The document discusses the development of comparative structure models for 16S and 23S ribosomal RNA (rRNA) over the past 20+ years. It describes how early models from the late 1970s were based on identifying covariations between sequences. As more sequences became available, algorithms improved to identify additional base pairs. The current models are based on analyzing thousands of rRNA sequences and incorporate base pairs with high covariation scores. When evaluated against crystal structures of the 30S and 50S ribosomal subunits, nearly all predicted base pairs were found to be correct.
Lee C.-Y., Lee J.C., and Gutell R.R. (2007).
Networks of interactions in the secondary and tertiary structure of ribosomal RNA.
Physica A, 386(1):564-572.
This document discusses the collection and analysis of small subunit ribosomal RNA structures, specifically 16S and 16S-like rRNA. It provides background on how comparative methods have been used to infer the secondary and tertiary structures of rRNA molecules. It then summarizes the evolution of 16S rRNA secondary structure models, from early minimal structures to current models incorporating tertiary interactions. The objectives of the annual structure collection are outlined, including presenting updated E. coli 16S rRNA structure, additional tertiary interactions, and a sampling of structures from diverse phylogenetic domains. Examples of higher-order structure diagrams are provided for E. coli, yeast, and C. elegans rRNA.
Muralidhara C., Gross A.M., Gutell R.R., and Alter O. (2011).
Tensor Decomposition Reveals Concurrent Evolutionary Convergences and Divergences and Correlations with Structural Motifs in Ribosomal RNA.
PLoS ONE, 6(4):e18768.
This document summarizes a compilation of large subunit (23S and 23S-like) ribosomal RNA secondary structures from a diverse set of organisms, including archaea, bacteria, plastids, and mitochondria. It provides tables tracking the growth of sequenced large subunit rRNAs over time. It describes the process of determining secondary and higher-order structures through comparative analysis. The structures are made available both as hard copies and online via FTP. The document requests feedback to improve the accuracy and coverage of the compiled structures.
This document summarizes a compilation of large subunit (23S-like) ribosomal RNA sequences presented in a secondary structure format. It includes 71 rRNA sequences from archaebacteria, eubacteria, eukaryotic organelles (plastids and mitochondria), and eukaryotic cytoplasm. The compilation is intended to facilitate comparison of homologous structural features among different rRNA sequences by configuring them according to the E. coli 23S rRNA model structure. The sequences were grouped by phylogenetic domain and references for each sequence were provided. Access to the secondary structure figures was described as available either through hard copy or online files.
Este documento resume o livro "A Heresia Humanista – Ensaio Sobre as Paixões de Fim de Século", de José Fernando Tavares, em três frases:
1) O livro analisa como o homem ocidental desenvolveu um interesse pelo fantástico através da literatura e do cinema, explorando temas como monstros e entidades demoníacas.
2) A obra critica o desinteresse contemporâneo pela cultura erudita e a preferência por manifestações culturais medíocres, vendo isso como uma "traição do humanismo
Lee C.-Y., Lee J.C., and Gutell R.R. (2007).
Networks of interactions in the secondary and tertiary structure of ribosomal RNA.
Physica A, 386(1):564-572.
This document discusses the collection and analysis of small subunit ribosomal RNA structures, specifically 16S and 16S-like rRNA. It provides background on how comparative methods have been used to infer the secondary and tertiary structures of rRNA molecules. It then summarizes the evolution of 16S rRNA secondary structure models, from early minimal structures to current models incorporating tertiary interactions. The objectives of the annual structure collection are outlined, including presenting updated E. coli 16S rRNA structure, additional tertiary interactions, and a sampling of structures from diverse phylogenetic domains. Examples of higher-order structure diagrams are provided for E. coli, yeast, and C. elegans rRNA.
Muralidhara C., Gross A.M., Gutell R.R., and Alter O. (2011).
Tensor Decomposition Reveals Concurrent Evolutionary Convergences and Divergences and Correlations with Structural Motifs in Ribosomal RNA.
PLoS ONE, 6(4):e18768.
This document summarizes a compilation of large subunit (23S and 23S-like) ribosomal RNA secondary structures from a diverse set of organisms, including archaea, bacteria, plastids, and mitochondria. It provides tables tracking the growth of sequenced large subunit rRNAs over time. It describes the process of determining secondary and higher-order structures through comparative analysis. The structures are made available both as hard copies and online via FTP. The document requests feedback to improve the accuracy and coverage of the compiled structures.
This document summarizes a compilation of large subunit (23S-like) ribosomal RNA sequences presented in a secondary structure format. It includes 71 rRNA sequences from archaebacteria, eubacteria, eukaryotic organelles (plastids and mitochondria), and eukaryotic cytoplasm. The compilation is intended to facilitate comparison of homologous structural features among different rRNA sequences by configuring them according to the E. coli 23S rRNA model structure. The sequences were grouped by phylogenetic domain and references for each sequence were provided. Access to the secondary structure figures was described as available either through hard copy or online files.
Este documento resume o livro "A Heresia Humanista – Ensaio Sobre as Paixões de Fim de Século", de José Fernando Tavares, em três frases:
1) O livro analisa como o homem ocidental desenvolveu um interesse pelo fantástico através da literatura e do cinema, explorando temas como monstros e entidades demoníacas.
2) A obra critica o desinteresse contemporâneo pela cultura erudita e a preferência por manifestações culturais medíocres, vendo isso como uma "traição do humanismo
El documento proporciona información sobre diferentes materiales que pueden reciclarse como vidrio, metales, plásticos, pilas, computadoras, tetra pak, papel y cartón, y CDs/DVDs. Explica que estos materiales pueden reciclarse y volver a usarse para crear nuevos productos, lo que ayuda a reducir la contaminación y proteger el medio ambiente.
This document outlines the structure of a Blackberry mobile application, including the homepage, mypages 1 through 3, and repeated sections for the Blackberry App name, Dhanyawaad title, and mypages. The document structure is duplicated three times with homepage sections 1 and 2.
Este documento presenta un resumen de 3 oraciones o menos del siguiente texto: Un estudiante de quinto grado de la sección B presenta un trabajo de computación e informática para su profesor Carlos Choque en Arequipa, Perú en el año 2013.
El documento describe los diferentes factores que influyen en la administración de una organización. Estos incluyen factores específicos como proveedores, clientes y competidores, así como factores generales en el ambiente como condiciones económicas, disponibilidad de capital y mano de obra, niveles de precios, tendencias tecnológicas, fuerzas sociales, políticas y éticas.
