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288
Pfam protein families database
- Nucleic Acids Research, 2008, 36(Database issue): D281–D288
"... Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenP ..."
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Cited by 771 (13 self)
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Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenPept and on sequences from selected metage-nomics projects. Pfam is available on the web from the consortium members using a new, consistent and improved website design in the UK
CDD: conserved domains and protein three-dimensional structure. Nucleic Acids Res 41: D348–D352
, 2013
"... CDD, the Conserved Domain Database, is part of NCBI’s Entrez query and retrieval system and is also accessible via ..."
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Cited by 104 (0 self)
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CDD, the Conserved Domain Database, is part of NCBI’s Entrez query and retrieval system and is also accessible via
Rfam 11.0: 10 years of RNA families
- Nucleic Acids Res
, 2013
"... The Rfam database (available via the website at ..."
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PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res
, 1093
"... With the aim to provide a resource for functional and evolutionary study of plant transcription factors (TFs), we updated the plant TF database PlantTFDB to version 3.0 ..."
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Cited by 36 (0 self)
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With the aim to provide a resource for functional and evolutionary study of plant transcription factors (TFs), we updated the plant TF database PlantTFDB to version 3.0
A large-scale evaluation of computational protein function prediction.
- Nat. Methods
, 2013
"... AnAlysis nAture methods | ADVANCE ONLINE PUBLICATION | Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. if computational predictions are to be relied upon, ..."
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Cited by 31 (2 self)
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AnAlysis nAture methods | ADVANCE ONLINE PUBLICATION | Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. if computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from organisms. two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools. The accurate annotation of protein function is key to understanding life at the molecular level and has great biomedical and pharmaceutical implications. However, with its inherent difficulty and expense, experimental characterization of function cannot scale up to accommodate the vast amount of sequence data already available 1 . The computational annotation of protein function has therefore emerged as a problem at the forefront of computational and molecular biology. Many solutions have been proposed in the last four decades 2-10 , yet the task of computational functional inference in a laboratory often relies on traditional approaches such as identifying domains or finding Basic Local Alignment Search Tool (BLAST) 11 hits among proteins with experimentally determined function. Recently, the availability of genomic-level sequence information for thousands of species, coupled with massive high-throughput experimental data, has created new opportunities for function prediction. A large number of methods have been proposed to exploit these data, including function prediction from amino acid sequence
eggNOG v4.0: nested orthology inference across 3686 organisms
- Nucleic Acids Res
, 2014
"... With the increasing availability of various ‘omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at ..."
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Cited by 29 (6 self)
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With the increasing availability of various ‘omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at
The eukaryotic linear motif resource ELM: 10 years and counting. Nucleic Acids Res 2014;42:D259–66
"... The eukaryotic linear motif (ELM ..."
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dcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more. Nucleic Acids Res
, 2012
"... We present ‘dcGO ’ ..."
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PhylomeDB v4: zooming into the plurality of evolutionary histories of a genome
- Nucleic Acids Res
, 2014
"... Phylogenetic trees representing the evolutionary relationships of homologous genes are the entry point for many evolutionary analyses. For instance, the use of a phylogenetic tree can aid in the infer-ence of orthology and paralogy relationships, and in the detection of relevant evolutionary events ..."
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Cited by 16 (4 self)
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Phylogenetic trees representing the evolutionary relationships of homologous genes are the entry point for many evolutionary analyses. For instance, the use of a phylogenetic tree can aid in the infer-ence of orthology and paralogy relationships, and in the detection of relevant evolutionary events such as gene family expansions and contractions, hori-zontal gene transfer, recombination or incomplete lineage sorting. Similarly, given the plurality of evo-lutionary histories among genes encoded in a given genome, there is a need for the combined analysis of genome-wide collections of phylogenetic trees (phylomes). Here, we introduce a new release of PhylomeDB
TIGRFAMs and genome properties in 2013
- Nucleic Acids Res
, 2013
"... TIGRFAMs, available online at ..."
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