Linear MALDI-ToF simultaneous spectrum deconvolution and baseline removal View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Vincent Picaud, Jean-Francois Giovannelli, Caroline Truntzer, Jean-Philippe Charrier, Audrey Giremus, Pierre Grangeat, Catherine Mercier

ABSTRACT

BACKGROUND: Thanks to a reasonable cost and simple sample preparation procedure, linear MALDI-ToF spectrometry is a growing technology for clinical microbiology. With appropriate spectrum databases, this technology can be used for early identification of pathogens in body fluids. However, due to the low resolution of linear MALDI-ToF instruments, robust and accurate peak picking remains a challenging task. In this context we propose a new peak extraction algorithm from raw spectrum. With this method the spectrum baseline and spectrum peaks are processed jointly. The approach relies on an additive model constituted by a smooth baseline part plus a sparse peak list convolved with a known peak shape. The model is then fitted under a Gaussian noise model. The proposed method is well suited to process low resolution spectra with important baseline and unresolved peaks. RESULTS: We developed a new peak deconvolution procedure. The paper describes the method derivation and discusses some of its interpretations. The algorithm is then described in a pseudo-code form where the required optimization procedure is detailed. For synthetic data the method is compared to a more conventional approach. The new method reduces artifacts caused by the usual two-steps procedure, baseline removal then peak extraction. Finally some results on real linear MALDI-ToF spectra are provided. CONCLUSIONS: We introduced a new method for peak picking, where peak deconvolution and baseline computation are performed jointly. On simulated data we showed that this global approach performs better than a classical one where baseline and peaks are processed sequentially. A dedicated experiment has been conducted on real spectra. In this study a collection of spectra of spiked proteins were acquired and then analyzed. Better performances of the proposed method, in term of accuracy and reproductibility, have been observed and validated by an extended statistical analysis. More... »

PAGES

123

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-018-2116-3

DOI

http://dx.doi.org/10.1186/s12859-018-2116-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103128677

