scholarly journals NOVEL INTRONIC NON-CODING RNAS CONTRIBUTE TO MAINTENANCE OF PHENOTYPE IN SACCHAROMYCES CEREVISIAE

2015 ◽  
Author(s):  
Katarzyna B Hooks ◽  
Samina Naseeb ◽  
Sam Griffiths-Jones ◽  
Daniela Delneri
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Rna Structure ◽  
Structure Prediction ◽  
De Novo ◽  
Intron Retention ◽  
Intron Loss ◽  
Common Belief ◽  
Rna Structures ◽  
Protein Coding ◽  
Non Coding Rna

The Saccharomyces cerevisiae genome has undergone extensive intron loss during its evolutionary history. It has been suggested that the few remaining introns (in only 5% of protein-coding genes) are retained because of their impact on function under stress conditions. Here, we explore the possibility that novel non-coding RNA structures (ncRNAs) are embedded within intronic sequences and are contributing to phenotype and intron retention in yeast. We employed de novo RNA structure prediction tools to screen intronic sequences in S. cerevisiae and 36 other fungi. We identified and validated 19 new intronic RNAs via RNAseq and RT-PCR. Contrary to common belief that excised introns are rapidly degraded, we found that, in six cases, the excised introns were maintained intact in the cells. In other two cases we showed that the ncRNAs were further processed from their introns. RNAseq analysis confirmed higher expression of introns in the ribosomial protein genes containing predicted RNA structures. We deleted the novel intronic RNA structure within the GLC7 intron and showed that this predicted ncRNA, rather than the intron itself, is responsible for the cell???s ability to respond to salt stress. We also showed a direct association between the presence of the intronic ncRNA and GLC7 expression. Overall, these data support the notion that some introns may have been maintained in the genome because they harbour functional ncRNAs.

scholarly journals An algorithm for template-based prediction of secondary structures of individual RNA sequences

2017 ◽  
Author(s):  
Josef Pánek ◽  
Martin Černý
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
De Novo ◽  
Secondary Structures ◽  
Rna Structures ◽  
Rna Sequences ◽  
Biologically Relevant ◽  
Rna Molecules ◽  
Rna Structure Prediction ◽  
Limited Applicability

ABSTRACTWhile understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we provide a method to solve a simpler yet still biologically relevant problem: prediction of secondary RNA structure using structure of different molecules as a template.Our method identifies conserved and unconserved subsequences within an RNA molecule. For conserved subsequences, the template structure is directly transferred into the generated structure and combined with de-novo predicted structure for the unconserved subsequences with low evolutionary conservation. The method also determines, when the generated structure is unreliable.The method is validated using experimentally identified structures. The accuracy of the method exceeds that of classical prediction algorithms and constrained prediction methods. This is demonstrated by comparison using large number of heterogeneous RNAs. The presented method is fast and robust, and useful for various applications requiring knowledge of secondary structures of individual RNA sequences.

scholarly journals Classification and clustering of RNA crosslink-ligation data reveal complex structures and homodimers

2021 ◽  
Author(s):  
Minjie Zhang ◽  
Irena T Fischer-Hwang ◽  
Kongpan Li ◽  
Jianhui Bai ◽  
Jian-Fu Chen ◽  
...  
Keyword(s):  
Rna Structure ◽  
De Novo ◽  
Rna Virus ◽  
Living Cells ◽  
Published Data ◽  
Rna Structures ◽  
Computational Tools ◽  
Systematic Analysis ◽  
Structure Information ◽  
Classification And Clustering

