Columba: an integrated database of proteins, structures, and annotations

Trißl, Silke; Rother, Kristian; Müller, Henrik Høeg; Steinke, Thomas; Koch, Ina; Preißner, Robert; Frömmel, Cornelius; Leser, Ulf

doi:10.1186/1471-2105-6-81

Cited by 42 publications

(7 citation statements)

References 32 publications

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“…[3]) to study three-dimensional RNA structure. Among them, two particular programs are of general use and of specific importance to our work: 3DNA [4] and RNAView [5].…”

Section: Introductionmentioning

confidence: 99%

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures

et al. 2010

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BackgroundRecent discoveries concerning novel functions of RNA, such as RNA interference, have contributed towards the growing importance of the field. In this respect, a deeper knowledge of complex three-dimensional RNA structures is essential to understand their new biological functions. A number of bioinformatic tools have been proposed to explore two major structural databases (PDB, NDB) in order to analyze various aspects of RNA tertiary structures. One of these tools is RNA FRABASE 1.0, the first web-accessible database with an engine for automatic search of 3D fragments within PDB-derived RNA structures. This search is based upon the user-defined RNA secondary structure pattern. In this paper, we present and discuss RNA FRABASE 2.0. This second version of the system represents a major extension of this tool in terms of providing new data and a wide spectrum of novel functionalities. An intuitionally operated web server platform enables very fast user-tailored search of three-dimensional RNA fragments, their multi-parameter conformational analysis and visualization.DescriptionRNA FRABASE 2.0 has stored information on 1565 PDB-deposited RNA structures, including all NMR models. The RNA FRABASE 2.0 search engine algorithms operate on the database of the RNA sequences and the new library of RNA secondary structures, coded in the dot-bracket format extended to hold multi-stranded structures and to cover residues whose coordinates are missing in the PDB files. The library of RNA secondary structures (and their graphics) is made available. A high level of efficiency of the 3D search has been achieved by introducing novel tools to formulate advanced searching patterns and to screen highly populated tertiary structure elements. RNA FRABASE 2.0 also stores data and conformational parameters in order to provide "on the spot" structural filters to explore the three-dimensional RNA structures. An instant visualization of the 3D RNA structures is provided. RNA FRABASE 2.0 is freely available at http://rnafrabase.cs.put.poznan.pl.ConclusionsRNA FRABASE 2.0 provides a novel database and powerful search engine which is equipped with new data and functionalities that are unavailable elsewhere. Our intention is that this advanced version of the RNA FRABASE will be of interest to all researchers working in the RNA field.

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“…[3]) to study three-dimensional RNA structure. Among them, two particular programs are of general use and of specific importance to our work: 3DNA [4] and RNAView [5].…”

Section: Introductionmentioning

confidence: 99%

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures

et al. 2010

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“…In exploratory analyses where samples prepared with the “whole genome method” recovered relatively fewer loci with the HybPiper pipeline, we subsequently assembled the whole genome sequence reads into de novo draft genomes prior to a target gene identification with Exonerate (Slater and Birney 2005 ) – we named this approach “de novo assembly method”. De novo assemblies of the trimmed paired-end Illumina reads were constructed with SPAdes v3.5 or v3.11 (Bankevich et al 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Using target enrichment sequencing to study the higher-level phylogeny of the largest lichen-forming fungi family: Parmeliaceae (Ascomycota)

Grewe¹,

Ametrano²,

Widhelm³

et al. 2020

IMA Fungus

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Parmeliaceae is the largest family of lichen-forming fungi with a worldwide distribution. We used a target enrichment data set and a qualitative selection method for 250 out of 350 genes to infer the phylogeny of the major clades in this family including 81 taxa, with both subfamilies and all seven major clades previously recognized in the subfamily Parmelioideae. The reduced genome-scale data set was analyzed using concatenated-based Bayesian inference and two different Maximum Likelihood analyses, and a coalescent-based species tree method. The resulting topology was strongly supported with the majority of nodes being fully supported in all three concatenated-based analyses. The two subfamilies and each of the seven major clades in Parmelioideae were strongly supported as monophyletic. In addition, most backbone relationships in the topology were recovered with high nodal support. The genus Parmotrema was found to be polyphyletic and consequently, it is suggested to accept the genus Crespoa to accommodate the species previously placed in Parmotrema subgen. Crespoa. This study demonstrates the power of reduced genome-scale data sets to resolve phylogenetic relationships with high support. Due to lower costs, target enrichment methods provide a promising avenue for phylogenetic studies including larger taxonomic/specimen sampling than whole genome data would allow.

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“…We also build on our own long-standing experience in working with data from the area of systems biology and systems medicine. This includes both technical expertise in integration of such data and its storage in relational databases and information systems (e.g., [ 32 – 35 ]), and practical application of such data in molecular oncology - and the insight of which information our own treatment decisions are influenced by.…”

Section: Methodsmentioning

confidence: 99%

Variant information systems for precision oncology

Starlinger

Pallarz

Ševa

et al. 2018

BMC Med Inform Decis Mak

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BackgroundThe decreasing cost of obtaining high-quality calls of genomic variants and the increasing availability of clinically relevant data on such variants are important drivers for personalized oncology. To allow rational genome-based decisions in diagnosis and treatment, clinicians need intuitive access to up-to-date and comprehensive variant information, encompassing, for instance, prevalence in populations and diseases, functional impact at the molecular level, associations to druggable targets, or results from clinical trials. In practice, collecting such comprehensive information on genomic variants is difficult since the underlying data is dispersed over a multitude of distributed, heterogeneous, sometimes conflicting, and quickly evolving data sources. To work efficiently, clinicians require powerful Variant Information Systems (VIS) which automatically collect and aggregate available evidences from such data sources without suppressing existing uncertainty.MethodsWe address the most important cornerstones of modeling a VIS: We take from emerging community standards regarding the necessary breadth of variant information and procedures for their clinical assessment, long standing experience in implementing biomedical databases and information systems, our own clinical record of diagnosis and treatment of cancer patients based on molecular profiles, and extensive literature review to derive a set of design principles along which we develop a relational data model for variant level data. In addition, we characterize a number of public variant data sources, and describe a data integration pipeline to integrate their data into a VIS.ResultsWe provide a number of contributions that are fundamental to the design and implementation of a comprehensive, operational VIS. In particular, we (a) present a relational data model to accurately reflect data extracted from public databases relevant for clinical variant interpretation, (b) introduce a fault tolerant and performant integration pipeline for public variant data sources, and (c) offer recommendations regarding a number of intricate challenges encountered when integrating variant data for clincal interpretation.ConclusionThe analysis of requirements for representation of variant level data in an operational data model, together with the implementation-ready relational data model presented here, and the instructional description of methods to acquire comprehensive information to fill it, are an important step towards variant information systems for genomic medicine.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0665-z) contains supplementary material, which is available to authorized users.

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Columba: an integrated database of proteins, structures, and annotations

Cited by 42 publications

References 32 publications

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures

RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures

Using target enrichment sequencing to study the higher-level phylogeny of the largest lichen-forming fungi family: Parmeliaceae (Ascomycota)

Variant information systems for precision oncology

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