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zivratech · 11 months
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aelumconsulting · 1 year
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ServiceNow ITAM vs. CMDB - What are the Differences?
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ITAM (IT Asset Management) and CMDB (Configuration Management Database) are two distinct approaches to managing IT resources. Some people feel confused to understand the connection and similarities between ITAM and CMDB. However, you can find some similarities between them, but also they have a lot of differences. In this blog, we have highlighted some of the most common differences between ITAM and CMDB. 
10 Key Differences Between ITAM and CMDB!
1. Scope: 
ITAM manages the entire lifecycle of IT assets. 
CMDB focuses on maintaining accurate and up-to-date information about the configuration items (CIs) in the IT infrastructure.
2. Focus: 
The ITAM module focuses on optimizing asset utilization, minimizing asset downtime, and reducing IT costs. 
In contrast, CMDB focuses on managing the relationships, dependencies, and changes of CIs across the IT infrastructure.
3. Functionality: 
ITAM tracks the inventory, procurement, deployment, maintenance, and retirement of hardware, software, and other IT assets. 
On the other hand, CMDB tracks the attributes, relationships, and dependencies of CIs, providing visibility into the impact of changes and incidents on the IT infrastructure.
4. Benefits: 
ITAM helps organizations make informed decisions about IT asset investments, upgrades, and replacements. 
In contrast, CMDB helps organizations manage risks, comply with regulations, and deliver IT services effectively.
5. Tools: 
Some examples of ITAM tools include ServiceNow, BMC Helix, and Ivanti. 
Examples of CMDB tools include ServiceNow, BMC Remedy, and HP Configuration Management System.
6. Data sources: 
ITAM typically collects data from various sources, such as asset tags, contracts, invoices, and discovery tools, to maintain an accurate inventory of IT assets. 
In contrast, CMDB collects data from various sources, such as network devices, servers, applications, and change management tools, to maintain an accurate configuration management database.
7. Metrics: 
ITAM measures various metrics, such as asset utilization, cost of ownership, and return on investment, to optimize asset management. 
In contrast, CMDB measures various metrics, such as service availability, incident impact, and change success rate, to improve service management.
8. Ownership: 
The IT asset management team owns the ITAM, responsible for the IT assets' procurement, deployment, and retirement. 
In contrast, the IT service management team owns the CMDB, responsible for the IT services delivery, support, and improvement.
9. Integration: 
ITAM can be integrated with other IT management tools, such as ITSM (IT Service Management), to provide a holistic view of IT operations. 
Similarly, CMDB can be integrated with other IT management tools, such as incident and change management, to support ITIL (IT Infrastructure Library) best practices.
10. Scope of control: 
ITAM helps manage IT assets within the organization's control, such as owned or leased assets. 
In contrast, CMDB helps manage the configuration items within the IT infrastructure, whether owned or leased by the organization or not.
In summary, ServiceNow ITAM is focused on managing IT assets throughout its lifecycle, while ServiceNow CMDB focuses on relationship management, dependencies, and changes of CIs in the IT infrastructure. Both approaches play an important role in managing IT resources effectively. You can come to us at Aelum Consulting for any problem with your instance. We have all the solutions for your problems no matter what ServiceNow module you want help with.
How Can Aelum Consulting Help You with Both ITAM and CMDB?
Aelum Consulting is Premier ServiceNow Partner specialized in helping organizations improve their IT management processes. Here are some ways that Aelum Consulting can help you with both ITAM and CMDB:
Assessment and Gap Analysis: We can check your organization's current ITAM and CMDB processes to identify gaps, inefficiencies, and areas for improvement. Based on the assessment, Aelum Consulting can provide recommendations for how to optimize your ITAM and CMDB processes.
Implementation and Integration: Our experts can help you implement and integrate ITAM and CMDB tools with other IT management tools, such as ITSM, to provide a holistic view of IT operations. Aelum Consulting can also help you customize the tools to fit your organization's needs.
Training and Support: We can train your IT staff how to use ITAM and CMDB tools effectively. Aelum Consulting can also provide ongoing support to ensure your ITAM and CMDB processes remain optimized and aligned with your organization's goals.
Compliance and Risk Management: We will ensure that your ITAM and CMDB processes comply with industry regulations and best practices. Aelum Consulting can also help you identify and mitigate IT-related risks that could impact your organization's operations or reputation.
Continuous Improvement: We can help you continuously improve your ITAM and CMDB processes by conducting regular reviews, gathering feedback from stakeholders, and implementing best practices from the industry.
In summary, Aelum Consulting can help you optimize your ITAM and CMDB processes by providing assessment, implementation, integration, training, support, compliance, risk management, and continuous improvement services.
Conclusion
So, this is how you can differentiate servicenow ITAM and CMDB and understand their use and importance better. You can come to our Aelum Consulting anytime you want as we have great servicenow experts with years of experience. 
For More Details And Blogs : Aelum Consulting Blogs
For ServiceNow Implementations and ServiceNow Consulting Visit our website: https://aelumconsulting.com/servicenow/
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itsmofficial · 3 years
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Vyom Labs specializes in BMC Atrium Discovery and Dependency Mapping consulting services like implementation, upgrade, integration and customization.