The recent development and application of methods based on the general principle of "crosslinking and proximity ligation" (crosslink-ligation) are revolutionizing RNA structure studies in living cells. However, extracting structure information from such data presents unique challenges. Here we introduce a set of computational tools for the systematic analysis of data from a wide variety of crosslink-ligation methods, specifically focusing on read mapping, alignment classification and clustering. We design a new strategy to map short reads with irregular gaps at high sensitivity and specificity. Analysis of previously published data reveals distinct properties and bias caused by the crosslinking reactions. We perform rigorous and exhaustive classification of alignments and discover 8 types of arrangements that provide distinct information on RNA structures and interactions. To deconvolve the dense and intertwined gapped alignments, we develop a net-work/graph-based tool CRSSANT (Crosslinked RNA Secondary Structure Analysis using Network Techniques), which enables clustering of gapped alignments and discovery of new alternative and dynamic conformations. We discover that multiple crosslinking and ligation events can occur on the same RNA, generating multi-segment alignments to report complex high level RNA structures and multi-RNA interactions. We find that alignments with overlapped segments are produced from potential homodimers and develop a new method for their de novo identification. Analysis of overlapping alignments revealed potential new homodimers in cellular noncoding RNAs and RNA virus genomes in the Picornaviridae family. Together, this suite of computational tools enables rapid and efficient analysis of RNA structure and interaction data in living cells.

scholarly journals RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

2022 ◽  
Vol 1 ◽  
Author(s):  
Zhi-Hao Guo ◽  
Li Yuan ◽  
Ya-Lan Tan ◽  
Ben-Gong Zhang ◽  
Ya-Zhou Shi
Keyword(s):  
Statistical Analysis ◽  
Rna Structure ◽  
Structure Prediction ◽  
Local Coordinate System ◽  
3D Structure ◽  
Scoring Function ◽  
Rna Structures ◽  
Cellular Functions ◽  
3D Structures ◽  
Structure Annotation

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

scholarly journals RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaswinder Singh ◽  
Jack Hanson ◽  
Kuldip Paliwal ◽  
Yaoqi Zhou
Keyword(s):  
Secondary Structure ◽  
Transfer Learning ◽  
Rna Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Noncoding Rnas ◽  
Rna Structures ◽  
Base Pairs ◽  
Functional Annotations ◽  
Model Training

AbstractThe majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only $$<$$<250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of $$> $$>10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.

scholarly journals De Novo Origination of a New Protein-Coding Gene in Saccharomyces cerevisiae

Genetics ◽  
2008 ◽  
Vol 179 (1) ◽  
pp. 487-496 ◽  
Author(s):  
Jing Cai ◽  
Ruoping Zhao ◽  
Huifeng Jiang ◽  
Wen Wang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
De Novo ◽  
Protein Coding ◽  
New Protein

scholarly journals Genome analysis of Plectus murrayi, a nematode from continental Antarctica

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xia Xue ◽  
Anton Suvorov ◽  
Stanley Fujimoto ◽  
Adler R Dilman ◽  
Byron J Adams
Keyword(s):  
De Novo ◽  
Intron Retention ◽  
Model Organism ◽  
Freeze Tolerance ◽  
Comparative Genomic ◽  
Harsh Environment ◽  
Adaptive Responses ◽  
Base Pairs ◽  
Protein Coding ◽  
Genome Decay

Abstract Plectus murrayi is one of the most common and locally abundant invertebrates of continental Antarctic ecosystems. Because it is readily cultured on artificial medium in the laboratory and highly tolerant to an extremely harsh environment, P. murrayi is emerging as a model organism for understanding the evolutionary origin and maintenance of adaptive responses to multiple environmental stressors, including freezing and desiccation. The de novo assembled genome of P. murrayi contains 225.741 million base pairs and a total of 14,689 predicted genes. Compared to Caenorhabditis elegans, the architectural components of P. murrayi are characterized by a lower number of protein-coding genes, fewer transposable elements, but more exons, than closely related taxa from less harsh environments. We compared the transcriptomes of lab-reared P. murrayi with wild-caught P. murrayi and found genes involved in growth and cellular processing were up-regulated in lab-cultured P. murrayi, while a few genes associated with cellular metabolism and freeze tolerance were expressed at relatively lower levels. Preliminary comparative genomic and transcriptomic analyses suggest that the observed constraints on P. murrayi genome architecture and functional gene expression, including genome decay and intron retention, may be an adaptive response to persisting in a biotically simplified, yet consistently physically harsh environment.

