Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Jeremy A Miller, Vilas Menon, Jeff Goldy, Ajamete Kaykas, Chang-Kyu Lee, Kimberly A Smith, Elaine H Shen, John W Phillips, Ed S Lein, Mike J Hawrylycz

ABSTRACT

BACKGROUND: High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking. RESULTS: To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform. CONCLUSIONS: Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories. More... »

PAGES

154

References to SciGraph publications

  • 2012-12. A normalization strategy for comparing tag count data in ALGORITHMS FOR MOLECULAR BIOLOGY
  • 2012-12. A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease in BMC MEDICAL GENOMICS
  • 2009-04. A network view of disease and compound screening in NATURE REVIEWS DRUG DISCOVERY
  • 2011-12. Strategies for aggregating gene expression data: The collapseRows R function in BMC BIOINFORMATICS
  • 2009-01. Target Identification for CNS Diseases by Transcriptional Profiling in NEUROPSYCHOPHARMACOLOGY
  • 2012-12. RNA-seq and microarray complement each other in transcriptome profiling in BMC GENOMICS
  • 2005-05. Multiple-laboratory comparison of microarray platforms in NATURE METHODS
  • 2010-03. RNA-Seq analysis to capture the transcriptome landscape of a single cell in NATURE PROTOCOLS
  • 2004-10. DNA-microarray analysis of brain cancer: molecular classification for therapy in NATURE REVIEWS NEUROSCIENCE
  • 2011-06. Computational methods for transcriptome annotation and quantification using RNA-seq in NATURE METHODS
  • 2008-03. Direct multiplexed measurement of gene expression with color-coded probe pairs in NATURE BIOTECHNOLOGY
  • 2010-12. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments in BMC BIOINFORMATICS
  • 2012-09. An anatomically comprehensive atlas of the adult human brain transcriptome in NATURE
  • 2010-12. A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling in BMC GENOMICS
  • 2008-09. Quantitative methods for genome-scale analysis of in situ hybridization and correlation with microarray data in GENOME BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2164-15-154