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Today, to succeed in the market every company needs to plan along with understanding the latest technologies. There has been a lot of talking about Chatbots, every time you read any business article online or receive a marketing email, it all revolves around chatbot technology. https://www.spmglobaltech.com/why-chatbot-live-agent-is-a-blessing/
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ulfix · 5 years
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Docker como herramienta  para la modernización de aplicaciones tradicionales.
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No es ningún secreto para nadie que  en los  últimos 2 años Docker se ha convertido en una herramienta fundamental en cuanto tecnología de contenedores, esto ha sido un resultado de la comunidad y clientes activos que favorecen la modernización de aplicaciones tradicionales con tecnología de contenedores. 
Dichas aplicaciones benefician típicamente a aquellas aplicaciones del tipo monolíticas y que corren sobre sistemas operativos como lo son Windows Server 2008 o Windows Server 2003, en los cuales son difíciles de realizar el traslado de los centros de datos o la nube.
La plataforma Docker permite resolver todos esos puntos de inflexibilidad,  desacoplando la aplicación de un sistema operativo, todo ello mediante patrones de arquitectura de microservicios los cuales favorecen la portabilidad a través de las instalaciones, cloud y ambientes híbridos
Docker ha invertido en herramientas y métodos para agilizar la transmisión de contenedores y al mismo tiempo disminuir el tiempo necesario para experimentar un valor superior por parte de la plataforma.
Descubrimiento y evaluación de aplicaciones 
Los desarrollos empresariales mantienen un gran portafolio de aplicaciones, dichas aplicaciones cuentan con diferentes lenguajes, idiomas y marcos de arquitectura desarrollado por terceros. El primer paso se requiere al implementar Docker es determinar cuáles aplicaciones cumplen con los parámetros para ser contenida así como determinar  dónde iniciar cada proceso.
El primer paso común es seleccionar la aplicación más compleja y sofisticada para ser contenida; la razón principal es el razonamiento de complejidad, asumiendo que si es posible contener una aplicación robusta, las aplicaciones que tienen menor peso responderán de la misma manera.
Para aquellas organizaciones nuevas la implementación de Docker suele ser un poco difícil debido en gran medida por la curva de aprendizaje, una aproximación prudente es el acercamiento al proceso de contenedores con aplicaciones menos complejas, lo cual permite alinear los objetivos y las metas, este proceso fomenta las habilidades del equipo, incrementando su experiencia para posteriores proyectos de mayor complejidad.
De igual manera Docker ha desarrollado una serie de arquetipos que permiten agrupar   aplicaciones similares basadas en características de arquitectura, con el fin de estimar la complejidad que requiere a la hora de ser contenidas.
Evaluar los portafolios de aplicaciones con cada uno de los arquetipos beneficia al proceso de estimación de nivel de dificultad,  así como la rápida selección de los candidatos más idóneos para ser contenidos en cada proyecto. Existen diversos métodos para la evaluación como lo son:
Evaluación manual de cada una de las aplicaciones.
Este método funciona bien con un pequeño número de aplicaciones, sin embargo es complicado realizarlo en entornos escalables (más de 100 aplicaciones)
 Configurar un (CMDBs) Configuration Management Databases 
Cuando se aplica en una organización es una poderosa herramienta que provee información detallada acerca del ambiente como lo son características y arquetipos relacionados.
Herramientas automatizadas como RISC Networks, Movere, BMC Helix Discovery
Son herramientas de terceros que ofrecen información detallada sobre los ambientes de  los centros de datos (data center), monitoreando la información durante un periodo de tiempo y generando reportes, dichos reportes pueden ser  utilizados en el proceso de contener  aplicaciones y a su vez permite conocer la carga de trabajo que implica.
Automatización de contenedores.
Construir un contenedor para aplicaciones tradicionales puede presentar muchos retos, alguna de las razones habituales que se presentan es que el desarrollador original  de la aplicación ya no está trabajando más en el proyecto o en el equipo, lo cual dificulta el proceso de entender cuál es la lógica de la aplicación o como fue construida, el código fuente no está disponible o las aplicaciones se ejecutan en máquinas virtuales sin activos, por ello la escalabilidad de los esfuerzos de contener docenas o ciento de aplicaciones requieren de mucha más tiempo.
Debido a esas particulares necesidades Docker cuenta con herramientas que ayudan al usuario, que a su vez forman parte de Docker Enterprise Platform, esta herramienta ha sido desarrollada para automatizar  y generar archivos “Dockerfiles” para aquellas aplicaciones que corren sobre un maquina virtual o un servidor. Los servidores son escaneados para determinar cómo es el sistema operativo así como su configuración, como los servicios web  están configurados y como el código está corriendo. Todos los datos son unidos dentro de un DockerFiles y el código de la aplicación es puesto dentro de un directorio el cual está listo para ser implementado en un moderno sistema operativo, por ejemplo, un Windows Server 2003 puede ser escaneado para generar DockerFiles para aplicaciones IIS-Based.Net que se ejecutan en grupos de aplicaciones IIS Pools. Esta manera de trabajo y automatización permite cambiar de ser un autor a convertirse en un editor, disminuyendo significativamente el tiempo y esfuerzo que se invierte en realizar contenedores tradicionales de aplicaciones. 