scholarly journals A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

2021 ◽  
Author(s):  
Tycho Marinus ◽  
Adam B Fessler ◽  
Craig A Ogle ◽  
Danny Incarnato
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Critical Role ◽  
Living Cells ◽  
Pathological Process ◽  
Rna Structures ◽  
Rna Molecules ◽  
Derived Data

Abstract Due to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

scholarly journals A putative de novo evolved gene required for spermatid chromatin condensation in Drosophila melanogaster

2021 ◽  
Author(s):  
Emily L. Rivard ◽  
Andrew G. Ludwig ◽  
Prajal H. Patel ◽  
Anna Grandchamp ◽  
Sarah E. Arnold ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Male Fertility ◽  
De Novo ◽  
Developmental Process ◽  
Chromatin Condensation ◽  
Protein Coding ◽  
Non Coding Rna ◽  
De Novo Genes ◽  
History Of ◽  
Redundant Role

Comparative genomics has enabled the identification of genes that potentially evolved de novo from non-coding sequences. Many such genes are expressed in male reproductive tissues, but their functions remain poorly understood. To address this, we conducted a functional genetic screen of over 40 putative de novo genes with testis-enriched expression in Drosophila melanogaster and identified one gene, atlas, required for male fertility. Detailed genetic and cytological analyses show that atlas is required for proper chromatin condensation during the final stages of spermatogenesis. Atlas protein is expressed in spermatid nuclei and facilitates the transition from histone- to protamine-based chromatin packaging. Complementary evolutionary analyses revealed the complex evolutionary history of atlas. The protein-coding portion of the gene likely arose at the base of the Drosophila genus on the X chromosome but was unlikely to be essential, as it was then lost in several independent lineages. Within the last ~15 million years, however, the gene moved to an autosome, where it fused with a conserved non-coding RNA and evolved a non-redundant role in male fertility. Altogether, this study provides insight into the integration of novel genes into biological processes, the links between genomic innovation and functional evolution, and the genetic control of a fundamental developmental process, gametogenesis.

scholarly journals RNA 3D modeling with FARFAR2, online

2020 ◽  
Author(s):  
Andrew Watkins ◽  
Rhiju Das
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Chemical Shifts ◽  
Three Dimensional ◽  
Structural Data ◽  
Dimensional Structure ◽  
Rna Structures ◽  
Beet Western Yellows Virus ◽  
H Nmr ◽  
Sampling Algorithms

AbstractUnderstanding the three-dimensional structure of an RNA molecule is often essential to understanding its function. Sampling algorithms and energy functions for RNA structure prediction are improving, due to the increasing diversity of structural data available for training statistical potentials and testing structural data, along with a steady supply of blind challenges through the RNA Puzzles initiative. The recent FARFAR2 algorithm enables near-native structure predictions on fairly complex RNA structures, including automated selection of final candidate models and estimation of model accuracy. Here, we describe the use of a publicly available webserver for RNA modeling for realistic scenarios using FARFAR2, available at https://rosie.rosettacommons.org/farfar2. We walk through two cases in some detail: a simple model pseudoknot from the frameshifting element of beet western yellows virus modeled using the “basic interface” to the webserver, and a replication of RNA-Puzzle 20, a metagenomic twister sister ribozyme, using the “advanced interface.” We also describe example runs of FARFAR2 modeling including two kinds of experimental data: a c-di-GMP riboswitch modeled with low resolution restraints from MOHCA-seq experiments and a tandem GA motif modeled with 1H NMR chemical shifts.

scholarly journals A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

2020 ◽  
Author(s):  
Tycho Marinus ◽  
Adam B. Fessler ◽  
Craig A. Ogle ◽  
Danny Incarnato
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Critical Role ◽  
Living Cells ◽  
Pathological Process ◽  
Rna Structures ◽  
Rna Molecules ◽  
Derived Data

ABSTRACTDue to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art SHAPE reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

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