posterior Probabilities on RNA Structural Alignment and RNA Secondary Structures with Incorporating Homologous-RNA Sequences

2019 ◽  
Author(s):  
Masaki Tagashira ◽  
Kiyoshi Asai
Keyword(s):  
Secondary Structure ◽  
Sequence Alignment ◽  
Structural Alignment ◽  
Secondary Structures ◽  
Simultaneous Optimization ◽  
Supplementary Information ◽  
Sequence Alignments ◽  
Rna Sequences ◽  
Link Type ◽  
Rna Structural Alignment

AbstractMotivationThe simultaneous optimization of the sequence alignment and secondary structures among RNAs, structural alignment, has been required for the more appropriate comparison of functional ncRNAs than sequence alignment. Pseudo-probabilities given RNA sequences on structural alignment have been desired for more-accurate secondary structures, sequence alignments, consensus secondary structures, and structural alignments. However, any algorithms have not been proposed for these pseudo-probabilities.ResultsWe invented the RNAfamProb algorithm, an algorithm for estimating these pseudo-probabilities. We performed the application of these pseudo-probabilities to two biological problems, the visualization with these pseudo-probabilities and maximum-expected-accuracy secondary-structure (estimation). The RNAfamProb program, an implementation of this algorithm, plus the NeoFold program, a maximum-expected-accuracy secondary-structure program with these pseudo-probabilities, demonstrated prediction accuracy better than three state-of-the-art programs of maximum-expected-accuracy secondary-structure while demanding running time far longer than these three programs as expected due to the intrinsic serious problem-complexity of structural alignment compared with independent secondary structure and sequence alignment. Both the RNAfamProb and NeoFold programs estimate matters more accurately with incorporating homologous-RNA sequences.AvailabilityThe source code of each of these two programs is available on each of “https://github.com/heartsh/rnafamprob” and “https://github.com/heartsh/neofold”.Contact“[email protected]” and “[email protected]”.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Estimating the power of sequence covariation for detecting conserved RNA structure

2020 ◽  
Vol 36 (10) ◽  
pp. 3072-3076 ◽  
Author(s):  
Elena Rivas ◽  
Jody Clements ◽  
Sean R Eddy
Keyword(s):  
Secondary Structure ◽  
Sequence Alignment ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Source Code ◽  
Web Server ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Detection Power ◽  
Non Coding Rnas

Abstract Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long non-coding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures. Availability and implementation The R-scape web server is at eddylab.org/R-scape, with a link to download the source code. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Consistent Consideration of RNA Structural Alignments Improves Prediction Accuracy of RNA Secondary Structures

2020 ◽  
Author(s):  
Masaki Tagashira
Keyword(s):  
Prediction Accuracy ◽  
Structural Alignment ◽  
Secondary Structures ◽  
The Novel ◽  
Pairwise Sequence Alignment ◽  
Sequence Alignments ◽  
Trade Off ◽  
Independent Components ◽  
Conventional Methods ◽  
Structural Alignments

ABSTRACTThe probabilistic consideration of the global pairwise sequence alignment of two RNAs tied with their global single secondary structures, or global pairwise structural alignment, is known to predict more accurately global single secondary structures of unaligned homologs by discriminating between conserved local single secondary structures and those not conserved. However, conducting rigorously this consideration is computationally impractical and thus has been done to decompose global pairwise structural alignments into their independent components, i.e. global pairwise sequence alignments and single secondary structures, by conventional methods. ConsHomfold and ConsAlifold, which predict the global single and consensus secondary structures of unaligned and aligned homologs considering consistently preferable (or sparse) global pairwise structural alignments on probability respectively, were developed and implemented in this study. These methods demonstrate the best trade-off of prediction accuracy while exhibiting comparable running time compared to conventional methods. ConsHomfold and ConsAlifold optionally report novel types of loop accessibility, which are useful for the analysis of sequences and secondary structures. These accessibilities are average on sparse global pairwise structural alignment and can be computed to extend the novel inside-outside algorithm proposed in this study that computes pair alignment probabilities on this alignment.

scholarly journals deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

2016 ◽  
Author(s):  
Dengfeng Guan ◽  
Bo Liu ◽  
Yadong Wang
Keyword(s):  
Source Code ◽  
Classification Method ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Supplementary Data ◽  
Link Type ◽  
Memory Footprint ◽  
Supplementary Material ◽  
De Bruijn

AbstractSummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.Availabilitythe C++ source code of deSPI is available at https://github.com/hitbc/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals KmerFinderJS: A client-server method for fast species typing of bacteria over slow Internet connections

2017 ◽  
Author(s):  
Jose Luis Bellod Cineros ◽  
Ole Lund
Keyword(s):  
Bacterial Species ◽  
Source Code ◽  
Supplementary Information ◽  
The Internet ◽  
Supplementary Data ◽  
Client Server ◽  
Link Type ◽  
Genome Data ◽  
Speed Up ◽  
Internet Connections