    DOI

    http://dx.doi.org/10.1186/1471-2164-15-154

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1035031118

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24564186


    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/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Brain", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Cluster Analysis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computational Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression Profiling", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "High-Throughput Nucleotide Sequencing", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Neocortex", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Oligonucleotide Array Sequence Analysis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Reproducibility of Results", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sequence Analysis, RNA", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Transcriptome", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Miller", 
            "givenName": "Jeremy A", 
            "id": "sg:person.01016772265.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01016772265.00"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Menon", 
            "givenName": "Vilas", 
            "id": "sg:person.0751677712.41", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751677712.41"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Goldy", 
            "givenName": "Jeff", 
            "id": "sg:person.01162106766.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01162106766.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kaykas", 
            "givenName": "Ajamete", 
            "id": "sg:person.01023220540.11", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023220540.11"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Chang-Kyu", 
            "id": "sg:person.0737532665.63", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0737532665.63"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Smith", 
            "givenName": "Kimberly A", 
            "id": "sg:person.011413554527.71", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011413554527.71"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shen", 
            "givenName": "Elaine H", 
            "id": "sg:person.0603756261.83", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0603756261.83"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Phillips", 
            "givenName": "John W", 
            "id": "sg:person.01312472674.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312472674.51"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lein", 
            "givenName": "Ed S", 
            "id": "sg:person.013277307467.70", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013277307467.70"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allen Institute for Brain Science", 
              "id": "https://www.grid.ac/institutes/grid.417881.3", 
              "name": [
                "Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hawrylycz", 
            "givenName": "Mike J", 
            "id": "sg:person.011061777727.47", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011061777727.47"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/nrd2826", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000699125", 
              "https://doi.org/10.1038/nrd2826"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrd2826", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000699125", 
              "https://doi.org/10.1038/nrd2826"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0400782101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002732260"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/npp.2008.172", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003610244", 
              "https://doi.org/10.1038/npp.2008.172"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2009.236", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004430683", 
              "https://doi.org/10.1038/nprot.2009.236"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/30.1.207", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005297170"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0605938103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005680522"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrn1518", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006061461", 
              "https://doi.org/10.1038/nrn1518"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrn1518", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006061461", 
              "https://doi.org/10.1038/nrn1518"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0308512100", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007985703"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-11-282", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008198713", 
              "https://doi.org/10.1186/1471-2164-11-282"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.1613", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009798986", 
              "https://doi.org/10.1038/nmeth.1613"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btp120", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012425816"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ygeno.2004.01.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012946204"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-12-322", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013163514", 
              "https://doi.org/10.1186/1471-2105-12-322"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/biostatistics/kxj037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016217055"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-13-629", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017696038", 
              "https://doi.org/10.1186/1471-2164-13-629"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gks1048", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018129858"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.celrep.2012.08.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019883673"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1755-8794-5-28", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020484619", 
              "https://doi.org/10.1186/1755-8794-5-28"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.112683499", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027699267"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btq643", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030748565"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1748-7188-7-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031497141", 
              "https://doi.org/10.1186/1748-7188-7-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2008-9-1-r23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032727814", 
              "https://doi.org/10.1186/gb-2008-9-1-r23"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.96.12.6745", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033514193"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tins.2012.09.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034519187"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0050986", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034642791"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1319700110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037851904"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth756", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039053552", 
              "https://doi.org/10.1038/nmeth756"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth756", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039053552", 
              "https://doi.org/10.1038/nmeth756"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gni179", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040526112"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.286.5439.531", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042995627"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2009.03.016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043922138"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymeth.2009.03.016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043922138"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11405", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044384575", 
              "https://doi.org/10.1038/nature11405"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.1530509100", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044620917"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.012025199", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044815609"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.121095.111", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044818605"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btp692", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045138418"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neuron.2009.03.027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045724594"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.079558.108", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045837493"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkn889", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048226122"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0051013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050588975"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fnins.2011.00098", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052748704"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-11-94", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053091615", 
              "https://doi.org/10.1186/1471-2105-11-94"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt1385", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053259870", 
              "https://doi.org/10.1038/nbt1385"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1535-6108(02)00030-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053589488"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2014-12", 
        "datePublishedReg": "2014-12-01", 
        "description": "BACKGROUND: High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking.\nRESULTS: To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform.\nCONCLUSIONS: Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/1471-2164-15-154", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1023790", 
            "issn": [
              "1471-2164"
            ], 
            "name": "BMC Genomics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "15"
          }
        ], 
        "name": "Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq", 
        "pagination": "154", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "a6e0184ccb20914e91c57d607ada0a4c7197426ef233ea41d685e95a8b6deb5b"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "24564186"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "100965258"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/1471-2164-15-154"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1035031118"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/1471-2164-15-154", 
          "https://app.dimensions.ai/details/publication/pub.1035031118"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T22:30", 
        "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/v90/0000000001_0000000264/records_8690_00000506.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1186%2F1471-2164-15-154"
      }
    ]
     

    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.10.1186/1471-2164-15-154'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-154'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-154'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-154'


     

