References of "Nucleic Acids Research"
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See detailMartini: using literature keywords to compare gene sets.
Soldatos, Theodoros G.; O'Donoghue, Sean I.; Satagopam, Venkata UL et al

in Nucleic acids research (2010), 38(1), 26-38

Life scientists are often interested to compare two gene sets to gain insight into differences between two distinct, but related, phenotypes or conditions. Several tools have been developed for comparing ... [more ▼]

Life scientists are often interested to compare two gene sets to gain insight into differences between two distinct, but related, phenotypes or conditions. Several tools have been developed for comparing gene sets, most of which find Gene Ontology (GO) terms that are significantly over-represented in one gene set. However, such tools often return GO terms that are too generic or too few to be informative. Here, we present Martini, an easy-to-use tool for comparing gene sets. Martini is based, not on GO, but on keywords extracted from Medline abstracts; Martini also supports a much wider range of species than comparable tools. To evaluate Martini we created a benchmark based on the human cell cycle, and we tested several comparable tools (CoPub, FatiGO, Marmite and ProfCom). Martini had the best benchmark performance, delivering a more detailed and accurate description of function. Martini also gave best or equal performance with three other datasets (related to Arabidopsis, melanoma and ovarian cancer), suggesting that Martini represents an advance in the automated comparison of gene sets. In agreement with previous studies, our results further suggest that literature-derived keywords are a richer source of gene-function information than GO annotations. Martini is freely available at http://martini.embl.de. [less ▲]

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See detailIdentification and classification of ncRNA molecules using graph properties.
Childs, Liam; Nikoloski, Zoran; May, Patrick UL et al

in Nucleic Acids Research (2009), 37(9), 66

The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA ... [more ▼]

The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets. [less ▲]

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See detailJAIL: a structure-based interface library for macromolecules.
Gunther, Stefan; von Eichborn, Joachim; May, Patrick UL et al

in Nucleic Acids Research (2009), 37(Database issue), 338-41

The increasing number of solved macromolecules provides a solid number of 3D interfaces, if all types of molecular contacts are being considered. JAIL annotates three different kinds of macromolecular ... [more ▼]

The increasing number of solved macromolecules provides a solid number of 3D interfaces, if all types of molecular contacts are being considered. JAIL annotates three different kinds of macromolecular interfaces, those between interacting protein domains, interfaces of different protein chains and interfaces between proteins and nucleic acids. This results in a total number of about 184,000 database entries. All the interfaces can easily be identified by a detailed search form or by a hierarchical tree that describes the protein domain architectures classified by the SCOP database. Visual inspection of the interfaces is possible via an interactive protein viewer. Furthermore, large scale analyses are supported by an implemented sequential and by a structural clustering. Similar interfaces as well as non-redundant interfaces can be easily picked out. Additionally, the sequential conservation of binding sites was also included in the database and is retrievable via Jmol. A comprehensive download section allows the composition of representative data sets with user defined parameters. The huge data set in combination with various search options allow a comprehensive view on all interfaces between macromolecules included in the Protein Data Bank (PDB). The download of the data sets supports numerous further investigations in macromolecular recognition. JAIL is publicly available at http://bioinformatics.charite.de/jail. [less ▲]

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See detailMedlineRanker: flexible ranking of biomedical literature.
Fontaine, Jean-Fred; Barbosa Da Silva, Adriano UL; Schaefer, Martin et al

in Nucleic acids research (2009), 37(Web Server issue), 141-6

The biomedical literature is represented by millions of abstracts available in the Medline database. These abstracts can be queried with the PubMed interface, which provides a keyword-based Boolean search ... [more ▼]

The biomedical literature is represented by millions of abstracts available in the Medline database. These abstracts can be queried with the PubMed interface, which provides a keyword-based Boolean search engine. This approach shows limitations in the retrieval of abstracts related to very specific topics, as it is difficult for a non-expert user to find all of the most relevant keywords related to a biomedical topic. Additionally, when searching for more general topics, the same approach may return hundreds of unranked references. To address these issues, text mining tools have been developed to help scientists focus on relevant abstracts. We have implemented the MedlineRanker webserver, which allows a flexible ranking of Medline for a topic of interest without expert knowledge. Given some abstracts related to a topic, the program deduces automatically the most discriminative words in comparison to a random selection. These words are used to score other abstracts, including those from not yet annotated recent publications, which can be then ranked by relevance. We show that our tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time. MedlineRanker is free for use and is available at http://cbdm.mdc-berlin.de/tools/medlineranker. [less ▲]