O documento descreve as características do avestruz, a maior ave viva. Ele pode alcançar até 2,4 metros de altura e 150 kg, tem asas atrofiadas mas pode correr até 120 km/h. O avestruz é onívoro, põe de 40 a 100 ovos por ano e vive em média 50 anos. Suas penas, couro e carne são produtos comercializados.
La cleptomanía es un trastorno mental grave caracterizado por impulsos irresistibles de robar objetos de poco valor que generalmente no se necesitan, causando placer durante el acto. Los síntomas incluyen robar por el fuerte impulso sin poder resistirse. Aunque la causa exacta se desconoce, se cree que cambios en el cerebro y factores como antecedentes familiares o traumas pueden ser factores de riesgo. El tratamiento con medicamentos o terapia puede ayudar a controlar los impulsos de robar compulsivamente.
Este documento presenta la tercera semana de un curso de operación de retroexcavadoras. Explica las partes principales de una retroexcavadora y los controles de operación. El objetivo general del curso es desarrollar habilidades para interpretar adecuadamente los diferentes niveles de falla y simbología de advertencia en maquinaria pesada. Los objetivos específicos incluyen leer e interpretar la simbología de equipos de carga y transporte, definir entre fallas de sistema, errores operacionales o emergencias, y proceder de
Quitapenas es una planta medicinal que se utiliza para tratar diversos problemas de salud. Contiene compuestos que pueden aliviar el dolor y la inflamación. Se usa comúnmente para tratar dolores musculares, artritis, estrés y problemas digestivos.
This document summarizes the Comparative RNA Web (CRW) Site, an online database that provides comparative sequence and structure information for ribosomal RNAs, transfer RNA, and other RNAs. The CRW Site contains:
1) Current comparative structure models for rRNA and other RNAs derived from extensive sequence analysis and alignment.
2) Nucleotide frequency and conservation data mapped onto phylogenetic trees to show variation and conservation across organisms.
3) A collection of RNA sequences and secondary structure models from diverse organisms.
4) Tools for accessing and searching the database of RNA sequences, structures, and associated metadata.
This document discusses the principles of rRNA structure that have been learned through comparative sequence analysis of rRNA genes. It begins by introducing rRNA and the importance of understanding its structure. It then discusses how comparative sequence analysis works and how large datasets allow detection of secondary and tertiary interactions. Several non-canonical base pairing interactions and structural motifs have been discovered this way. The document provides examples of covariations found that support structural interactions and discusses how the evidence standards have evolved as more data became available. It aims to review what has been learned about rRNA architecture and design principles from this comparative approach.
Gutell 053.book r rna.1996.dahlberg.zimmermann.p111-128.ocrRobin Gutell
This document summarizes the current models of secondary and tertiary structure for 16S and 23S rRNA that have been inferred through comparative sequence analysis. It discusses the principles and methodology of comparative sequence analysis, including how compensatory base changes provide evidence for base pairing. The models have been refined over time as more rRNA sequences became available and computational methods for identifying covariations improved. While some base pairings have been eliminated or added, the overall secondary structures have remained largely the same. Figures 1A and 1B show the current secondary and tertiary structure models for E. coli 16S and 23S rRNA.
This document evaluates the suitability of using free energy minimization and nearest-neighbor energy parameters to predict RNA secondary structure from sequence data alone. It compares RNA secondary structure predictions made by the Mfold 3.1 program to structures determined by comparative analysis for over 1,400 RNA sequences, including rRNAs, tRNAs, and 5S rRNAs. The results show that while Mfold 3.1 predicts shorter RNA structures like tRNAs and 5S rRNAs reasonably well, it is unable to consistently and reliably predict the correct secondary structure of larger rRNAs like 16S and 23S rRNAs. On average, Mfold 3.1 predicts 16S and 23S rRNA structures with only about 40% accuracy. The study finds
This document discusses the identification of a potential base triple in 16S rRNA through comparative sequence analysis and molecular modeling. Comparative sequence analysis of over 5,000 prokaryotic 16S rRNA sequences identified strong covariation between position 121 and the base pairs at positions 124:237 and 125:236, suggesting one of two potential base triples: U121(C124:G237) or U121(U125:A236). Molecular modeling provided additional evidence that U121 interacts with C124 in the U121(C124:G237) configuration, which is consistent with chemical reactivity data and allows for similar structures in the three most common sequence motifs observed, comprising over 90% of bacterial and archaeal sequences. This
This study analyzed the conserved A:A and A:G base pairs found at the ends of helices in 16S and 23S rRNA. It found that 30% of helix ends in 16S rRNA and 28% in 23S rRNA have an A:A or A:G pair in at least 90% of bacterial sequences, far more than expected by chance. Most A:G pairs have the guanine on the 3' side of the helix. These non-canonical base pairs are found in a variety of structural contexts and may be important for structural rearrangements associated with RNA function.
This document summarizes Robin Gutell's review of using comparative analysis of RNA sequences to infer higher-order RNA structure. It discusses how evolutionary principles encoded in nucleic acid sequences can reveal RNA structure and function. Specifically, it describes how comparing tRNA, 5S rRNA, 16S rRNA and 23S rRNA sequences from different organisms led to models for their secondary and tertiary structures, which were later supported by experimental data. The methodology involved identifying conserved sequence elements and base pairing patterns common across organisms to deduce structural components.
El documento proporciona información sobre diferentes materiales que pueden reciclarse como vidrio, metales, plásticos, pilas, computadoras, tetra pak, papel y cartón, y CDs/DVDs. Explica que estos materiales pueden reciclarse y volver a usarse para crear nuevos productos, lo que ayuda a reducir la contaminación y proteger el medio ambiente.
This document outlines the structure of a Blackberry mobile application, including the homepage, mypages 1 through 3, and repeated sections for the Blackberry App name, Dhanyawaad title, and mypages. The document structure is duplicated three times with homepage sections 1 and 2.
Este documento presenta un resumen de 3 oraciones o menos del siguiente texto: Un estudiante de quinto grado de la sección B presenta un trabajo de computación e informática para su profesor Carlos Choque en Arequipa, Perú en el año 2013.
El documento describe los diferentes factores que influyen en la administración de una organización. Estos incluyen factores específicos como proveedores, clientes y competidores, así como factores generales en el ambiente como condiciones económicas, disponibilidad de capital y mano de obra, niveles de precios, tendencias tecnológicas, fuerzas sociales, políticas y éticas.
O documento descreve as características do avestruz, a maior ave viva. Ele pode alcançar até 2,4 metros de altura e 150 kg, tem asas atrofiadas mas pode correr até 120 km/h. O avestruz é onívoro, põe de 40 a 100 ovos por ano e vive em média 50 anos. Suas penas, couro e carne são produtos comercializados.