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29621971


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0301", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Analytical Chemistry", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Chemical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Artifacts", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Laboratoire de l'Integration du Materiau au Systeme", 
          "id": "https://www.grid.ac/institutes/grid.462974.a", 
          "name": [
            "University of Bordeaux, IMS, UMR 5218, 33400, Talence, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Picaud", 
        "givenName": "Vincent", 
        "id": "sg:person.01106027447.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106027447.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratoire de l'Integration du Materiau au Systeme", 
          "id": "https://www.grid.ac/institutes/grid.462974.a", 
          "name": [
            "University of Bordeaux, IMS, UMR 5218, 33400, Talence, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Giovannelli", 
        "givenName": "Jean-Francois", 
        "id": "sg:person.010501316025.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010501316025.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Burgundy", 
          "id": "https://www.grid.ac/institutes/grid.5613.1", 
          "name": [
            "CLIPP, P\u00f4le de Recherche Universit\u00e9 de Bourgogne, 21000, Dijon, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Truntzer", 
        "givenName": "Caroline", 
        "id": "sg:person.01267066175.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01267066175.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "BioM\u00e9rieux (France)", 
          "id": "https://www.grid.ac/institutes/grid.424167.2", 
          "name": [
            "Technology Research Department, Innovation Unit, Marcy l\u2019\u00c9toile, bioM\u00e9rieux SA, 69280, Marcy l\u2019\u00c9toile, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Charrier", 
        "givenName": "Jean-Philippe", 
        "id": "sg:person.016632237551.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016632237551.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratoire de l'Integration du Materiau au Systeme", 
          "id": "https://www.grid.ac/institutes/grid.462974.a", 
          "name": [
            "University of Bordeaux, IMS, UMR 5218, 33400, Talence, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Giremus", 
        "givenName": "Audrey", 
        "id": "sg:person.016305433245.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016305433245.69"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institut N\u00e9el", 
          "id": "https://www.grid.ac/institutes/grid.450308.a", 
          "name": [
            "Universit\u00e9 Grenoble Alpes, 38000, Grenoble, France", 
            "CEA, LETI, MINATEC Campus, DTBS, 17 Rue des Martyrs, 38054, Grenoble, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Grangeat", 
        "givenName": "Pierre", 
        "id": "sg:person.011066771206.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011066771206.63"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Biometry and Evolutionary Biology Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.462854.9", 
          "name": [
            "Service de Biostatistique, Hospices Civils de Lyon, 69000, Lyon, France", 
            "Universit\u00e9 Lyon 1, 69100, Villeurbanne, France", 
            "CNRS UMR5558, Laboratoire de Biom\u00e9trie et Biologie \u00c9volutive, \u00c9quipe Biostatistique-Sant\u00e9, 69100, Villeurbanne, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mercier", 
        "givenName": "Catherine", 
        "id": "sg:person.011475221040.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011475221040.07"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/0168-583x(88)90063-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000800400"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0168-583x(88)90063-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000800400"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2353/jmoldx.2009.080079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001457083"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jstsp.2007.910281", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002268288"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fmicb.2015.00791", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006259825"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-17798-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009606370", 
          "https://doi.org/10.1007/978-3-642-17798-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-17798-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009606370", 
          "https://doi.org/10.1007/978-3-642-17798-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10556788.2012.656368", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016331125"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.chemolab.2004.10.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017047506"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c3an00743j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018158948"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btl355", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019595677"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-13-291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022710912", 
          "https://doi.org/10.1186/1471-2105-13-291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-13-291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022710912", 
          "https://doi.org/10.1186/1471-2105-13-291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00211-004-0569-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026272611", 
          "https://doi.org/10.1007/s00211-004-0569-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00211-004-0569-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026272611", 
          "https://doi.org/10.1007/s00211-004-0569-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bts447", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026550613"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-4073(00)00021-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028381845"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-2544-7_17", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030591905", 
          "https://doi.org/10.1007/978-1-4612-2544-7_17"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-2544-7_17", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030591905", 
          "https://doi.org/10.1007/978-1-4612-2544-7_17"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0168-9002(97)01023-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040170875"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-9-355", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041068805", 