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See detailSuperTarget and Matador: resources for exploring drug-target relationships
Guenther, Stefan; Kuhn, Michael; Dunkel, Mathias et al

in Nucleic Acids Research (2008), 36(SI), 919-922

The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical ... [more ▼]

The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use query interface enables the user to pose complex queries, for example to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.de. [less ▲]

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See detailTractor_DB (version 2.0): a database of regulatory interactions in gamma-proteobacterial genomes.
Perez, Abel Gonzalez; Espinosa Angarica, Vladimir UL; Vasconcelos, Ana Tereza R. et al

in Nucleic acids research (2007), 35(Database issue), 132-6

The version 2.0 of Tractor_DB is now accessible at its three international mirrors: www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br and http://www.ccg.unam.mx/tractorDB. This database contains a collection ... [more ▼]

The version 2.0 of Tractor_DB is now accessible at its three international mirrors: www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br and http://www.ccg.unam.mx/tractorDB. This database contains a collection of computationally predicted Transcription Factors' binding sites in gamma-proteobacterial genomes. These data should aid researchers in the design of microarray experiments and the interpretation of their results. They should also facilitate studies of Comparative Genomics of the regulatory networks of this group of organisms. In this paper we describe the main improvements incorporated to the database in the past year and a half which include incorporating information on the regulatory networks of 13-increasing to 30-new gamma-proteobacteria and developing a new computational strategy to complement the putative sites identified by the original weight matrix-based approach. We have also added dynamically generated navigation tabs to the navigation interfaces. Moreover, we developed a new interface that allows users to directly retrieve information on the conservation of regulatory interactions in the 30 genomes included in the database by navigating a map that represents a core of the known Escherichia coli regulatory network. [less ▲]

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See detailSYSTOMONAS — an integrated database for systems biology analysis of Pseudomonas
Choi, Claudia; Münch, Richard; Leupold, Stefan et al

in Nucleic Acids Research (2007)

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See detailTreeDet: a web server to explore sequence space.
Carro, Angel; Tress, Michael; de Juan, David et al

in Nucleic acids research (2006), 34(Web Server issue), 110-5

The TreeDet (Tree Determinant) Server is the first release of a system designed to integrate results from methods that predict functional sites in protein families. These methods take into account the ... [more ▼]

The TreeDet (Tree Determinant) Server is the first release of a system designed to integrate results from methods that predict functional sites in protein families. These methods take into account the relation between sequence conservation and evolutionary importance. TreeDet fully analyses the space of protein sequences in either user-uploaded or automatically generated multiple sequence alignments. The methods implemented in the server represent three main classes of methods for the detection of family-dependent conserved positions, a tree-based method, a correlation based method and a method that employs a principal component analyses coupled to a cluster algorithm. An additional method is provided to highlight the reliability of the position in the alignments. The server is available at http://www.pdg.cnb.uam.es/servers/treedet. [less ▲]

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See detailJProGO: a novel tool for the functional interpretation of prokaryotic microarray data using Gene Ontology information
Scheer, Maurice; Klawonn, Frank; Münch, Richard et al

in Nucleic Acids Research (2006)

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See detailRegulation of the human cyclin C gene via multiple vitamin D3-responsive regions in its promoter
Sinkkonen, Lasse UL; Malinen, Marjo; Saavalainen, Katri et al

in Nucleic Acids Research (2005), 33(8), 2440-51

The candidate human tumor suppressor gene cyclin C is a primary target of the anti-proliferative hormone 1alpha,25-dihydroxyvitamin D3 [1alpha,25(OH)2D3], but binding sites for the 1alpha,25(OH)2D3 ... [more ▼]