La cleptomanía es un trastorno mental grave caracterizado por impulsos irresistibles de robar objetos de poco valor que generalmente no se necesitan, causando placer durante el acto. Los síntomas incluyen robar por el fuerte impulso sin poder resistirse. Aunque la causa exacta se desconoce, se cree que cambios en el cerebro y factores como antecedentes familiares o traumas pueden ser factores de riesgo. El tratamiento con medicamentos o terapia puede ayudar a controlar los impulsos de robar compulsivamente.
Este documento presenta la tercera semana de un curso de operación de retroexcavadoras. Explica las partes principales de una retroexcavadora y los controles de operación. El objetivo general del curso es desarrollar habilidades para interpretar adecuadamente los diferentes niveles de falla y simbología de advertencia en maquinaria pesada. Los objetivos específicos incluyen leer e interpretar la simbología de equipos de carga y transporte, definir entre fallas de sistema, errores operacionales o emergencias, y proceder de
Quitapenas es una planta medicinal que se utiliza para tratar diversos problemas de salud. Contiene compuestos que pueden aliviar el dolor y la inflamación. Se usa comúnmente para tratar dolores musculares, artritis, estrés y problemas digestivos.
This document summarizes the Comparative RNA Web (CRW) Site, an online database that provides comparative sequence and structure information for ribosomal RNAs, transfer RNA, and other RNAs. The CRW Site contains:
1) Current comparative structure models for rRNA and other RNAs derived from extensive sequence analysis and alignment.
2) Nucleotide frequency and conservation data mapped onto phylogenetic trees to show variation and conservation across organisms.
3) A collection of RNA sequences and secondary structure models from diverse organisms.
4) Tools for accessing and searching the database of RNA sequences, structures, and associated metadata.
This document discusses the principles of rRNA structure that have been learned through comparative sequence analysis of rRNA genes. It begins by introducing rRNA and the importance of understanding its structure. It then discusses how comparative sequence analysis works and how large datasets allow detection of secondary and tertiary interactions. Several non-canonical base pairing interactions and structural motifs have been discovered this way. The document provides examples of covariations found that support structural interactions and discusses how the evidence standards have evolved as more data became available. It aims to review what has been learned about rRNA architecture and design principles from this comparative approach.
Gutell 053.book r rna.1996.dahlberg.zimmermann.p111-128.ocrRobin Gutell
This document summarizes the current models of secondary and tertiary structure for 16S and 23S rRNA that have been inferred through comparative sequence analysis. It discusses the principles and methodology of comparative sequence analysis, including how compensatory base changes provide evidence for base pairing. The models have been refined over time as more rRNA sequences became available and computational methods for identifying covariations improved. While some base pairings have been eliminated or added, the overall secondary structures have remained largely the same. Figures 1A and 1B show the current secondary and tertiary structure models for E. coli 16S and 23S rRNA.
This document evaluates the suitability of using free energy minimization and nearest-neighbor energy parameters to predict RNA secondary structure from sequence data alone. It compares RNA secondary structure predictions made by the Mfold 3.1 program to structures determined by comparative analysis for over 1,400 RNA sequences, including rRNAs, tRNAs, and 5S rRNAs. The results show that while Mfold 3.1 predicts shorter RNA structures like tRNAs and 5S rRNAs reasonably well, it is unable to consistently and reliably predict the correct secondary structure of larger rRNAs like 16S and 23S rRNAs. On average, Mfold 3.1 predicts 16S and 23S rRNA structures with only about 40% accuracy. The study finds
This document discusses the identification of a potential base triple in 16S rRNA through comparative sequence analysis and molecular modeling. Comparative sequence analysis of over 5,000 prokaryotic 16S rRNA sequences identified strong covariation between position 121 and the base pairs at positions 124:237 and 125:236, suggesting one of two potential base triples: U121(C124:G237) or U121(U125:A236). Molecular modeling provided additional evidence that U121 interacts with C124 in the U121(C124:G237) configuration, which is consistent with chemical reactivity data and allows for similar structures in the three most common sequence motifs observed, comprising over 90% of bacterial and archaeal sequences. This
This study analyzed the conserved A:A and A:G base pairs found at the ends of helices in 16S and 23S rRNA. It found that 30% of helix ends in 16S rRNA and 28% in 23S rRNA have an A:A or A:G pair in at least 90% of bacterial sequences, far more than expected by chance. Most A:G pairs have the guanine on the 3' side of the helix. These non-canonical base pairs are found in a variety of structural contexts and may be important for structural rearrangements associated with RNA function.
This document summarizes Robin Gutell's review of using comparative analysis of RNA sequences to infer higher-order RNA structure. It discusses how evolutionary principles encoded in nucleic acid sequences can reveal RNA structure and function. Specifically, it describes how comparing tRNA, 5S rRNA, 16S rRNA and 23S rRNA sequences from different organisms led to models for their secondary and tertiary structures, which were later supported by experimental data. The methodology involved identifying conserved sequence elements and base pairing patterns common across organisms to deduce structural components.
Wu J.C., Gardner D.P., Ozer S., Gutell R.R. and Ren P. (2009).
Correlation of RNA Secondary Structure Statistics with Thermodynamic Stability and Applications to Folding.
Journal of Molecular Biology, 391(4):769-783.
The document analyzes the folding of 72 23S rRNA sequences using the thermodynamic folding method of Zuker and Turner. It finds that on average, the method correctly predicts 44% of canonical base pairs in the secondary structures, similar to previous results for 16S rRNA sequences. Certain properties of the sequences correlate with higher or lower prediction scores, including the percentage of noncanonical base pairs, stable hairpin loops, and G+C content. The results provide insights into using thermodynamic folding to predict the structure of large RNA molecules like rRNA.
This summary analyzes a document describing methods for identifying constraints on the higher-order structure of RNA using comparative sequence analysis. The document presents new, more rigorous comparative analysis protocols developed by the authors to analyze RNA sequence datasets, including tRNA, 16S rRNA and 23S rRNA sequences. The initial results from applying these new protocols to various RNA datasets are encouraging, substantiating previous structural proposals and beginning to reveal additional constraints on the higher-order structure of these RNA molecules.
This document summarizes the results of a comprehensive comparison of secondary structure models for the cytoplasmic large subunit (23S-like) rRNA of eukaryotes. It finds that while eukaryotic 23S-like rRNAs range greatly in length, they share a common conserved core secondary structure with a few distinct differences from prokaryotes. Variable regions outside the core accommodate size variation between species. Newly proposed or refined secondary structures were defined for many variable regions based on comparative evidence, improving structural models across eukaryotes.