          "https://doi.org/10.1186/1471-2105-9-355"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-10-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041575797", 
          "https://doi.org/10.1186/1471-2105-10-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-10-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041575797", 
          "https://doi.org/10.1186/1471-2105-10-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1387-3806(01)00562-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045069000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/0-387-24255-4_10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045158096", 
          "https://doi.org/10.1007/0-387-24255-4_10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.dsp.2006.01.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047529954"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pmic.200401261", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048112958"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pmic.200401261", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048112958"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac00113a006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054972687"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac00205a007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054980411"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac401048d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055004346"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac60214a047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055048783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1051/0004-6361:20047011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056934583"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/cmb.2010.0264", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059245991"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/imanum/13.3.321", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059688652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/imanum/8.1.141", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059689253"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.142909", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061155678"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/78.157290", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061228108"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0801008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062854121"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/s1052623494266365", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062883435"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/s1052623497330963", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062883648"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/000370208783412762", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065257425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/000370208783412762", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065257425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/000370210791414281", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065258049"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/000370210791414281", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065258049"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077320559", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3343/alm.2017.37.6.475", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091343579"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/9789812701626_0023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1096040154"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/bimj.201600198", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099647574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/bimj.201600198", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099647574"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "BACKGROUND: Thanks to a reasonable cost and simple sample preparation procedure, linear MALDI-ToF spectrometry is a growing technology for clinical microbiology. With appropriate spectrum databases, this technology can be used for early identification of pathogens in body fluids. However, due to the low resolution of linear MALDI-ToF instruments, robust and accurate peak picking remains a challenging task. In this context we propose a new peak extraction algorithm from raw spectrum. With this method the spectrum baseline and spectrum peaks are processed jointly. The approach relies on an additive model constituted by a smooth baseline part plus a sparse peak list convolved with a known peak shape. The model is then fitted under a Gaussian noise model. The proposed method is well suited to process low resolution spectra with important baseline and unresolved peaks.\nRESULTS: We developed a new peak deconvolution procedure. The paper describes the method derivation and discusses some of its interpretations. The algorithm is then described in a pseudo-code form where the required optimization procedure is detailed. For synthetic data the method is compared to a more conventional approach. The new method reduces artifacts caused by the usual two-steps procedure, baseline removal then peak extraction. Finally some results on real linear MALDI-ToF spectra are provided.\nCONCLUSIONS: We introduced a new method for peak picking, where peak deconvolution and baseline computation are performed jointly. On simulated data we showed that this global approach performs better than a classical one where baseline and peaks are processed sequentially. A dedicated experiment has been conducted on real spectra. In this study a collection of spectra of spiked proteins were acquired and then analyzed. Better performances of the proposed method, in term of accuracy and reproductibility, have been observed and validated by an extended statistical analysis.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s12859-018-2116-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "19"
      }
    ], 
    "name": "Linear MALDI-ToF simultaneous spectrum deconvolution and baseline removal", 
    "pagination": "123", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c8ff2be827362b2ed70a8426adeddc2a0e182dbbd85f691c511d12bca454671a"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29621971"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12859-018-2116-3"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1103128677"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12859-018-2116-3", 
      "https://app.dimensions.ai/details/publication/pub.1103128677"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:03", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000371_0000000371/records_130831_00000005.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs12859-018-2116-3"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12859-018-2116-3'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12859-018-2116-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12859-018-2116-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12859-018-2116-3'