Gestión de Cluster.
Correr contenedores sobre un único servidor  puede ser suficiente para un solo desarrollador, sin embargo  un cluster que tiene servidores trabajando en conjunto requiere de sistemas de contenedores, históricamente la creación y administración de este tipo de clusters son controlados por un servidor público en la nube, vinculando a los usuario para que pueda utilizar la infraestructura.
Un nuevo Docker CLI Plugin, llamado “Docker Cluster”  es incluido en el paquete de Docker Enterprise 3.0, Docker Cluster  simplifica la  creación inicial de Docker Enterprise consumiendo un archivo declarado YAML, el cual automáticamente provee y configura los recursos de la infraestructura. Los Clusters tal vez utilizan una variedad de infraestructuras, incluyendo Azure, AWS, and VMware, con el fin de mantener plataformas de contenedores idénticas  en cada una de sus infraestructuras  objetivo.
A su vez agrega flexibilidad y disminuye las necesidades de elegir un único proveedor, permitiendo a su vez utilizar entornos multi-clound, entornos híbridos y provee la opción de  desplegar contenedores ya sea los orquestadores Kubernetes o Swarm.
Más allá de la herramienta de automatización, docker también ofrece  detalles específicos sobre la infraestructura, referentes a la arquitectura la cual  incluye un catálogo de las mejores prácticas para varios proveedores, dichos documentos ofrecen una guía exhaustiva sobre la implementación de Docker Enterprise adicionalmente a la herramienta CLI.
En conclusión, el proceso de crear contenedores ha sido ampliamente simplificado por Docker Enterprise mediante soluciones como Docker Cluster, Solution Briefs y Reference Architectures, estas herramientas le permiten  enfocarse en el proceso de contener aplicaciones heredadas en lugar de invertir tiempo adicional en la configuración de un clúster de contenedores.
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faizrashis1995 · 5 years
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Find and Fix Docker Doomsday with BMC
That quote is true today just like it was in the 1700’s, and while he was actually talking about fire safety (some believe he was referring to staying healthy – not true), Mr. Franklin could just as easily have been talking about protecting against security vulnerabilities, including a new and very dangerous one, Docker Doomsday.
What is Docker Doomsday?
The Docker Doomsday vulnerability affects almost any organization using Docker and containers. Here’s a quick look at what it does. First, an attacker infects a container with a malicious program. The malicious code exploits a flaw in runc, which is the container runtime utility for Docker and Kubernetes.
Next, the malicious code breaks out and infects the entire container host, and spreads to potentially thousands of other containers running on that host. This is a Doomsday scenario because the attack can ultimately affect many interconnected, production systems.
How bad is Docker Doomsday?
Well, it’s CVE 2019-5736 and has an overall Common Vulnerability Scoring System (CVSS) value of 8.6, that’s on a scale of 1-10 where 10 is as bad as it gets. Another perspective comes from RedHat. They classified it as “Important Impact”, a category reserved for vulnerabilities that can lead to unauthorized access to sensitive data, or a denial of service.
How to Solve for Docker Doomsday
Now the good news. Since the leading security vulnerability scanners (such as Qualys and Nessus) can find Docker Doomsday, you can run a scan and automatically import the vulnerability data into TrueSight Vulnerability Management. There you can analyze it and leverage its integration with TrueSight Server Automation to fix it, either on-premises or in the cloud. If you want to go one step further, use BMC Helix Discovery to find “blind spots” (cloud-based Docker instances that the scanners missed) to obtain a complete picture of where Docker Doomsday exists.
If you are in Cloud Operations and use TrueSight Cloud Security, you can scan your Docker instances and containers, find Docker Doomsday, and fix it with a security patch using TrueSight Server Automation.
Thinking back to Benjamin Franklin, your ounce of prevention is patching with BMC TrueSight Server Automation. But do it soon, time favors the attacker, not the defender.[Source]-https://www.bmc.com/blogs/find-and-fix-docker-doomsday-with-bmc/
Beginners & Advanced level Docker Training in Mumbai. Asterix Solution's 25 Hour Docker Training gives broad hands-on practicals.
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bjcits · 5 years
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Consultant BMC Helix Discovery (remote) (m/w/d) - Neckarsulm - 49176/XG
http://dlvr.it/R4r182
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naivelocus · 7 years
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Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations
Cech, T. R., Zaug, A. J. & Grabowski, P. J. splicing of the ribosomal RNA precursor of tetrahymena: Involvement of a guanosine nucleotide in the excision of the intervening sequence. Cell 27, 487–496, http://dx.doi.org/10.1016/0092-8674(81)90390-1 (1981).
Kruger, K. et al. Self-Splicing Rna - Auto-Excision and Auto-Cyclization of the Ribosomal-Rna Intervening Sequence of Tetrahymena. Cell 31, 147–157, doi: 10.1016/0092-8674(82)90414-7 (1982).
Guerriertakada, C., Gardiner, K., Marsh, T., Pace, N. & Altman, S. The RNA moiety Of ribonuclease-P is the catalytic subunit of the enzyme. Cell 35, 849–857, doi: 10.1016/0092-8674(83)90117-4 (1983).