This document analyzes the reliability of predicting RNA secondary structure using statistical mechanics. It summarizes:
1) The authors analyzed base-pairing probability distributions (BPPDs) of ribosomal RNAs from different phylogenetic groups. They found that bases with lower Shannon entropy (S) values in their BPPDs had a higher probability of being correctly predicted by minimum free energy structure folding.
2) BPPDs of thermophilic prokaryotes had lower average S values than mesophilic/psychrophilic prokaryotes, reflecting an adaptation to higher temperatures. Among phylogenetic groups, Archaea had the lowest S values followed by Bacteria, with chloroplasts, mitochondria and Eukary
Gutell R.R. (2013).
Comparative Analysis of the Higher-Order Structure of RNA.
in: Biophysics of RNA Folding. Volume editor: Rick Russell. Series title: Biophysics for the Life Sciences. Series editors: Norma Allewell, Ivan Rayment, Bertrand Garcia-Moreno, Jonathan Dinman, and Michael McCarthy. pp. 11-22. Publisher: Springer, New York, NY.
This document describes the lonepair triloop (LPTL), a new RNA structural motif identified through comparative sequence analysis. The LPTL contains a single base pair ("lonepair") capped by a hairpin loop of three nucleotides. Analysis of ribosomal crystal structures validated seven previously predicted LPTLs and identified 16 additional examples. In total, 24 LPTLs were found in ribosomal RNAs and tRNAs. These LPTLs fall into different classes and groups based on their structure and tertiary interactions. At least one nucleotide in the triloop forms tertiary interactions in most examples, demonstrating the three-dimensional functional role of this motif.
Gardner D.P., Ren P., Ozer S., and Gutell R.R. (2011).
Statistical Potentials for Hairpin and Internal Loops Improve the Accuracy of the Predicted RNA Structure.
Journal of Molecular Biology, 413(2):473-483.2011. pp 15-22.
Lee J.C., Gutell R.R., and Russell R. (2006).
The UAA/GAN internal loop motif: a new RNA structural element that forms a cross-strand AAA stack and long-range tertiary interactions.
Journal of Molecular Biology, 360(5):978-988.
This document summarizes the 1993 compilation of large subunit (23S and 23S-like) ribosomal RNA structures. It provides an overview of the growth of the database, which saw the largest annual increase in sequences that year. It also describes models of higher-order structure for bacterial, yeast, and nematode mitochondrial rRNA. The accuracy of the data and availability of the structure figures are discussed.
This document provides secondary structure diagrams for large subunit ribosomal RNA sequences from a variety of organisms. It summarizes 40 complete rRNA sequences and 5 partial sequences published as of 1987. The diagrams are presented in a standardized format based on Escherichia coli rRNA secondary structure. Many regions of unknown structure are indicated. The structures were determined using comparative sequence analysis to identify compensating base changes maintaining Watson-Crick base pairing across evolutionary distances. Limitations of this approach include requiring sequences that contain the structural feature and sufficient sequence variation within those sequences to determine the structure.
This document summarizes Carl Woese's contributions to science, particularly his discovery of the third domain of life (Archaea) through analysis of rRNA sequences. It describes how his work established the use of comparative analysis to determine rRNA secondary structure and identify structural motifs. It highlights that he envisioned comparative analysis providing details about RNA structure and energetics. The summary discusses Woese's seminal concepts regarding the need for a universal phylogenetic framework and how analysis of rRNA satisfied criteria to reconstruct evolutionary relationships across all life.
Gutell 123.app environ micro_2013_79_1803Robin Gutell
This document summarizes a study examining the host specificity of Lactobacillus bacteria associated with different hymenopteran (bee and ant) hosts. The researchers compiled nearly full-length 16S rRNA gene sequences of Lactobacillus from public databases and used these to construct phylogenetic trees. They also included shorter 16S sequences from surveys of bacteria associated with sweat bees, fungus-growing ants, and fire ants. The results showed that lactobacilli associated with honey bees and bumble bees are highly host specific, while sweat bees and ants associate with lactobacilli more closely related to those found in diverse environments or vertebrate hosts. The high host specificity seen in corbiculate bees (honey bees
Gardner D.P., Xu W., Miranker D.P., Ozer S., Cannone J.J., and Gutell R.R. (2012).
An Accurate Scalable Template-based Alignment Algorithm.
Proceedings of 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012), Philadelphia, PA. October 4-7, 2012. IEEE Computer Society, Washington, DC, USA. pp. 237-243.
Lee J.C. and Gutell R.R. (2012).
A Comparison of the Crystal Structures of the Eukaryotic and Bacterial SSU Ribosomal RNAs Reveals Common Structural Features in the Hypervariable Regions.
PLoS ONE, 7(5):e38203.
Ozer S., Doshi K.J., Xu W., and Gutell R.R. (2011).
rCAD: A Novel Database Schema for the Comparative Analysis of RNA.
7th IEEE International Conference on e-Science, Stockholm, Sweden. December 5-8, 2011. pp 15-22.
Jiang Y., Xu W., Thompson L.P., Gutell R., and Miranker D. (2011).
R-PASS: A Fast Structure-based RNA Sequence Alignment Algorithm.
Proceedings of 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2011), Atlanta, GA. November 12-15, 2011. IEEE Computer Society, Washington, DC, USA. pp. 618-622.
Xu W., Wongsa A., Lee J., Shang L., Cannone J.J., and Gutell R.R. (2011).
RNA2DMap: A Visual Exploration Tool of the Information in RNA's Higher-Order Structure.
Proceedings of 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2011), Atlanta, GA. November 12-15, 2011. IEEE Computer Society, Washington, DC, USA. pp. 613-617.
Xia Z., Gardner D.P., Gutell R.R., and Ren P. (2010).
Coarse-Grained Model for Simulation of RNA Three-Dimensional Structures.
The Journal of Physical Chemistry B, 114(42):13497-13506.
The document describes research on fragmentation of the large subunit ribosomal RNA (LSU rRNA) gene in oyster mitochondrial genomes. Key findings include:
1) The LSU rRNA gene is split into two fragments separated by thousands of nucleotides in three species of oysters.
2) RT-PCR and EST analysis showed the two fragments are transcribed separately in Crassostrea virginica and are not spliced together.
3) Secondary structure models of the fragmented LSU rRNA genes were predicted for C. virginica, C. gigas, and C. hongkongensis based on comparative sequence analysis. This fragmentation represents a novel phenomenon in bilateral metazoan mitochondrial genomes.
Mueller U.G., Ishak H., Lee J.C., Sen R., and Gutell R.R. (2010).