The candidate human tumor suppressor gene cyclin C is a primary target of the anti-proliferative hormone 1alpha,25-dihydroxyvitamin D3 [1alpha,25(OH)2D3], but binding sites for the 1alpha,25(OH)2D3 receptor (VDR), so-called 1alpha,25(OH)2D3 response elements (VDREs), have not yet been identified in the promoter of this gene. We screened various cancer cell lines by quantitative PCR and found that the 1alpha,25(OH)2D3 inducibility of cyclin C mRNA expression, in relationship with the 24-hydroxylase (CYP24) gene, was best in MCF-7 human breast cancer cells. To characterize the molecular mechanisms, we analyzed 8.4 kb of the cyclin C promoter by using chromatin immunoprecipitation assays (ChIP) with antibodies against acetylated histone 4, VDR and its partner receptor, retinoid X receptor (RXR). The histone 4 acetylation status of all 23 investigated regions of the cyclin C promoter did not change significantly in response to 1alpha,25(OH)2D3, but four independent promoter regions showed a consistent, 1alpha,25(OH)2D3-dependent association with VDR and RXR over a time period of 240 min. Combined in silico/in vitro screening identified in each of these promoter regions a VDRE and reporter gene assays confirmed their functionality. Moreover, re-ChIP assays monitored simultaneous association of VDR with RXR, coactivator, mediator and RNA polymerase II proteins on these regions. Since cyclin C protein is associated with those mediator complexes that display transcriptional repressive properties, this study contributes to the understanding of the downregulation of a number of secondary 1alpha,25(OH)2D3-responding genes. [less ▲]

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See detailTRACTOR_DB: a database of regulatory networks in gamma-proteobacterial genomes.
Gonzalez, Abel D.; Espinosa Angarica, Vladimir UL; Vasconcelos, Ana T. et al

in Nucleic acids research (2005), 33(Database issue), 98-102

Experimental data on the Escherichia coli transcriptional regulatory system has been used in the past years to predict new regulatory elements (promoters, transcription factors (TFs), TFs' binding sites ... [more ▼]

Experimental data on the Escherichia coli transcriptional regulatory system has been used in the past years to predict new regulatory elements (promoters, transcription factors (TFs), TFs' binding sites and operons) within its genome. As more genomes of gamma-proteobacteria are being sequenced, the prediction of these elements in a growing number of organisms has become more feasible, as a step towards the study of how different bacteria respond to environmental changes at the level of transcriptional regulation. In this work, we present TRACTOR_DB (TRAnscription FaCTORs' predicted binding sites in prokaryotic genomes), a relational database that contains computational predictions of new members of 74 regulons in 17 gamma-proteobacterial genomes. For these predictions we used a comparative genomics approach regarding which several proof-of-principle articles for large regulons have been published. TRACTOR_DB may be currently accessed at http://www.bioinfo.cu/Tractor_DB, http://www.tractor.lncc.br/ or at http://www.cifn.unam.mx/Computational_Genomics/tractorDB. Contact Email id is tractor@cifn.unam.mx. [less ▲]

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See detailThe subsystems approach to genome annotation and its use in the project to annotate 1000 genomes.
Overbeek, Ross; Begley, Tadhg; Butler, Ralph M. et al

in Nucleic Acids Research (2005), 33(17), 5691-702

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to ... [more ▼]

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms. [less ▲]

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See detailVisualization of individual DNA loops and a map of loop domains in the human dystrophin gene
Iarovaia, Olga V.; Bystritskiy, Andrey; Ravcheev, Dmitry UL et al

in Nucleic Acids Research (2004), 32(7), 2079-2086

The organization of the human dystrophin gene into loop domains has been studied using two different experimental approaches: excision of DNA loops mediated by nuclear matrix-bound topoisomerase II and in ... [more ▼]

The organization of the human dystrophin gene into loop domains has been studied using two different experimental approaches: excision of DNA loops mediated by nuclear matrix-bound topoisomerase II and in situ hybridization of different probes with histone- depleted nuclei (nuclear halos). Our objective was to examine if the DNA loops mapped by this biochemical approach coincide with loops visualized by microscopy. The results obtained using both approaches were in good agreement. Eight loops separated by attachment regions of different length were mapped in the upstream part (up to exon 54) of the gene by topoisomerase II-mediated excision. One of these loops was then directly visualized by in situ hybridization of the corresponding bacmid clone with nuclear halos. This is the ®rst direct demonstration that a DNA domain mapped as a loop using a biochemical approach corresponds to a loop visible on cytological preparations. The validity of this result and of the whole map of loop domains was con®rmed by in situ hybridization using probes derived from other attachment regions or loops mapped by topoisomerase II-mediated cleavage; these probes hybridized on the core or halo region, respectively, of nuclear halos. Our results demonstrate that a single transcription unit may be organized into several loops and that DNA loop attachment regions may be fairly long. Three out of four replication origins mapped in this gene co-localize with loop attachment regions, and the major deletion hot spot is harbored in an attachment region. These results strongly suggest that partitioning of genomic DNA into speci®c loops attached to a skeletal structure is a characteristic feature of eukaryotic chromosome organization in interphase. [less ▲]