Mironov, A. S. et al. Sensing small molecules by nascent RNA: A mechanism to control transcription in bacteria. Cell 111, 747–756, doi: 10.1016/s0092-8674(02)01134-0 (2002).
Nahvi, A. et al. Genetic control by a metabolite binding mRNA. Chem. Biol. 9, 1043–1049, doi: 10.1016/s1074-5521(02)00224-7 (2002).
Winkler, W., Nahvi, A. & Breaker, R. R. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature 419, 952–956, doi: 10.1038/nature01145 (2002).
Breaker, R. R. Prospects for Riboswitch Discovery and Analysis. Mol. Cell 43, 867–879, doi: 10.1016/j.molcel.2011.08.024 (2011).
Serganov, A. & Nudler, E. A Decade of Riboswitches. Cell 152, 17–24, doi: 10.1016/j.cell.2012.12.024 (2013).
Lai, D., Proctor, J. R. & Meyer, I. M. On the importance of cotranscriptional RNA structure formation. RNA-Publ. RNA Soc. 19, 1461–1473, doi: 10.1261/rna.037390.112 (2013).
Russell, R. In Biophysics of RNA Folding Biophysics for the Life Sciences (ed. R. Russell) Ch. 1, 1–10 (Springer-Verlag: New York, 2013).
Mitchell, D., Jarmoskaite, I., Seval, N., Seifert, S. & Russell, R. The Long-Range P3 Helix of the Tetrahymena Ribozyme Is Disrupted during Folding between the Native and Misfolded Conformations. J. Mol. Biol. 425, 2670–2686, doi: 10.1016/j.jmb.2013.05.008 (2013).
Mitchell, D. & Russell, R. Folding Pathways of the Tetrahymena Ribozyme. J. Mol. Biol. 426, 2300–2312, doi: 10.1016/j.jmb.2014.04.011 (2014).
Russell, R. et al. The paradoxical behavior of a highly structured misfolded intermediate in RNA folding. J. Mol. Biol. 363, 531–544, doi: 10.1016/j.jmb.2006.08.024 (2006).
Russell, R. et al. Exploring the folding landscape of a structured RNA. Proceedings of the National Academy of Sciences of the United States of America 99, 155–160, doi: 10.1073/pnas.221593598 (2002).
Thirumalai, D. & Hyeon, C. In Non-Protein Coding RNAs (eds Nils G. Walter, Sarah A. Woodson & Robert T. Batey) 27–47 (Springer Berlin Heidelberg, 2009).
Silverman, S. K., Deras, M. L., Woodson, S. A., Scaringe, S. A. & Cech, T. R. Multiple Folding Pathways for the P4–P6 RNA Domain. Biochemistry 39, 12465–12475, doi: 10.1021/bi000828y (2000).
Woodson, S. A. Recent insights on RNA folding mechanisms from catalytic RNA. Cell. Mol. Life Sci. 57, 796–808, doi: 10.1007/s000180050042 (2000).
Schroeder, R., Barta, A. & Semrad, K. Strategies for RNA folding and assembly. Nature Reviews Molecular Cell Biology 5, 908–919, doi: 10.1038/nrm1497 (2004).
Bokinsky, G. & Zhuang, X. W. Single-molecule RNA folding. Accounts Chem. Res. 38, 566–573, doi: 10.1021/ar040142o (2005).
Gell, C. et al. Single-Molecule Fluorescence Resonance Energy Transfer Assays Reveal Heterogeneous Folding Ensembles in a Simple RNA Stem-Loop. J. Mol. Biol. 384, 264–278, doi: 10.1016/j.jmb.2008.08.088 (2008).
Uhlenbeck, O. C. Keeping RNA happy. RNA-Publ. RNA Soc. 1, 4–6 (1995).
Uhlenbeck, O. C. RNA biophysics has come of age. Biopolymers 91, 811–814, doi: 10.1002/bip.21269 (2009).
Schuster, P. Prediction of RNA secondary structures: from theory to models and real molecules. Rep. Prog. Phys. 69, 1419–1477, doi: 10.1088/0034-4885/69/5/r04 (2006).
Cannone, J. J. et al. The Comparative RNA Web (CRW) Site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. Bmc Bioinformatics 3, doi: 10.1186/1471-2105-3-2 (2002).
Bernhart, S. H., Hofacker, I. L., Will, S., Gruber, A. R. & Stadler, P. F. RNAalifold: improved consensus structure prediction for RNA alignments. Bmc Bioinformatics 9, 13, doi: 10.1186/1471-2105-9-474 (2008).
Hofacker, I. L., Fekete, M. & Stadler, P. F. Secondary structure prediction for aligned RNA sequences. J. Mol. Biol. 319, 1059–1066, doi: 10.1016/s0022-2836(02)00308-x (2002).
Knudsen, B. & Hein, J. Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Research 31, 3423–3428, doi: 10.1093/nar/gkg614 (2003).
Turner, D. H. & Mathews, D. H. NNDB: the nearest neighbor parameter database for predicting stability of nucleic acid secondary structure. Nucleic Acids Research 38, D280–D282, doi: 10.1093/nar/gkp892 (2010).