Placement of attine ant-associated Pseudonocardia in a global phylogeny (Pseudonocardiaceae, Actinomycetales): a test of two symbiont-association models.
Antonie van Leeuwenhoek International Journal of General and Molecular Microbiology, 98(2):195-212.
Theriot E.C., Cannone J.J., Gutell R.R., and Alverson A.J. (2009).
The limits of nuclear encoded SSU rDNA for resolving the diatom phylogeny.
European Journal of Phycology, 44(3):277-290.
Xu W., Ozer S., and Gutell R.R. (2009).
Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA.
21st International Conference on Scientific and Statistical Database Management. June 2-4, 2009. Springer-Verlag. pp. 200-216.
Chen Y.P., Evans J.D., Murphy C., Gutell R., Zuker M., Gundersen-Rindal D., and Pettis J.S. (2009).
Morphological, Molecular, and Phylogenetic Characterization of Nosema cerenae, a Microsporidian Parasite Isolated from the European Honey Bee, Apis mellifera.
The Journal of Eukaryotic Microbiology, 56(2):142-147.
Maddison D.R., Moore W., Baker M.D., Ellis T.M., Ober K.A., Cannone J.J., and Gutell R.R. (2009).
Monophyly of terrestrial adephagan beetles as indicated by three nuclear genes (Coleoptera: Carabidae and Trachypachidae).
Zoologica Scripta, 38(1):43-62.
The document discusses the origin and evolution of the ribosome. It finds:
1) There is no single self-folding RNA segment that defines the small subunit's decoding site, while the large subunit's peptidyl transfer center is defined by one self-folding RNA segment.
2) The proteins contacting the small subunit's decoding site use universally alignable sequence blocks, while the large subunit's contact proteins use bacterial- or archaeal-specific blocks.
3) These differences support an earlier origin for the large subunit's peptidyl transfer center, with the small subunit's decoding site evolving later as an addition to the ribosome. The implications are that a single self-folding
Chandramouli P., Topf M., Ménétret J.-F., Eswar N., Cannone J.J., Gutell R.R., Sali A., and Akey C.W. (2008).
Structure of the Mammalian 80S Ribosome at 8.7 Å Resolution.
Structure, 16(4):535-548.
This document describes a new method called BlockMSA for performing local multiple sequence alignment (MSA) of non-coding RNA sequences. BlockMSA uses a biclustering approach that simultaneously clusters sequences and identifies conserved subsequences within the clusters. The authors test BlockMSA on benchmark RNA datasets and two large biological datasets, finding it outperforms other MSA tools for larger problems with highly variable sequences. BlockMSA is able to scale to larger datasets while identifying functionally conserved regions missed by other methods.
Gillespie J.J., Johnston J.S., Cannone J.J., and Gutell R.R. (2006).
Characteristics of the nuclear (18S, 5.8S, 28S and 5S) and mitochondrial (12S and 16S) rRNA genes of Apis mellifera (Insecta:Hymenoptera): structure, organization, and retrotransposable elements.
Insect Molecular Biology, 15(5):657-686.
Weinstock et al. (81 authors), Gillespie J.J., Cannone J.J., Gutell R.R., et al. (100 authors) (2006).
Insights into social insects from the genome of the honeybee Apis mellifera.
Nature, 443(7114):931-949.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
2. comparative analysis, is based on a very simple and
profound principle. This method has been utilized to predict
the secondary structure and the early stages of the tertiary
structure of several RNA molecules, including the rRNAs.
In addition to these structure predictions, the comparative
approach has also revealed new information about RNA
structural motifs and other principles of RNA structure.
Inferring higher-order structure from patterns
of sequence variation
Shortly after the first tRNA sequence was determined [7],
it was rationalized from a comparative perspective that all
tRNA sequences should have equivalent secondary and
tertiary structures to allow them to interact with the same
binding sites on the ribosome and with the same set of
proteins and RNAs during protein synthesis. Two basic
principles form the foundation for the comparative analysis
of RNA structure: firstly, different RNA sequences can
fold into the same secondary and tertiary structures and,
secondly, the unique structure and function of an RNA
molecule is maintained through the evolutionary process
of mutation and selection. We utilized this comparative
paradigm for the prediction of the 16S and 23S rRNA
structures. We assumed that all 16S (and 16S-like) and 23S
(and 23S-like) rRNAs have the same general secondary and
tertiary structures, regardless of the extent of conservation
and variation among the sequences. The correct helices
that have been identified using comparative analysis are
present in the same homologous region of the rRNAs and
have variation in the composition of the sequences, whilst
maintaining G•C, A•U and G•U base pairs. Initially, we
identified base-paired positions within a potential helix that
have ‘covariation’ (similar patterns of variation) in a set of
sequences aligned for maximum sequence identity [8–10].
Proposed helices with two or more covariations were
considered ‘proven’. Versions of the 16S and 23S rRNA
structure models from the early 1980s (Santa Cruz/Urbana
versions) are shown in Figure 1. The majority of the helices
in these early structure models had at least one covariation
per helix. We considered this model to be the minimal
structure, that is, there were areas that were incomplete.
Two other sets of 16S and 23S rRNA structure models
were determined independently with comparative methods
[11–14], whereas another set of model diagrams was adapted
in full from previously proposed structure models [15–17].
Subsequently, as the number of sequences in our 16S and
23S rRNA alignments surpassed 25, we developed different
algorithms and computer programs to identify positions in
an alignment that have similar patterns of variation [18–20].
Given this series of improvements in the covariation
algorithms, coupled with very dramatic increases in the
302 Nucleic acids
Figure 1
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(a) (b) (c)
Current Opinion in Structural Biology
The original (1980–81) Noller-Woese-Gutell comparative structure
models for the 16S and 23S rRNAs. (a) 16S rRNA (adapted from
[8]). (b) 23S rRNA, 5′ half (adapted from [9]). (c) 23S rRNA, 3′ half
(adapted from [9]). E. coli (GenBank accession number J01695) is
used as the reference sequence. Each of these models has been
superimposed onto the corresponding current model diagrams to
highlight the similarities and differences. Nucleotides are replaced with
colored dots: black, positions that are unchanged between the original
and current models; blue, base pairs present in the original models
but absent from the current models; red, positions that are unpaired in
the original models but are part of a base pair in the current models;
green, positions that are part of one base pair in the original models
but are part of a different base pair in the current models. Full-page
versions of each panel are available online at
http://www.rna.icmb.utexas.edu/ANALYSIS/COSB2002/ (part of the
CRW site at http://www.rna.icmb.utexas.edu/).