AbstractMotivationKmerFinder is a program based on K-mer statistics for identifying bacterial species in whole genome data, that as a web server that have been used more than 10.000 times. Kmer-FinderJS is a development of the KmerFinder that benefits from the downsampling of data using a prefix filtering used by KmerFinder, to minimize amount of data that needs to be transferred between the client and the server.ResultsKmerFinderJS replaces the python based hash structure for holding the databases with a true Key-value database. These improvements are shown to lead to a many-fold speed up of species identification with the internet transfer speeds that are realistic to expect today. It is also shown that the method can find the true content of an artificial metagenomic cocktail with no false positives.AvailabilityThe method is freely available at https://cge.cbs.dtu.dk/services/KmerFinderJS/ and as a source code at https://bitbucket.org/genomicepidemiology/[email protected] informationSupplementary data are available at biorxiv online.

scholarly journals hmmIBD: software to infer pairwise identity by descent between haploid genotypes

2017 ◽  
Author(s):  
Stephen F. Schaffner ◽  
Aimee R. Taylor ◽  
Wesley Wong ◽  
Dyann F. Wirth ◽  
Daniel E. Neafsey
Keyword(s):  
Plasmodium Falciparum ◽  
Malaria Parasite ◽  
Source Code ◽  
Simulated Data ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Identity By Descent ◽  
Pairwise Identity ◽  
Link Type

AbstractSummaryWe introduce hmmIBD, software to estimate pairwise identity by decent between haploid genomes, such as those of the malaria parasite, sampled from one or more populations. We verified hmmIBD using simulated data, benchmarked it against a previously published method for detecting IBD within populations, and demonstrated its utility using Plasmodium falciparum data from Cambodia and Ghana.Supplementary informationSupplementary data include Appendices S1, S2 and S3, and are available online.Availability and ImplemetationSource code written in C99/C11-compliant C and requiring no external libraries, is freely available for download at https://github.com/glipsnort/hmmIBD/releases, alongside test [email protected]

KEC: unique sequence search by K-mer exclusion

2021 ◽  
Author(s):  
Pavel Beran ◽  
Dagmar Stehlíková ◽  
Stephen P Cohen ◽  
Vladislav Čurn
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Source Code ◽  
Unique Sequence ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Laptop Computers ◽  
Sequence Search ◽  
Target Sequences ◽  
Cross Reference

Abstract Summary Searching for amino acid or nucleic acid sequences unique to one organism may be challenging depending on size of the available datasets. K-mer elimination by cross-reference (KEC) allows users to quickly and easily find unique sequences by providing target and non-target sequences. Due to its speed, it can be used for datasets of genomic size and can be run on desktop or laptop computers with modest specifications. Availability and implementation KEC is freely available for non-commercial purposes. Source code and executable binary files compiled for Linux, Mac and Windows can be downloaded from https://github.com/berybox/KEC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BioCommons: a robust java library for RNA structural bioinformatics

2021 ◽  
Author(s):  
Tomasz Zok
Keyword(s):  
Source Code ◽  
Structural Bioinformatics ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Bioinformatic Tools ◽  
Data Formats ◽  
Central Repository ◽  
Diverse Data ◽  
2D And 3D ◽  
Java Library

Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Amount of RNA secondary structure required to induce an alternative splice.

1987 ◽  
Vol 7 (9) ◽  
pp. 3194-3198 ◽  
Author(s):  
D Solnick ◽  
S I Lee
Keyword(s):  
Alternative Splicing ◽  
Secondary Structure ◽  
Alternative Splice ◽  
Rna Secondary Structure ◽  
Secondary Structures ◽  
Splice Sites ◽  
Perfect Complementarity ◽  
Set Up

We set up an alternative splicing system in vitro in which the relative amounts of two spliced RNAs, one containing and the other lacking a particular exon, were directly proportional to the length of an inverted repeat inserted into the flanking introns. We then used the system to measure the effect of intramolecular complementarity on alternative splicing in vivo. We found that an alternative splice was induced in vivo only when the introns contained more than approximately 50 nucleotides of perfect complementarity, that is, only when the secondary structure was much more stable than most if not all possible secondary structures in natural mRNA precursors. We showed further that intron insertions containing long complements to splice sites and a branch point inhibited splicing in vitro but not in vivo. These results raise the possibility that in cells most pre-mRNA secondary structures either are not maintained long enough to influence splicing choices, or never form at all.

scholarly journals RNA secondary structure prediction using deep learning with thermodynamic integration

2020 ◽  
Author(s):  
Kengo Sato ◽  
Manato Akiyama ◽  
Yasubumi Sakakibara
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Secondary Structures ◽  
Thermodynamic Integration ◽  
Rna Secondary Structure Prediction ◽  
Rna Secondary Structures ◽  
Non Coding Rnas

RNA secondary structure prediction is one of the key technologies for revealing the essential roles of functional non-coding RNAs. Although machine learning-based rich-parametrized models have achieved extremely high performance in terms of prediction accuracy, the risk of overfitting for such models has been reported. In this work, we propose a new algorithm for predicting RNA secondary structures that uses deep learning with thermodynamic integration, thereby enabling robust predictions. Similar to our previous work, the folding scores, which are computed by a deep neural network, are integrated with traditional thermodynamic parameters to enable robust predictions. We also propose thermodynamic regularization for training our model without overfitting it to the training data. Our algorithm (MXfold2) achieved the most robust and accurate predictions in computational experiments designed for newly discovered non-coding RNAs, with significant 2–10 % improvements over our previous algorithm (MXfold) and standard algorithms for predicting RNA secondary structures in terms of F-value.

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