Markham, N. & Zuker, M. In Bioinformatics Vol. 453 Methods in Molecular Biology™ (ed. Jonathan M. Keith) Ch. 1, 3–31 (Humana Press, 2008).
Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research 31, 3406–3415, doi: 10.1093/nar/gkg595 (2003).
Hofacker, I. L. et al. Fast folding and comparison of RNA secondary structures. Mon. Chem. 125, 167–188, doi: 10.1007/bf00818163 (1994).
Hofacker, I. In Comparative Genomics Vol. 395 Methods in Molecular Biology™ (ed. Nicholas. H. Bergman) Ch. 33, 527–543 (Humana Press, 2008).
Lorenz, R. et al. ViennaRNA package 2.0. Algorithms for Molecular Biology 6, 1–14, doi: 10.1186/1748-7188-6-26 (2011).
Doshi, K. J., Cannone, J. J., Cobaugh, C. W. & Gutell, R. R. Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction. BMC Bioinformatics 5, 1–22, doi: 10.1186/1471-2105-5-105 (2004).
Bellaousov, S. & Mathews, D. H. ProbKnot: Fast prediction of RNA secondary structure including pseudoknots. RNA 16, 1870–1880, doi: 10.1261/rna.2125310 (2010).
Ren, J., Rastegari, B., Condon, A. & Hoos, H. H. HotKnots: Heuristic prediction of RNA secondary structures including pseudoknots. RNA 11, 1494–1504, doi: 10.1261/rna.7284905 (2005).
Sato, K., Kato, Y., Hamada, M., Akutsu, T. & Asai, K. IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming. Bioinformatics 27, i85–i93, doi: 10.1093/bioinformatics/btr215 (2011).
Wilkinson, K. A., Merino, E. J. & Weeks, K. M. Selective 2[prime]-hydroxyl acylation analyzed by primer extension (SHAPE): quantitative RNA structure analysis at single nucleotide resolution. Nat. Protocols 1, 1610–1616, doi: 10.1038/nprot.2006.249 (2006).
Lusvarghi, S., Sztuba-Solinska, J., Purzycka, K. J., Rausch, J. W. & Le Grice, S. F. J. RNA Secondary Structure Prediction Using High-throughput SHAPE. e50243, doi: 10.3791/50243 (2013).
Leonard, C. W. et al. Principles for Understanding the Accuracy of SHAPE-Directed RNA Structure Modeling. Biochemistry 52, 588–595, doi: 10.1021/bi300755u (2013).
Kladwang, W., VanLang, C. C., Cordero, P. & Das, R. Understanding the Errors of SHAPE-Directed RNA Structure Modeling. Biochemistry 50, 8049–8056, doi: 10.1021/bi200524n (2011).
Sükösd, Z., Swenson, M. S., Kjems, J. & Heitsch, C. E. Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions. Nucleic Acids Research 41, 2807–2816, doi: 10.1093/nar/gks1283 (2013).
Lorenz, R., Luntzer, D., Hofacker, I. L., Stadler, P. F. & Wolfinger, M. T. SHAPE directed RNA folding. Bioinformatics 32, 145–147, doi: 10.1093/bioinformatics/btv523 (2016).
Hajdin, C. E. et al. Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots. Proceedings of the National Academy of Sciences 110, 5498–5503, doi: 10.1073/pnas.1219988110 (2013).
Laing, C. & Schlick, T. Computational approaches to RNA structure prediction, analysis, and design. Current Opinion in Structural Biology 21, 306–318, doi: 10.1016/j.sbi.2011.03.015 (2011).
Laing, C. & Schlick, T. Computational approaches to 3D modeling of RNA. J. Phys.-Condes. Matter 22, 18, doi: 10.1088/0953-8984/22/28/283101 (2010).
Parisien, M. & Major, F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452, 51–55, doi: http://www.nature.com/nature/journal/v452/n7183/suppinfo/nature06684_S1.html (2008).
Frellsen, J. et al. A Probabilistic Model of RNA Conformational Space. Plos Computational Biology 5, 11, doi: 10.1371/journal.pcbi.1000406 (2009).
Bida, J. P. & Maher, L. J. Improved prediction of RNA tertiary structure with insights into native state dynamics. RNA-Publ. RNA Soc. 18, 385–393, doi: 10.1261/rna.027201.111 (2012).
Zhao, Y. J. et al. Automated and fast building of three-dimensional RNA structures. Sci Rep. 2, 6, doi: 10.1038/srep00734 (2012).
Popenda, M. et al. Automated 3D structure composition for large RNAs. Nucleic Acids Research 40, 12, doi: 10.1093/nar/gks339 (2012).
Cao, S. & Chen, S.-J. Predicting RNA folding thermodynamics with a reduced chain representation model. RNA 11, 1884–1897, doi: 10.1261/rna.2109105 (2005).
Cao, S. & Chen, S. J. Predicting structures and stabilities for H-type pseudoknots with interhelix loops. RNA-Publ. RNA Soc. 15, 696–706, doi: 10.1261/rna.1429009 (2009).
Cao, S. & Chen, S. J. Physics-Based De Novo Prediction of RNA 3D Structures. J. Phys. Chem. B. 115, 4216–4226, doi: 10.1021/jp112059y (2011).