 

This table displays all metadata directly associated to this object as RDF triples.

263 TRIPLES      21 PREDICATES      72 URIs      24 LITERALS      12 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12859-018-2116-3 schema:about N0150e334aeb2493b836449f3cdb779b3
2 N88f48aac8a56409490c75c47b6e069a4
3 N9bd4d09288c04bc1b8514d7c85c520b0
4 anzsrc-for:03
5 anzsrc-for:0301
6 schema:author Nacbaff6b532743bf975a8437c6127014
7 schema:citation sg:pub.10.1007/0-387-24255-4_10
8 sg:pub.10.1007/978-1-4612-2544-7_17
9 sg:pub.10.1007/978-3-642-17798-9
10 sg:pub.10.1007/s00211-004-0569-y
11 sg:pub.10.1186/1471-2105-10-4
12 sg:pub.10.1186/1471-2105-13-291
13 sg:pub.10.1186/1471-2105-9-355
14 https://app.dimensions.ai/details/publication/pub.1077320559
15 https://doi.org/10.1002/bimj.201600198
16 https://doi.org/10.1002/pmic.200401261
17 https://doi.org/10.1016/0168-583x(88)90063-8
18 https://doi.org/10.1016/j.chemolab.2004.10.003
19 https://doi.org/10.1016/j.dsp.2006.01.006
20 https://doi.org/10.1016/s0022-4073(00)00021-2
21 https://doi.org/10.1016/s0168-9002(97)01023-1
22 https://doi.org/10.1016/s1387-3806(01)00562-0
23 https://doi.org/10.1021/ac00113a006
24 https://doi.org/10.1021/ac00205a007
25 https://doi.org/10.1021/ac401048d
26 https://doi.org/10.1021/ac60214a047
27 https://doi.org/10.1039/c3an00743j
28 https://doi.org/10.1051/0004-6361:20047011
29 https://doi.org/10.1080/10556788.2012.656368
30 https://doi.org/10.1089/cmb.2010.0264
31 https://doi.org/10.1093/bioinformatics/btl355
32 https://doi.org/10.1093/bioinformatics/bts447
33 https://doi.org/10.1093/imanum/13.3.321
34 https://doi.org/10.1093/imanum/8.1.141
35 https://doi.org/10.1109/34.142909
36 https://doi.org/10.1109/78.157290
37 https://doi.org/10.1109/jstsp.2007.910281
38 https://doi.org/10.1137/0801008
39 https://doi.org/10.1137/s1052623494266365
40 https://doi.org/10.1137/s1052623497330963
41 https://doi.org/10.1142/9789812701626_0023
42 https://doi.org/10.1366/000370208783412762
43 https://doi.org/10.1366/000370210791414281
44 https://doi.org/10.2353/jmoldx.2009.080079
45 https://doi.org/10.3343/alm.2017.37.6.475
46 https://doi.org/10.3389/fmicb.2015.00791
47 schema:datePublished 2018-12
48 schema:datePublishedReg 2018-12-01
49 schema:description BACKGROUND: Thanks to a reasonable cost and simple sample preparation procedure, linear MALDI-ToF spectrometry is a growing technology for clinical microbiology. With appropriate spectrum databases, this technology can be used for early identification of pathogens in body fluids. However, due to the low resolution of linear MALDI-ToF instruments, robust and accurate peak picking remains a challenging task. In this context we propose a new peak extraction algorithm from raw spectrum. With this method the spectrum baseline and spectrum peaks are processed jointly. The approach relies on an additive model constituted by a smooth baseline part plus a sparse peak list convolved with a known peak shape. The model is then fitted under a Gaussian noise model. The proposed method is well suited to process low resolution spectra with important baseline and unresolved peaks. RESULTS: We developed a new peak deconvolution procedure. The paper describes the method derivation and discusses some of its interpretations. The algorithm is then described in a pseudo-code form where the required optimization procedure is detailed. For synthetic data the method is compared to a more conventional approach. The new method reduces artifacts caused by the usual two-steps procedure, baseline removal then peak extraction. Finally some results on real linear MALDI-ToF spectra are provided. CONCLUSIONS: We introduced a new method for peak picking, where peak deconvolution and baseline computation are performed jointly. On simulated data we showed that this global approach performs better than a classical one where baseline and peaks are processed sequentially. A dedicated experiment has been conducted on real spectra. In this study a collection of spectra of spiked proteins were acquired and then analyzed. Better performances of the proposed method, in term of accuracy and reproductibility, have been observed and validated by an extended statistical analysis.
50 schema:genre research_article
51 schema:inLanguage en
52 schema:isAccessibleForFree true
53 schema:isPartOf N8552fd92754a47789ec2e5fc4c5248da
54 Na17bb9e7ec604f8494d17fbd3b885b36
55 sg:journal.1023786
56 schema:name Linear MALDI-ToF simultaneous spectrum deconvolution and baseline removal
57 schema:pagination 123
58 schema:productId N915daaf21c814b3696f6bb1a4ac54b6d
59 Ndfb37ad8417a487e83d0b4f9f11bed4d
60 Ne1edb66d601144e896bb86c2e08c9a9e
61 Nf4f812b828e34876883a52bae41c1d87
62 Nfad30a89b38f453d9fa5bff466164cd7
63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103128677
64 https://doi.org/10.1186/s12859-018-2116-3
65 schema:sdDatePublished 2019-04-11T14:03
66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
67 schema:sdPublisher Nc768f67f24834862b8d512628bdedeab
68 schema:url https://link.springer.com/10.1186%2Fs12859-018-2116-3
69 sgo:license sg:explorer/license/
70 sgo:sdDataset articles
71 rdf:type schema:ScholarlyArticle
72 N0150e334aeb2493b836449f3cdb779b3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
73 schema:name Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
74 rdf:type schema:DefinedTerm
75 N2197b28f515847a6bdae7000e33dfb1a rdf:first sg:person.01267066175.55
76 rdf:rest N234e4df47049494693300169b5a5dee1
77 N234e4df47049494693300169b5a5dee1 rdf:first sg:person.016632237551.79
