scholarly journals Combining High‐Throughput Structure Determination and Enzyme Characterization for Functional Annotation of a Large Enzyme Superfamily

2010 ◽  
Vol 24 (S1) ◽  
Author(s):  
Stephen Kevin Burley
Keyword(s):  
High Throughput ◽  
Structure Determination ◽  
Functional Annotation ◽  
Enzyme Characterization ◽  
Enzyme Superfamily

Molecular replacement and high-throughput structure determination

2002 ◽  
Vol 217 (12) ◽  
Author(s):  
J. Navaza ◽  
P. M. Alzari
Keyword(s):  
High Throughput ◽  
Structure Determination ◽  
Solution Space ◽  
Molecular Replacement ◽  
Fast Rotation ◽  
The Way

AbstractWe describe the philosophy of the MR method as implemented in the AMoRe package. Fast rotation and translation functions are first used to obtain a meaningful sampling of solution space, whose elements are subsequently assessed by using more robust criteria. The introduction of fast and accurate algorithms for screening a large number of possible solutions opened the way to automation, thus bringing MR methods to the realm of high-throughput structure determination. Selected examples are discussed to illustrate specific aspects of the method.

scholarly journals High-throughput functional annotation and data mining with the Blast2GO suite

2008 ◽  
Vol 36 (10) ◽  
pp. 3420-3435 ◽  
Author(s):  
S. Gotz ◽  
J. M. Garcia-Gomez ◽  
J. Terol ◽  
T. D. Williams ◽  
S. H. Nagaraj ◽  
...  
Keyword(s):  
Data Mining ◽  
High Throughput ◽  
Functional Annotation

scholarly journals Parameter-space screening: a powerful tool for high-throughput crystal structure determination. Corrigendum

2005 ◽  
Vol 61 (9) ◽  
pp. 1311-1311 ◽  
Author(s):  
Zhi-Jie Liu ◽  
Dawei Lin ◽  
Wolfram Tempel ◽  
Jeremy L. Praissman ◽  
John P. Rose ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Parameter Space ◽  
High Throughput ◽  
Structure Determination ◽  
Crystal Structure Determination

scholarly journals Fast iodide-SAD phasing for high-throughput membrane protein structure determination

2017 ◽  
Vol 3 (5) ◽  
pp. e1602952 ◽  
Author(s):  
Igor Melnikov ◽  
Vitaly Polovinkin ◽  
Kirill Kovalev ◽  
Ivan Gushchin ◽  
Mikhail Shevtsov ◽  
...  
Keyword(s):  
Protein Structure ◽  
Membrane Protein ◽  
High Throughput ◽  
Structure Determination ◽  
Protein Structure Determination ◽  
Membrane Protein Structure ◽  
Sad Phasing ◽  
Membrane Protein Structure Determination

scholarly journals MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Carlos A. Ruiz-Perez ◽  
Roth E. Conrad ◽  
Konstantinos T. Konstantinidis
Keyword(s):  
High Throughput ◽  
Functional Annotation ◽  
High Throughput Sequencing ◽  
Gene Annotation ◽  
Single Cells ◽  
Computationally Efficient ◽  
High Throughput Analysis ◽  
Microbial Genomes ◽  
Reference Protein ◽  
User Friendly

Abstract Background High-throughput sequencing has increased the number of available microbial genomes recovered from isolates, single cells, and metagenomes. Accordingly, fast and comprehensive functional gene annotation pipelines are needed to analyze and compare these genomes. Although several approaches exist for genome annotation, these are typically not designed for easy incorporation into analysis pipelines, do not combine results from different annotation databases or offer easy-to-use summaries of metabolic reconstructions, and typically require large amounts of computing power for high-throughput analysis not available to the average user. Results Here, we introduce MicrobeAnnotator, a fully automated, easy-to-use pipeline for the comprehensive functional annotation of microbial genomes that combines results from several reference protein databases and returns the matching annotations together with key metadata such as the interlinked identifiers of matching reference proteins from multiple databases [KEGG Orthology (KO), Enzyme Commission (E.C.), Gene Ontology (GO), Pfam, and InterPro]. Further, the functional annotations are summarized into Kyoto Encyclopedia of Genes and Genomes (KEGG) modules as part of a graphical output (heatmap) that allows the user to quickly detect differences among (multiple) query genomes and cluster the genomes based on their metabolic similarity. MicrobeAnnotator is implemented in Python 3 and is freely available under an open-source Artistic License 2.0 from https://github.com/cruizperez/MicrobeAnnotator. Conclusions We demonstrated the capabilities of MicrobeAnnotator by annotating 100 Escherichia coli and 78 environmental Candidate Phyla Radiation (CPR) bacterial genomes and comparing the results to those of other popular tools. We showed that the use of multiple annotation databases allows MicrobeAnnotator to recover more annotations per genome compared to faster tools that use reduced databases and is computationally efficient for use in personal computers. The output of MicrobeAnnotator can be easily incorporated into other analysis pipelines while the results of other annotation tools can be seemingly incorporated into MicrobeAnnotator to generate summary plots.

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