Xu, X. J., Zhao, P. N. & Chen, S. J. Vfold: A Web Server for RNA Structure and Folding Thermodynamics Prediction. PLoS One 9, 7, doi: 10.1371/journal.pone.0107504 (2014).
Reinharz, V., Major, F. & Waldispühl, J. Towards 3D structure prediction of large RNA molecules: an integer programming framework to insert local 3D motifs in RNA secondary structure. Bioinformatics 28, i207–i214, doi: 10.1093/bioinformatics/bts226 (2012).
Das, R. & Baker, D. Automated de novo prediction of native-like RNA tertiary structures. Proceedings of the National Academy of Sciences 104, 14664–14669, doi: 10.1073/pnas.0703836104 (2007).
Das, R., Karanicolas, J. & Baker, D. Atomic accuracy in predicting and designing noncanonical RNA structure. Nature Methods 7, 291–294, doi: 10.1038/nmeth.1433 (2010).
Cheng, C. Y., Chou, F.-C. & Das, R. In Methods in Enzymology Vol. 553 (eds Chen Shi-Jie & H. Burke-Aguero Donald) 35–64 (Academic Press, 2015).
Leaver-Fay, A. et al. InMethods in Enzymology Vol. 487 (eds L. Johnson Michael & Brand Ludwig) 545–574 (Academic Press, 2011).
Jossinet, F., Ludwig, T. E. & Westhof, E. Assemble: an interactive graphical tool to analyze and build RNA architectures at the 2D and 3D levels. Bioinformatics 26, 2057–2059, doi: 10.1093/bioinformatics/btq321 (2010).
Martinez, H. M., Maizel, J. V. & Shapiro, B. A. RNA2D3D: A program for Generating, Viewing, and Comparing 3-Dimensional Models of RNA. Journal of Biomolecular Structure and Dynamics 25, 669–683, doi: 10.1080/07391102.2008.10531240 (2008).
Kim, N., Petingi, L. & Schlick, T. Network Theory Tools for RNA Modeling. WSEAS transactions on mathematics 9, 941–955 (2013).
Kim, N. et al. Graph-based sampling for approximating global helical topologies of RNA. Proceedings of the National Academy of Sciences 111, 4079–4084, doi: 10.1073/pnas.1318893111 (2014).
Kim, N., Zahran, M. & Schlick, T. Computational prediction of riboswitch tertiary structures including pseudoknots by RAGTOP: a hierarchical graph sampling approach. Methods in enzymology 553, 115–135, doi: 10.1016/bs.mie.2014.10.054 (2015).
Zahran, M., Sevim Bayrak, C., Elmetwaly, S. & Schlick, T. RAG-3D: a search tool for RNA 3D substructures. Nucleic Acids Research, doi: 10.1093/nar/gkv823 (2015).
Izzo, J. A., Kim, N., Elmetwaly, S. & Schlick, T. RAG: An update to the RNA-As-Graphs resource. Bmc Bioinformatics 12, 17, doi: 10.1186/1471-2105-12-219 (2011).
Fulle, S. & Gohlke, H. Statics of the Ribosomal Exit Tunnel: Implications for Cotranslational Peptide Folding, Elongation Regulation, and Antibiotics Binding. J. Mol. Biol. 387, 502–517, doi: 10.1016/j.jmb.2009.01.037 (2009).
Gillespie, J., Mayne, M. & Jiang, M. RNA folding on the 3D triangular lattice. BMC Bioinformatics 10, 1–17, doi: 10.1186/1471-2105-10-369 (2009).
Kerpedjiev, P., Höner zu Siederdissen, C. & Hofacker, I. L. Predicting RNA 3D structure using a coarse-grain helix-centered model. RNA 21, 1110–1121, doi: 10.1261/rna.047522.114 (2015).
Lamiable, A., Quessette, F., Vial, S., Barth, D. & Denise, A. An Algorithmic Game-Theory Approach for Coarse-Grain Prediction of RNA 3D Structure. Ieee-Acm Transactions on Computational Biology and Bioinformatics 10, 193–199, doi: 10.1109/tcbb.2012.148 (2013).
Dawson, W. K., Maciejczyk, M., Jankowska, E. J. & Bujnicki, J. M. Coarse-grained modeling of RNA 3D structure. Methods, doi: 10.1016/j.ymeth.2016.04.026.
Malhotra, A., Tan, R. K. Z. & Harvey, S. C. Modeling large RNAS and ribonucleoprotein-particles using molecular mechanics techniques. Biophys. J. 66, 1777–1795 (1994).
Tan, R. K. Z., Petrov, A. S. & Harvey, S. C. YUP: A molecular simulation program for coarse-grained and multiscaled models. Journal of Chemical Theory and Computation 2, 529–540, doi: 10.1021/ct050323r (2006).
Jonikas, M. A., Radmer, R. J. & Altman, R. B. Knowledge-based instantiation of full atomic detail into coarse-grain RNA 3D structural models. Bioinformatics 25, 3259–3266, doi: 10.1093/bioinformatics/btp576 (2009).
Jonikas, M. A. et al. Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA 15, 189–199, doi: 10.1261/rna.1270809 (2009).