78 rdf:rest N4de8b0d41d974706bc1b065d8bf096dd
79 N4de8b0d41d974706bc1b065d8bf096dd rdf:first sg:person.016305433245.69
80 rdf:rest Nb611e232a0a54606a2ec474f7d1d5e06
81 N8552fd92754a47789ec2e5fc4c5248da schema:volumeNumber 19
82 rdf:type schema:PublicationVolume
83 N863ced2efdf944469751a8b4235a94e8 rdf:first sg:person.011475221040.07
84 rdf:rest rdf:nil
85 N88f48aac8a56409490c75c47b6e069a4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
86 schema:name Algorithms
87 rdf:type schema:DefinedTerm
88 N915daaf21c814b3696f6bb1a4ac54b6d schema:name nlm_unique_id
89 schema:value 100965194
90 rdf:type schema:PropertyValue
91 N9bd4d09288c04bc1b8514d7c85c520b0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
92 schema:name Artifacts
93 rdf:type schema:DefinedTerm
94 Na17bb9e7ec604f8494d17fbd3b885b36 schema:issueNumber 1
95 rdf:type schema:PublicationIssue
96 Nacbaff6b532743bf975a8437c6127014 rdf:first sg:person.01106027447.43
97 rdf:rest Nf39361e48d9c446fb252878cc320984a
98 Nb611e232a0a54606a2ec474f7d1d5e06 rdf:first sg:person.011066771206.63
99 rdf:rest N863ced2efdf944469751a8b4235a94e8
100 Nc768f67f24834862b8d512628bdedeab schema:name Springer Nature - SN SciGraph project
101 rdf:type schema:Organization
102 Ndfb37ad8417a487e83d0b4f9f11bed4d schema:name dimensions_id
103 schema:value pub.1103128677
104 rdf:type schema:PropertyValue
105 Ne1edb66d601144e896bb86c2e08c9a9e schema:name doi
106 schema:value 10.1186/s12859-018-2116-3
107 rdf:type schema:PropertyValue
108 Nf39361e48d9c446fb252878cc320984a rdf:first sg:person.010501316025.14
109 rdf:rest N2197b28f515847a6bdae7000e33dfb1a
110 Nf4f812b828e34876883a52bae41c1d87 schema:name pubmed_id
111 schema:value 29621971
112 rdf:type schema:PropertyValue
113 Nfad30a89b38f453d9fa5bff466164cd7 schema:name readcube_id
114 schema:value c8ff2be827362b2ed70a8426adeddc2a0e182dbbd85f691c511d12bca454671a
115 rdf:type schema:PropertyValue
116 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
117 schema:name Chemical Sciences
118 rdf:type schema:DefinedTerm
119 anzsrc-for:0301 schema:inDefinedTermSet anzsrc-for:
120 schema:name Analytical Chemistry
121 rdf:type schema:DefinedTerm
122 sg:journal.1023786 schema:issn 1471-2105
123 schema:name BMC Bioinformatics
124 rdf:type schema:Periodical
125 sg:person.010501316025.14 schema:affiliation https://www.grid.ac/institutes/grid.462974.a
126 schema:familyName Giovannelli
127 schema:givenName Jean-Francois
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010501316025.14
129 rdf:type schema:Person
130 sg:person.01106027447.43 schema:affiliation https://www.grid.ac/institutes/grid.462974.a
131 schema:familyName Picaud
132 schema:givenName Vincent
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106027447.43
134 rdf:type schema:Person
135 sg:person.011066771206.63 schema:affiliation https://www.grid.ac/institutes/grid.450308.a
136 schema:familyName Grangeat
137 schema:givenName Pierre
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011066771206.63
139 rdf:type schema:Person
140 sg:person.011475221040.07 schema:affiliation https://www.grid.ac/institutes/grid.462854.9
141 schema:familyName Mercier
142 schema:givenName Catherine
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011475221040.07
144 rdf:type schema:Person
145 sg:person.01267066175.55 schema:affiliation https://www.grid.ac/institutes/grid.5613.1
146 schema:familyName Truntzer
147 schema:givenName Caroline
148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01267066175.55
149 rdf:type schema:Person
150 sg:person.016305433245.69 schema:affiliation https://www.grid.ac/institutes/grid.462974.a
151 schema:familyName Giremus
152 schema:givenName Audrey
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016305433245.69
154 rdf:type schema:Person
155 sg:person.016632237551.79 schema:affiliation https://www.grid.ac/institutes/grid.424167.2
156 schema:familyName Charrier
157 schema:givenName Jean-Philippe
158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016632237551.79
159 rdf:type schema:Person
160 sg:pub.10.1007/0-387-24255-4_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045158096
161 https://doi.org/10.1007/0-387-24255-4_10
162 rdf:type schema:CreativeWork
163 sg:pub.10.1007/978-1-4612-2544-7_17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030591905
164 https://doi.org/10.1007/978-1-4612-2544-7_17
165 rdf:type schema:CreativeWork
166 sg:pub.10.1007/978-3-642-17798-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009606370
167 https://doi.org/10.1007/978-3-642-17798-9
168 rdf:type schema:CreativeWork
169 sg:pub.10.1007/s00211-004-0569-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1026272611
170 https://doi.org/10.1007/s00211-004-0569-y
171 rdf:type schema:CreativeWork
172 sg:pub.10.1186/1471-2105-10-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041575797
173 https://doi.org/10.1186/1471-2105-10-4
174 rdf:type schema:CreativeWork
175 sg:pub.10.1186/1471-2105-13-291 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022710912
176 https://doi.org/10.1186/1471-2105-13-291
177 rdf:type schema:CreativeWork
178 sg:pub.10.1186/1471-2105-9-355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041068805
179 https://doi.org/10.1186/1471-2105-9-355
180 rdf:type schema:CreativeWork
181 https://app.dimensions.ai/details/publication/pub.1077320559 schema:CreativeWork
182 https://doi.org/10.1002/bimj.201600198 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099647574
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1002/pmic.200401261 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048112958
