Presentation Open Access
Bittremieux, Wout;
Meysman, Pieter;
Noble, William Stafford;
Laukens, Kris
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="DOI">10.5281/zenodo.1319036</identifier> <creators> <creator> <creatorName>Bittremieux, Wout</creatorName> <givenName>Wout</givenName> <familyName>Bittremieux</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3105-1359</nameIdentifier> <affiliation>University of Washington</affiliation> </creator> <creator> <creatorName>Meysman, Pieter</creatorName> <givenName>Pieter</givenName> <familyName>Meysman</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5903-633X</nameIdentifier> <affiliation>University of Antwerp</affiliation> </creator> <creator> <creatorName>Noble, William Stafford</creatorName> <givenName>William Stafford</givenName> <familyName>Noble</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7283-4715</nameIdentifier> <affiliation>University of Washington</affiliation> </creator> <creator> <creatorName>Laukens, Kris</creatorName> <givenName>Kris</givenName> <familyName>Laukens</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8217-2564</nameIdentifier> <affiliation>University of Antwerp</affiliation> </creator> </creators> <titles> <title>Fast open modification spectral library searching through approximate nearest neighbor indexing</title> </titles> <publisher>Zenodo</publisher> <publicationYear>2018</publicationYear> <dates> <date dateType="Issued">2018-07-23</date> </dates> <resourceType resourceTypeGeneral="Text">Presentation</resourceType> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1319036</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsDocumentedBy">10.1101/326173</relatedIdentifier> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1319035</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="https://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons Attribution Share Alike 4.0 International</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract"><p>Open modification search (OMS) is a powerful search strategy that identifies peptides carrying any type of modification by allowing a modified spectrum to match against its unmodified variant by using a very wide precursor mass window. A drawback of this strategy, however, is that it leads to a large increase in search time. Although performing an open search can be done using existing spectral library search engines by simply setting a wide precursor mass window, none of these tools have been optimized for OMS, leading to excessive runtimes and suboptimal identification results.</p> <p>Here we present the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. This approach is combined with a cascade search strategy to maximize the number of identified unmodified and modified spectra while strictly controlling the false discovery rate, as well as a shifted dot product score to sensitively match modified spectra to their unmodified counterparts.</p> <p>ANN-SoLo outperforms the state-of-the-art SpectraST spectral library search engine both in terms of speed and the number of identifications. On a previously published human cell line data set, ANN-SoLo confidently identifies 40% more spectra than SpectraST while achieving a speedup of an order of magnitude.</p> <p>ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo.</p></description> </descriptions> </resource>
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