Krokhotin, A., Houlihan, K. & Dokholyan, N. V. iFoldRNA v2: folding RNA with constraints. Bioinformatics, doi: 10.1093/bioinformatics/btv221 (2015).
Sharma, S., Ding, F. & Dokholyan, N. V. iFoldRNA: three-dimensional RNA structure prediction and folding. Bioinformatics 24, 1951–1952, doi: 10.1093/bioinformatics/btn328 (2008).
Denesyuk, N. A. & Thirumalai, D. Coarse-Grained Model for Predicting RNA Folding Thermodynamics. J. Phys. Chem. B 117, 4901–4911, doi: 10.1021/jp401087x (2013).
Denesyuk, N. A. & Thirumalai, D. How do metal ions direct ribozyme folding? Nat Chem 7, 793–801, doi: 10.1038/nchem.2330, http://www.nature.com/nchem/journal/v7/n10/abs/nchem.2330.html-supplementary-information (2015).
Mustoe, A. M., Al-Hashimi, H. M. & Brooks, C. L. Coarse Grained Models Reveal Essential Contributions of Topological Constraints to the Conformational Free Energy of RNA Bulges. The Journal of Physical Chemistry B 118, 2615–2627, doi: 10.1021/jp411478x (2014).
Mustoe, A. M., Brooks, C. L. & Al-Hashimi, H. M. Topological constraints are major determinants of tRNA tertiary structure and dynamics and provide basis for tertiary folding cooperativity. Nucleic Acids Research 42, 11792–11804, doi: 10.1093/nar/gku807 (2014).
Mustoe, A. M. et al. Noncanonical Secondary Structure Stabilizes Mitochondrial tRNASer(UCN) by Reducing the Entropic Cost of Tertiary Folding. J. Am. Chem. Soc. 137, 3592–3599, doi: 10.1021/ja5130308 (2015).
Cragnolini, T., Derreumaux, P. & Pasquali, S. Coarse-Grained Simulations of RNA and DNA Duplexes. J. Phys. Chem. B 117, 8047–8060, doi: 10.1021/jp400786b (2013).
Pasquali, S. & Derreumaux, P. HiRE-RNA: A High Resolution Coarse-Grained Energy Model for RNA. The Journal of Physical Chemistry B 114, 11957–11966, doi: 10.1021/jp102497y (2010).
Cragnolini, T., Laurin, Y., Derreumaux, P. & Pasquali, S. Coarse-Grained HiRE-RNA Model for ab Initio RNA Folding beyond Simple Molecules, Including Noncanonical and Multiple Base Pairings. Journal of Chemical Theory and Computation 11, 3510–3522, doi: 10.1021/acs.jctc.5b00200 (2015).
Boniecki, M. J. et al. SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Research 44, e63, doi: 10.1093/nar/gkv1479 (2016).
Magnus, M., Boniecki, M. J., Dawson, W. & Bujnicki, J. M. SimRNAweb: a web server for RNA 3D structure modeling with optional restraints. Nucleic Acids Research, doi: 10.1093/nar/gkw279 (2016).
Bernauer, J., Huang, X., Sim, A. Y. L. & Levitt, M. Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation. RNA 17, 1066–1075, doi: 10.1261/rna.2543711 (2011).
Xia, Z., Bell, D. R., Shi, Y. & Ren, P. RNA 3D Structure Prediction by Using a Coarse-Grained Model and Experimental Data. The Journal of Physical Chemistry B 117, 3135–3144, doi: 10.1021/jp400751w (2013).
Xia, Z., Gardner, D. P., Gutell, R. R. & Ren, P. Y. Coarse-Grained Model for Simulation of RNA Three-Dimensional Structures. J. Phys. Chem. B 114, 13497–13506, doi: 10.1021/jp104926t (2010).
Xia, Z. & Ren, P. In Biophysics of RNA Folding Vol. 3 Biophysics for the Life Sciences (ed. Rick Russell) Ch. 4, 53–68 (Springer New York, 2013).
TINKER Molecular Modeling Package v. 6.3 (http://dasher.wustl.edu/tinker).
Wang, L.-P., Chen, J. & Van Voorhis, T. Systematic Parametrization of Polarizable Force Fields from Quantum Chemistry Data. Journal of Chemical Theory and Computation 9, 452–460, doi: 10.1021/ct300826t (2013).
Hyeon, C., Dima, R. I. & Thirumalai, D. Size, shape, and flexibility of RNA structures. The Journal of Chemical Physics 125, 194905, doi: 10.1063/1.2364190 (2006).
Saunders, M. G. & Voth, G. A. Coarse-Graining Methods for Computational Biology. Annual Review of Biophysics 42, 73–93, doi: 10.1146/annurev-biophys-083012-130348 (2013).
Müller-Plathe, F. Coarse-Graining in Polymer Simulation: From the Atomistic to the Mesoscopic Scale and Back. ChemPhysChem 3, 754–769, doi: 10.1002/1439-7641(20020916)3:9<754::AID-CPHC754>3.0.CO;2-U (2002).
