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URL: http://bioinformatics.biol.uoa.gr/PRED-LIPO/
Proper Citation: PRED-LIPO (RRID:SCR_006187)
Description: A web tool using the Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides.
Abbreviations: PRED-LIPO
Synonyms: PRED-LIPO: Prediction of Lipoprotein and Secretory Signal Peptides in Gram-positive Bacteria with Hidden Markov Models
Resource Type: analysis service resource, data analysis service, production service resource, service resource
Defining Citation: PMID:19367716
Keywords: hidden markov model, lipoprotein signal peptide, gram-positive bacteria, lipoprotein, prediction, peptide, protein, signal peptide, bio.tools
Funding: National Scholarships Foundation of Greece
Availability: Free
Resource Name: PRED-LIPO
Resource ID: SCR_006187
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University of Athens Biophysics and Bioinformatics Laboratory |
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Source: SciCrunch Registry (RRID:SCR_005400)
Description: Interactive portal for finding and submitting biomedical resources. Resources within SciCrunch have assigned RRIDs which are used to cite resources in scientific manuscripts. SciCrunch Registry, formerly NIF Registry, provides resources catalog. Allows to add new resources. Allows edit existing resources after registration. Curators are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping contents current.
URL: https://rrid.site/rin/sources/SCR_005400
The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
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