3. number and diversity of rRNA sequences in our sequence
collection, we were able to identify more positions with
similar patterns of variation. Although the early covariation
analysis only identified those covariations that involve A•U
and G•C pairings within a potential helix, our algorithms
have, for the past ten years, identified all positional
covariations, regardless of base pair type and their types of
interchanges with other base pairs (e.g. U•U ↔ C•C,
A•A ↔ G•G, U•U ↔ G•G), and independent of the spatial
relationship with other base pairings and structural elements
[21]. Consequently, we began identifying single base pairings
not flanked by other base pairings, noncanonical base pairs
and other types of tertiary interactions (see below). In
addition to the inclusion of newly identified base pairs,
previously proposed base pairs were removed from the
structure models when the ratio of covariation to variation
dropped with increasing numbers of sequences.
To gauge the extent of positional covariation and our
confidence in the accuracy of each of these proposed base
pairs, we established a quantitative scoring method.
Higher scores reflect a greater extent of pure covariation
(simultaneous changes at both of the paired positions),
larger numbers of exchanges between a set of base pair
types that covary with one another (e.g. A•U ↔ G•C)
and/or a larger number of mutual changes or covariations
that occur during the evolution of the RNA (also called
phylogenetic events). These three parameters can,
individually or collectively, influence our confidence in a
putative base pair. For example, we were more confident
in the authenticity of the 570•866 base pair in 16S rRNA
because of several phylogenetic events within the bacteria,
archaea and eucarya [22]. These 16S and 23S rRNA
covariation-based structure models only contain those base
pairs with positional covariation or G•C, A•U or G•U base
pairs that are within a regular helix and present in more
than 80% of the sequences.
The most recent comparative structure models for 16S and
23S rRNA are shown in Figure 2 and are based on the
analysis of approximately 7000 16S and 1050 23S rRNA
sequences [21,23]. These two structure models are the
culmination of 20 years of comparative analysis (see
below). The base pair symbols are color coded to reveal our
confidence in the authenticity of that base pair; base pairs
with the highest covariation scores are shown in red,
followed by green and black. Base pairs with gray symbols
are conserved in more than 98% of the sequences, whereas
Ribosomal RNA comparative structure models Gutell, Lee and Cannone 303
Figure 2
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(a) (b) (c)
Current Opinion in Structural Biology
The current Noller-Woese-Gutell comparative structure models for the
16S and 23S rRNAs. (a) 16S rRNA. (b) 23S rRNA, 5′ half. (c) 23S
rRNA, 3′ half. E. coli (GenBank accession number J01695) is used as
the reference sequence. Nucleotides are replaced with colored dots
that represent confidence in the base pair: red, high covariation scores;
green, lower but significant covariation scores occurring within a
standard helix containing a red base pair; black, even lower covariation
scores occurring within a standard helix containing a red base pair;
gray, conserved in more than 98% of the sequences occurring within
a standard helix containing a red base pair; blue, do not have a significant
amount of pure covariation and do not occur within a standard helix (see
[23] for additional details). Base pair symbols indicate the type of base
pair: line, canonical base pair; small closed circle, G•U base pair; large
open circle, G•A base pair; large closed circle, other noncanonical
base pairs. Nucleotides involved in tertiary interactions (including
pseudoknots) are boxed and connected with lines. Diagrams adapted
from [23]. Full-page versions of each panel are available online at the
CRW site (http://www.rna.icmb.utexas.edu/ANALYSIS/COSB2002/).
4. blue base pairs do not have a significant amount of pure
covariation and do not occur within a standard helix
(see [23] for more details). As the majority of the base pairs
have red symbols, we believe that nearly all of the base
pairs in the current 16S and 23S rRNA covariation-based
structure models are correct (see below).
The evolution of the 16S and 23S rRNA covariation-based
structure models is shown graphically in Figure 1 and
quantitatively in Table 1. To allow easy comparison with the
current models, the original 1980–81 16S and 23S rRNA
structure models were redrawn using the current models as
a template (Figure 1). Base pairs that are present in both the
original and current models are shown in black, and those
that are different in the original structure models and the
most recent covariation-based structure models are illustrated
in blue, red and green. Blue base pair symbols indicate base
pairs in the original models that are absent from the current
models, red nucleotides are unpaired in the original models
and paired in the current models, and green nucleotides are
part of different base pairs in the two structure models.
In 1980–81, the 16S and 23S rRNA structure models were
based on just two complete rRNA sequences per structure;
at the end of 1999, this work culminated with the analysis of
approximately 7000 16S and 1050 23S rRNA sequences.
These structure models evolved over nearly 20 years as the
collection of sequences grew and our methods to identify
and score covariations were developed and refined. To assess
the changes, the original 1980–81 structure models were
compared with the current 1999 structure models (Table 1,
adapted from Section 1b on the ‘Comparative RNA Web’
[CRW] site and database; http://www.rna.icmb.utexas.edu).
We draw four significant conclusions from this analysis.
Firstly, nearly 60% of the base pairs in the current 16S
rRNA structure model were predicted from the analysis
of two sequences for the original structure model; nearly
78% of the current 23S rRNA base pairings were predicted
from the original structure model. Secondly, in contrast,
approximately 80% of the original 16S and 87% of the
original 23S rRNA base pairs proposed in 1980–81 are
present in the current models. Thirdly, approximately 70
16S and 100 23S initial base pairs have been removed from
the original rRNA structure models. Finally, the number of
unusual, tertiary and tertiary-like base pairings that are pre-
dicted with confidence increases in parallel with increases
in the number and diversity of rRNA sequences studied
and with improvements in the covariation algorithms. In
conclusion, the major components of the 16S and 23S
rRNA structure models were predicted correctly from the
analysis of just a few 16S and 23S rRNA sequences that are
approximately 75% similar to one another. Thousands of
additional rRNA sequences with significant degrees of
similarity and diversity with one another were subsequently
analyzed with covariation analysis to refine the secondary
structure models, to begin to identify tertiary base pairs and
to establish a system to measure the extent of covariation at
all of the proposed base pairs. Beyond the prediction of
base pairs with covariation analysis, the comparative
sequence and structure data are encrypted with fundamental
principles of RNA structure and archaeological markers
that indicate the ancestry of that RNA sequence [24].
Our next task is to decipher these ‘treasures’ from the
comparative RNA sequence and structure data sets. To
this end, we have established the CRW site and database
([23]; http://www.rna.icmb.utexas.edu/) to organize, analyze
and disseminate comparative data for the 5S, 16S (and
16S-like) and 23S (and 23S-like) rRNAs, group I and II
introns, and tRNAs. The main types of information and
data available online for each of these RNAs are: the current
comparative RNA structure model; nucleotide and base
pair frequency tables for all positions in the reference
structures; secondary structure conservation diagrams that
reveal the extent of conservation of the RNA sequence
and structure; more than 400 representative secondary
structure diagrams for organisms from groups that span the
phylogenetic tree and reveal the major forms of structural
variation; nearly 12,000 publicly available sequences that
are 90% or more complete; and sequence alignments.
