ArachnoServer 3.0: An online resource for automated discovery, analysis and annotation of spider toxins

S S Pineda; P A Chaumeil; A Kunert; Q Kaas; M W C Thang; L Le; M Nuhn; Volker Herzig; N J Saez; B Cristofori-Armstrong; R Anangi; S Senff; D Gorse; G F King

doi:10.1093/bioinformatics/btx661

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ArachnoServer 3.0: An online resource for automated discovery, analysis and annotation of spider toxins

Journal article

Peer reviewed

ArachnoServer 3.0: An online resource for automated discovery, analysis and annotation of spider toxins

S S Pineda, P A Chaumeil, A Kunert, Q Kaas, M W C Thang, L Le, M Nuhn, Volker Herzig, N J Saez, B Cristofori-Armstrong, …

Bioinformatics, Vol.34(6), pp.1074-1076

2018

DOI: https://doi.org/10.1093/bioinformatics/btx661

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Abstract

ArachnoServer is a manually curated database that consolidates information on the sequence, structure, function and pharmacology of spider-venom toxins. Although spider venoms are complex chemical arsenals, the primary constituents are small disulfide-bridged peptides that target neuronal ion channels and receptors. Due to their high potency and selectivity, these peptides have been developed as pharmacological tools, bioinsecticides and drug leads. A new version of ArachnoServer (v3.0) has been developed that includes a bioinformatics pipeline for automated detection and analysis of peptide toxin transcripts in assembled venom-gland transcriptomes. ArachnoServer v3.0 was updated with the latest sequence, structure and functional data, the search-by-mass feature has been enhanced, and toxin cards provide additional information about each mature toxin. Availability and implementation http://arachnoserver.org Contact support@arachnoserver.org Supplementary informationSupplementary dataare available at Bioinformatics online.

Details

Title: ArachnoServer 3.0: An online resource for automated discovery, analysis and annotation of spider toxins
Authors: S S Pineda (Corresponding Author) - University of Queensland
P A Chaumeil (Author) - University of Queensland
A Kunert (Author) - University of Queensland
Q Kaas (Author) - University of Queensland
M W C Thang (Author) - University of Queensland
L Le (Author) - University of Queensland
M Nuhn (Author) - University of Queensland
Volker Herzig (Author) - University of Queensland
N J Saez (Author) - University of Queensland
B Cristofori-Armstrong (Author) - University of Queensland
R Anangi (Author) - University of Queensland
S Senff (Author) - University of Queensland
D Gorse (Author) - University of Queensland
G F King (Corresponding Author) - University of Queensland
Publication details: Bioinformatics, Vol.34(6), pp.1074-1076
Publisher: Oxford University Press
Date published: 2018
DOI: 10.1093/bioinformatics/btx661
ISSN: 1367-4803
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
Language: English
Record Identifier: 99450960702621
Output Type: Journal article

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