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1016/0168-583x(88)90063-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000800400
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1016/j.chemolab.2004.10.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017047506
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1016/j.dsp.2006.01.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047529954
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1016/s0022-4073(00)00021-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028381845
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1016/s0168-9002(97)01023-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040170875
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1016/s1387-3806(01)00562-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045069000
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1021/ac00113a006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054972687
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1021/ac00205a007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054980411
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1021/ac401048d schema:sameAs https://app.dimensions.ai/details/publication/pub.1055004346
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1021/ac60214a047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055048783
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1039/c3an00743j schema:sameAs https://app.dimensions.ai/details/publication/pub.1018158948
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1051/0004-6361:20047011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056934583
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1080/10556788.2012.656368 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016331125
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1089/cmb.2010.0264 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059245991
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1093/bioinformatics/btl355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019595677
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1093/bioinformatics/bts447 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026550613
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1093/imanum/13.3.321 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059688652
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1093/imanum/8.1.141 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059689253
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1109/34.142909 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061155678
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1109/78.157290 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061228108
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1109/jstsp.2007.910281 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002268288
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1137/0801008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062854121
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1137/s1052623494266365 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062883435
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1137/s1052623497330963 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062883648
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1142/9789812701626_0023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1096040154
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1366/000370208783412762 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065257425
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1366/000370210791414281 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065258049
239 rdf:type schema:CreativeWork
240 https://doi.org/10.2353/jmoldx.2009.080079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001457083
241 rdf:type schema:CreativeWork
242 https://doi.org/10.3343/alm.2017.37.6.475 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091343579
243 rdf:type schema:CreativeWork
244 https://doi.org/10.3389/fmicb.2015.00791 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006259825
245 rdf:type schema:CreativeWork
246 https://www.grid.ac/institutes/grid.424167.2 schema:alternateName BioMérieux (France)
247 schema:name Technology Research Department, Innovation Unit, Marcy l’Étoile, bioMérieux SA, 69280, Marcy l’Étoile, France
248 rdf:type schema:Organization
249 https://www.grid.ac/institutes/grid.450308.a schema:alternateName Institut Néel
250 schema:name CEA, LETI, MINATEC Campus, DTBS, 17 Rue des Martyrs, 38054, Grenoble, France
251 Université Grenoble Alpes, 38000, Grenoble, France
252 rdf:type schema:Organization
253 https://www.grid.ac/institutes/grid.462854.9 schema:alternateName Biometry and Evolutionary Biology Laboratory
254 schema:name CNRS UMR5558, Laboratoire de Biométrie et Biologie Évolutive, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
255 Service de Biostatistique, Hospices Civils de Lyon, 69000, Lyon, France
256 Université Lyon 1, 69100, Villeurbanne, France
257 rdf:type schema:Organization
258 https://www.grid.ac/institutes/grid.462974.a schema:alternateName Laboratoire de l'Integration du Materiau au Systeme
259 schema:name University of Bordeaux, IMS, UMR 5218, 33400, Talence, France
260 rdf:type schema:Organization
261 https://www.grid.ac/institutes/grid.5613.1 schema:alternateName University of Burgundy
262 schema:name CLIPP, Pôle de Recherche Université de Bourgogne, 21000, Dijon, France
263 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...