304 Nucleic acids
Table 1
Summary of the evolution of the Noller-Woese-Gutell 16S and 23S rRNA structure models from the first to the most recent
covariation-based structure models (adapted from Table 3a,b in [23]).
Model 16S rRNA 23S rRNA
Date 1980 1999 1981 1999
1. Approximate number of complete sequences 2 7000 2 1050
2. Percentage of 1999 sequences* 0.03 100 0.2 100
3. Number of bp proposed correctly* 284 478 676 870
4. Number of bp proposed incorrectly* 69 0 102 0
5. Total bp in model (3 + 4) 353 478 778 870
6. Percentage of bp in model present in the current model (3 / X)*†
59.4 100 77.7 100
7. Accuracy of proposed bp (3 / 5) 80.5 100 86.9 100
8. Number of bp in current model missing from this model (X – 3)*†
194 0 194 0
9. Number of tertiary bp proposed correctly* 4 40 4 65
10. Percentage of tertiary bp proposed correctly* 10.0 100 6.2 100
11. Number of base triples proposed correctly* 0 6 0 7
12. Percentage of base triples proposed correctly* 0 100 0 100
*Comparisons are made against the current (1999) models. †
X = 478 for 16S rRNA; X= 870 for 23S rRNA. bp, base pairs.
5. This type of comparative data is the foundation for the
subsequent identification and analysis of RNA structural
motifs. Although the patterns of variation at both positions
in many of the base pairs in the RNA structure are similar
and thus should be identified with covariation analysis,
other sets of base pairs do not have similar patterns of
variation at the two interacting positions. Thus, one of the
larger goals of comparative analysis is to predict those base
pairs lacking similar patterns of variation that occur in
several different types of structural elements, as well as
those base pairs with positional covariation that are conserved
among the sequences in that data set. The process of
comparative analysis, then, is to first predict base pairings
with covariation analysis, followed by the identification of
motifs that are composed of unique arrangements of
sequences within specific structural elements. Several
RNA structural motifs have been identified and/or are still
being defined from sequence and structure perspectives.
These motifs include:
1. Unpaired adenosines in the covariation-based structure
model [18,25•].
2. Tetraloops — hairpin loops with four nucleotides that are
composed of specific sequences [26].
3. Tetraloop receptors and other tertiary interactions involving
tetraloops [27–30].
4. Dominant G•U base pairs [31,32].
5. Tandem G•A oppositions [33,34].
6. Base triples [20].
7. Adenosine platforms [25•,35].
8. U-turns [36].
9. E loops (or S turns) [25•,37,38].
10. E-like loops [25•].
11. Cross-strand purine stacks [39].
12. A•A and A•G oppositions/base pairs at the ends of
helices [10,40,41•].
13. Lone pair triloops ([21]; RR Gutell et al., unpublished
data).
14. A-minor motif [42•,43•].
15. Kink-turn [44•].
Crystal structures of the 16S and 23S rRNAs:
the accuracy of the rRNA comparative
structure models
To assess the accuracy of the covariation-based structure
models, the comparative models for tRNA [19,20,45–50],
fragments of 5S rRNA [51], the L11-binding region of
23S rRNA [9,21,23] and the group I intron [52,53] were
compared with the corresponding high-resolution crystal
structures [39,54–58]. Nearly all of the secondary structure
base pairings and a few of the tertiary base pairs observed
in the crystal structure were predicted in the comparative
structure models for all of these RNAs. More recently, the
high-resolution crystal structures of the 30S [59••,60] and
50S [61••] ribosomal subunits were solved, giving us the
opportunity to evaluate the accuracy of our most recent
16S and 23S rRNA structure models. The results were
again affirmative: approximately 97–98% of the base
pairings predicted with covariation analysis (in the final
covariation-based structure models) are indeed present
in the 16S and 23S rRNA crystal structures (Table 2;
RR Gutell et al., unpublished data). The accuracy of the 16S
and 23S rRNA covariation-based structure prediction not
only augments the credibility of the comparative approach,
but it also validates the sequence alignments that have
been initiated, refined and expanded over the past 20 years,
the initial covariation analysis and our subsequent
Ribosomal RNA comparative structure models Gutell, Lee and Cannone 305
Table 2
Comparison of the current comparative structure models and the crystal structures of the 16S and 23S rRNAs*.
16S rRNA†
23S rRNA‡
Total
Predicted base pairs§
Model CB #
461 / 476 / 97% 779 / 797 / 98% 1240 / 1273 / 97%
Tentative CB#
8 / 23 / 35% 18 / 36 / 50% 26 / 59 / 44%
Motif-based¶
45 / 65 / 70% 86 / 122 / 70% 131 / 187 / 70%
Crystal structure interactions¥
+/+ base–base 514 883 1397
–/+ base–base 56 425 481
Total base–base 683 1297 1862
Base–backbone 49 237 286
*A more complete analysis will be presented later (RR Gutell et al., unpublished data). †
T. thermophilus, GenBank accession number M26923,
PDB code 1FJF [59
]. ‡
H. marismortui, GenBank accession number AF034620, PDB code 1JJ2 [61
]. §
Data are shown as approximate
number of base pairs present in the crystal structure / approximate number of predicted base pairs / percentage of predicted base pairs
present in the crystal structure. #
CB, covariation-based. ¶
The motifs analyzed here are AA.AG@helix.ends [41
], tandem GA [33,34], E and
E-like loops [25
], lone pair triloops (RR Gutell et al., unpublished data) and base triples [20]. ¥
Approximate numbers of interactions in the two
ribosomal crystal structures.
6. covariation algorithms and their refinements. In addition
to the final covariation-based structure model, nearly 45%
of the tentative covariation-based base pairs and 70% of
the motif-based base pairs that were predicted are in the
crystal structure (Table 2). In total, about 90% of the base
pairs predicted by comparative analysis are from the
covariation-based analysis and 10% are from the alternative
motif-based analysis ([20,25•,33,34,41•]; RR Gutell et al.,
unpublished data).