Tschöp, W., Kremer, K., Batoulis, J., Bürger, T. & Hahn, O. Simulation of polymer melts. I. Coarse-graining procedure for polycarbonates. Acta Polymerica 49, 61–74, doi: 10.1002/(SICI)1521-4044(199802)49:2/3<61::AID-APOL61>3.0.CO;2-V (1998).
Zhao, F. & Xu, J. A Position-Specific Distance-Dependent Statistical Potential for Protein Structure and Functional Study. Structure 20, 1118–1126, doi: 10.1016/j.str.2012.04.003 (2012).
Zhou, H. & Zhou, Y. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Science 11, 2714–2726, doi: 10.1110/ps.0217002 (2002).
Shen, M.-y. & Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Science: A Publication of the Protein Society 15, 2507–2524, doi: 10.1110/ps.062416606 (2006).
Anfinsen, C. B. Principles that Govern the Folding of Protein Chains. Science 181, 223–230, doi: 10.1126/science.181.4096.223 (1973).
Yakovchuk, P., Protozanova, E. & Frank-Kamenetskii, M. D. Base-stacking and base-pairing contributions into thermal stability of the DNA double helix. Nucleic Acids Research 34, 564–574, doi: 10.1093/nar/gkj454 (2006).
Xia, T. B. et al. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 37, 14719–14735, doi: 10.1021/bi9809425 (1998).
Mathews, D. H. et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proceedings of the National Academy of Sciences of the United States of America 101, 7287–7292, doi: 10.1073/pnas.0401799101 (2004).
Freier, S. M. et al. Improved Free-Energy Parameters for Predictions of Rna Duplex Stability. Proceedings of the National Academy of Sciences of the United States of America 83, 9373–9377, doi: 10.1073/pnas.83.24.9373 (1986).
Borer, P. N., Dengler, B., Tinoco, I. Jr. & Uhlenbeck, O. C. Stability of ribonucleic acid double-stranded helices. J. Mol. Biol. 86, 843–853, doi: 10.1016/0022-2836(74)90357-X (1974).
Breslauer, K. J., Frank, R., Blocker, H. & Marky, L. A. Predicting DNA duplex stability from the base sequence. Proceedings of the National Academy of Sciences of the United States of America 83, 3746–3750, doi: 10.1073/pnas.83.11.3746 (1986).
Xia, T. B., McDowell, J. A. & Turner, D. H. Thermodynamics of nonsymmetric tandem mismatches adjacent to G center dot C base pairs in RNA. Biochemistry 36, 12486–12497, doi: 10.1021/bi971069v (1997).
Li, P. T. X., Collin, D., Smith, S. B., Bustamante, C. & Tinoco, I. Probing the mechanical folding kinetics of TAR RNA by hopping, force-jump, and force-ramp methods. Biophys. J. 90, 250–260, doi: 10.1529/biophysj.105.068049 (2006).
WHAM: The Weighted Histogram Analysis Method v. 2.0.9 (http://membrane.urmc.rochester.edu/content/wham).
Burkard, M. E., Kierzek, R. & Turner, D. H. Thermodynamics of unpaired terminal nucleotides on short RNA helixes correlates with stacking at helix termini in larger RNAs1. J. Mol. Biol. 290, 967–982, doi: 10.1006/jmbi.1999.2906 (1999).
Woodside, M. T. et al. Direct Measurement of the Full, Sequence-Dependent Folding Landscape of a Nucleic Acid. Science 314, 1001–1004, doi: 10.1126/science.1133601 (2006).
Woodside, M. T. et al. Nanomechanical measurements of the sequence-dependent folding landscapes of single nucleic acid hairpins. Proceedings of the National Academy of Sciences 103, 6190–6195, doi: 10.1073/pnas.0511048103 (2006).
Liphardt, J., Onoa, B., Smith, S. B., Tinoco, I. & Bustamante, C. Reversible Unfolding of Single RNA Molecules by Mechanical Force. Science 292, 733–737, doi: 10.1126/science.1058498 (2001).
Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H. & Kollman, P. A. THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem. 13, 1011–1021, doi: 10.1002/jcc.540130812 (1992).
Eastman, P. et al. OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation. Journal of Chemical Theory and Computation 9, 461–469, doi: 10.1021/ct300857j (2013).
Dale, T., Smith, R. & Serra, M. J. A test of the model to predict unusually stable RNA hairpin loop stability. RNA 6, 608–615 (2000).
Giese, M. R. et al. Stability of RNA Hairpins Closed by Wobble Base Pairs. Biochemistry 37, 1094–1100, doi: 10.1021/bi972050v (1998).
Antao, V. P. & Tinoco, I. Thermodynamic parameters for loop formation in RNA and DNA hairpin tetraloops. Nucleic Acids Research 20, 819–824, doi: 10.1093/nar/20.4.819 (1992).
Serra, M. J., Lyttle, M. H., Axenson, T. J., Schadt, C. A. & Turner, D. H. RNA hairpin loop stability depends on closing base pair. Nucleic Acids Research 21, 3845–3849 (1993).
Groebe, D. R. & Uhlenbeck, O. C. Characterization of Rna Hairpin Loop Stability. Nucleic Acids Research 16, 11725–11735, doi: 10.1093/nar/16.24.11725 (1988).
— Nature Scientific Reports
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