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See detailPrediSi: prediction of signal peptides and their cleavage positions
Hiller, Karsten UL; Grote, Andreas; Scheer, Maurice et al

in Nucleic Acids Research (2004)

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See detailJVirGel: calculation of virtual two-dimensional protein gels
Hiller, Karsten UL; Schobert, Max; Hundertmark, Claudia et al

in Nucleic Acids Research (2003)

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See detailPRODORIC: prokaryotic database of gene regulation
Münch, Richard; Hiller, Karsten UL; Barg, Heiko et al

in Nucleic Acids Research (2003)

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See detailThe HSSP database of protein structure sequence alignments and family profiles
Dodge, C.; Schneider, Reinhard UL; Sander, C.

in Nucleic Acids Research (1998), 26(1), 313-315

HSSP (http://www.sander.embl-ebi.ac.uk/hssp/) is a derived database merging structure (3-D) and sequence (1-D) information, For each protein of known 3D structure from the Protein Data Bank (PDB), we ... [more ▼]

HSSP (http://www.sander.embl-ebi.ac.uk/hssp/) is a derived database merging structure (3-D) and sequence (1-D) information, For each protein of known 3D structure from the Protein Data Bank (PDB), we provide a multiple sequence alignment of putative homologues and a sequence profile characteristic of the protein family, centered on the known structure. The list of homologues is the result of an iterative database search in SWISS-PROT using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). The database is updated frequently, The listed putative homologues are very likely to have the same 3D structure as the PDB protein to which they have been aligned. As a result, the database not only provides aligned sequence families, but also implies secondary and tertiary structures covering 33% of all sequences in SWISS-PROT. [less ▲]

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See detailThe HSSP database of protein structure-sequence alignments
Schneider, Reinhard UL; deDaruvar, A.; Sander, C.

in Nucleic Acids Research (1997), 25(1), 226-230

HSSP is a derived database merging structural (3-D) and sequence (1-D) information. For each protein of known 3-D structure from the Protein Data Bank (PDB), the database has a multiple sequence alignment ... [more ▼]

HSSP is a derived database merging structural (3-D) and sequence (1-D) information. For each protein of known 3-D structure from the Protein Data Bank (PDB), the database has a multiple sequence alignment of all available homologues and a sequence profile characteristic of the family. The list of homologues is the result of a database search in SwissProt using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). The database is updated frequently. The listed homologues are very likely to have the same 3-D structure as the PDB protein to which they have been aligned. As a result, the database is not only a database of aligned sequence families, but also a database of implied secondary and tertiary structures covering 29% of all SwissProt-stored sequences. [less ▲]

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See detailThe HSSP database of protein structure sequence alignments
Schneider, Reinhard UL; Sander, C.

in Nucleic Acids Research (1996), 24(1), 201-205

HSSP is a derived database merging structural three dimensional (3-D) and sequence one dimensional (1-D) information. For each protein of known 3-D structure from the Protein Data Bank (PDB), the database ... [more ▼]

HSSP is a derived database merging structural three dimensional (3-D) and sequence one dimensional (1-D) information. For each protein of known 3-D structure from the Protein Data Bank (PDB), the database has a multiple sequence alignment of all available homologues and a sequence profile characteristic of the family. The list of homologues is the result of a database search in Swissprot using a position-weighted dynamic programming method for sequence profile alignment (MaxHom). The database is updated frequently. The listed homologues are very likely to have the same 3-D structure as the PDB protein to which they have been aligned. As a result, the database is not only a database of aligned sequence families, but also a database of implied secondary and tertiary structures covering 27% of all Swissprotstored sequences. [less ▲]

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See detailTHE HSSP DATABASE OF PROTEIN-STRUCTURE SEQUENCE ALIGNMENTS
SANDER, C.; Schneider, Reinhard UL

in Nucleic Acids Research (1994), 22(17), 3597-3599

HSSP (homology-derived structures of proteins) is a derived database merging structural (2-D and 3-D) and sequence information (1-D). For each protein of known 3D structure from the Protein Data Bank, the ... [more ▼]

HSSP (homology-derived structures of proteins) is a derived database merging structural (2-D and 3-D) and sequence information (1-D). For each protein of known 3D structure from the Protein Data Bank, the database has a file with all sequence homologues, properly aligned to the PDB protein. Homologues are very likely to have the same 3D structure as the PDB protein to which they have been aligned. As a result, the database is not only a database of sequence aligned sequence families, but it is also a database of implied secondary and tertiary structures. [less ▲]

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