The secondary structure diagrams for Thermus thermophilus
16S rRNA and Haloarcula marismortui 23S rRNA are shown
in Figure 3. All of the base–base and base–backbone
interactions in the 30S [59••] and 50S [61••] ribosomal
subunit crystal structures are colored to reflect the initial
identification of each pairing. The three primary categories
are: present in both the comparative model (covariation
and motif analysis) and the crystal structure (+/+), present
in the comparative model but not in the crystal structure
(+/–), and not present in the comparative model but
present in the crystal structure (–/+). The nucleotides and
base pair symbols are colored red for +/+, green for +/–,
blue for –/+ base–base interactions and brown for –/+
base–backbone interactions.
The affirmative base pairs that were predicted using
covariation analysis (see red nucleotides and base pair
symbols in Figure 3) include: essentially all base pairs that are
strictly homologous between the E. coli reference structure
models and the T. thermophilus 16S and H. marismortui 23S
rRNA crystal structures that have a significant amount of
positional covariation; base pairs that are standard
Watson–Crick (G•C and A•U) and G•U base pair
exchanges; base pairs that occur within standard secondary
structure helices (2 base pairs in length) that are nested
(i.e. not a pseudoknot); individual base pairs and helices
306 Nucleic acids
Figure 3
Comparison of the current Noller-Woese-Gutell
comparative structure models for the 16S and
23S rRNAs with the corresponding ribosomal
subunit crystal structures. (a) 16S rRNA
versus the T. thermophilus structure
(GenBank accession number M26923;
PDB code 1FJF; [59••]). (b) 23S rRNA,
5′ half versus the H. marismortui structure
(GenBank accession number AF034620;
PDB code 1JJ2; [61••]). (c) 23S rRNA, 3′ half
versus the H. marismortui structure (GenBank
accession number AF034620; PDB code 1JJ2;
[61••]). Nucleotides are replaced with colored
dots that show the sources of the
interactions: red, present in both the
covariation-based structure model and the
crystal structure; green, present in the
comparative structure and not present in
the crystal structure; blue, not present in
the comparative structure and present in the
crystal structure; magenta, present in the
covariation-based tentatives or motif-based
analysis, and present in the crystal structure;
brown, base–backbone or
backbone–backbone interactions; purple,
positions that are unresolved in the crystal
structure. Colored open circles around
positions show the third nucleotide of base
triples and colored open rectangles show the
base pairs of base triples. Colored open squares
are used for clarity. Full-page versions of each
panel are available online at the CRW site
(http://www.rna.icmb.utexas.edu/ANALYSIS/
COSB2002/).
5’
3’
50
100
150
200
250
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450 500
550
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Current Opinion in Structural Biology
(a)
7. Ribosomal RNA comparative structure models Gutell, Lee and Cannone 307
Figure 3 continued
3’half
5’
3’
5’
3’
5’3’
b
b
a
a
50
100
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CurrentOpinioninStructuralBiology
8. that form pseudoknots, including tertiary interactions;
lone pairs, including those in the lone pair triloop motif
(RR Gutell et al., unpublished data); and noncanonical
base pairs and their exchanges — A•A ↔ G•G, U•U ↔ C•C,
A•G ↔ G•A, A•C ↔ G•U, U•A ↔ G•G, A•C ↔ U•A and
A•G ↔ R•U [21].
Although more than 1250 base pairs predicted with covari-
ation analysis are in the crystal structure, approximately 35
of them are not (see green nucleotides in Figure 3; note
that the green interactions include those predicted with
both covariation analysis and motif-based analysis). The
majority of these +/– proposed covariation-based base pairs
that are absolutely homologous between the E. coli reference
models and the T. thermophilus 16S and H. marismortui 23S
rRNA structures were not predicted with our highest (red)
confidence rating. Instead, there was either no positional
covariation or an insignificant amount of these putative
base pairs; these interactions were included in the structure
model because they form a G•C, A•U or G•U pair in more
than 80% of the sequences and were adjacent to a base pair
with covariation. The majority of these +/– base pairs are
colored black, our lowest covariation confidence rating.
The aberrant base pairs that are truly homologous between
the crystal structure and the E. coli reference structure
have two other important characteristics. First, all of these
putative base pairs occur at the ends of helices and, second,
there is a bias in the types of base pairs that are not predicted
correctly at the ends of helices. The two most frequent
pairing types (in this latter category) are U•G and U•A
(where the U is at the 5′ half of the helix). These putative
base pairs might not occur in the rRNA structure or,
alternatively, they might be dynamic and are paired at
certain stages of protein synthesis and not in the states of
the crystal structures analyzed here. There is a precedent
for conformational changes of the base pairings at the ends
of helices. Positions 1408 and 1493 form an A•A base pair
in the uncomplexed 30S ribosomal subunit (PDB code
1FJF; [59••]), but are not paired when tRNA and mRNA
are complexed to the 30S subunit [62]. We speculate that
other A•A and A•G oppositions/base pairs at the ends of
helices in the 16S and 23S rRNAs might be involved in
conformational changes [41•]. There is also an interesting
anecdote about the putative U•A pairings that are not in
the crystal structure. The orientation of these U•A pairs
would place the conserved, ’unpaired’ adenosine at the
3′ end of the loop, a very common arrangement in the 16S
and 23S rRNAs [25•].
We will not know all of the structural possibilities for these
putative base pairings until we obtain more crystallographic,
NMR or other experimental data for these regions of the
rRNA. Although comparative analysis has predicted
approximately 510 16S and 880 23S rRNA base pairs, an
additional ~170 16S and ~415 23S rRNA base pairs
(base–base) are in the crystal structure that were not
predicted with comparative methods. Essentially, none of
these ‘–/+’ base pairs has a significant amount of positional
covariation and thus could not be predicted with covariation
analysis. In general, these ‘–/+’ base pairs comprise
noncanonical base pairs that are not associated with
standard helices that were predicted with covariation
analysis. A more detailed comparison between the compar-
ative and crystal structures will be presented elsewhere
(RR Gutell et al., unpublished data).
Conclusions
Covariation analysis has accurately predicted all of the
standard secondary structure base pairings and helices in
the 16S and 23S rRNA crystal structures. These methods
have also identified some of the 16S and 23S rRNA tertiary
base–base interactions. Motif-based analysis has begun to
identify some of the base pairs that do not have similar
patterns of variation. Our future goal is to gain a better
understanding of tertiary base–base interactions from a
comparative perspective and, more specifically, to determine
their base pair types and exchanges, and the types of
structural elements or motifs with which they are associated.
A more complete set of RNA structure constraints is
necessary to accurately and reliably predict an RNA structure
from its underlying sequence, and to understand the
dynamics between structure and function.
Acknowledgements
This work was supported by the National Institutes of Health (GM48207),
by the Welch Foundation (F-1427) and by start-up funds from the Institute
for Cellular and Molecular Biology at the University of Texas at Austin.
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