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

    323 TRIPLES      21 PREDICATES      84 URIs      33 LITERALS      21 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/1471-2164-15-154 schema:about N1a5e7f5984124f7a9d70d89273c8054b
    2 N3b76524c292948e98f52144ad5d9de01
    3 N3ccd515767524c35ab7dc1db37730f5c
    4 N4eee80c644ed4801afa90fac3adb2769
    5 N86b916c5802a41bf9ad17f208b4063a2
    6 N94ba903e67ab44c38b42fa70dde7e797
    7 Na49de4a1f0cc4c47976bb87586039abe
    8 Na6633baf1941494c8455998c6e3e519e
    9 Naf52ca73e0a842258e2518dd75dd428e
    10 Nbc72650c3a40443bb7cbc8831b617fe5
    11 Ne144f7d2e2d642edadea2e16f38cc0c9
    12 Nfb1cd2613771406ca94ee4c6a91d5e38
    13 anzsrc-for:06
    14 anzsrc-for:0604
    15 schema:author N57f996aa97b04515aed8c0c03c0c9935
    16 schema:citation sg:pub.10.1038/nature11405
    17 sg:pub.10.1038/nbt1385
    18 sg:pub.10.1038/nmeth.1613
    19 sg:pub.10.1038/nmeth756
    20 sg:pub.10.1038/npp.2008.172
    21 sg:pub.10.1038/nprot.2009.236
    22 sg:pub.10.1038/nrd2826
    23 sg:pub.10.1038/nrn1518
    24 sg:pub.10.1186/1471-2105-11-94
    25 sg:pub.10.1186/1471-2105-12-322
    26 sg:pub.10.1186/1471-2164-11-282
    27 sg:pub.10.1186/1471-2164-13-629
    28 sg:pub.10.1186/1748-7188-7-5
    29 sg:pub.10.1186/1755-8794-5-28
    30 sg:pub.10.1186/gb-2008-9-1-r23
    31 https://doi.org/10.1016/j.celrep.2012.08.003
    32 https://doi.org/10.1016/j.neuron.2009.03.027
    33 https://doi.org/10.1016/j.tins.2012.09.005
    34 https://doi.org/10.1016/j.ygeno.2004.01.004
    35 https://doi.org/10.1016/j.ymeth.2009.03.016
    36 https://doi.org/10.1016/s1535-6108(02)00030-2
    37 https://doi.org/10.1073/pnas.012025199
    38 https://doi.org/10.1073/pnas.0308512100
    39 https://doi.org/10.1073/pnas.0400782101
    40 https://doi.org/10.1073/pnas.0605938103
    41 https://doi.org/10.1073/pnas.112683499
    42 https://doi.org/10.1073/pnas.1319700110
    43 https://doi.org/10.1073/pnas.1530509100
    44 https://doi.org/10.1073/pnas.96.12.6745
    45 https://doi.org/10.1093/bioinformatics/btp120
    46 https://doi.org/10.1093/bioinformatics/btp692
    47 https://doi.org/10.1093/bioinformatics/btq643
    48 https://doi.org/10.1093/biostatistics/kxj037
    49 https://doi.org/10.1093/nar/30.1.207
    50 https://doi.org/10.1093/nar/gkn889
    51 https://doi.org/10.1093/nar/gks1048
    52 https://doi.org/10.1093/nar/gni179
    53 https://doi.org/10.1101/gr.079558.108
    54 https://doi.org/10.1101/gr.121095.111
    55 https://doi.org/10.1126/science.286.5439.531
    56 https://doi.org/10.1371/journal.pone.0050986
    57 https://doi.org/10.1371/journal.pone.0051013
    58 https://doi.org/10.3389/fnins.2011.00098
    59 schema:datePublished 2014-12
    60 schema:datePublishedReg 2014-12-01
    61 schema:description BACKGROUND: High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking. RESULTS: To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform. CONCLUSIONS: Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.
    62 schema:genre research_article
    63 schema:inLanguage en
    64 schema:isAccessibleForFree true
    65 schema:isPartOf N866f683b51d34299bcd3392951177df3
    66 Nc99ed3e835bb4d2cbe6d90c104005c35
    67 sg:journal.1023790
    68 schema:name Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq
    69 schema:pagination 154
    70 schema:productId N2cc0d12f265743feadf701cbf5a89074
    71 N4b236edc07014f5d97161903489c8a57
    72 N856571a3c31d43a6b50c68990e0eed97
    73 N85b3ab9a34a644b885faa2f707547c5d
    74 Nabb871ec5b964206b29820ca8ddfb3f9
    75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035031118
    76 https://doi.org/10.1186/1471-2164-15-154
    77 schema:sdDatePublished 2019-04-10T22:30
    78 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    79 schema:sdPublisher N2798d046450843bb9fd98785d2f6a2af
    80 schema:url http://link.springer.com/10.1186%2F1471-2164-15-154
    81 sgo:license sg:explorer/license/
    82 sgo:sdDataset articles
    83 rdf:type schema:ScholarlyArticle
    84 N1a5e7f5984124f7a9d70d89273c8054b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    85 schema:name Neocortex
    86 rdf:type schema:DefinedTerm
    87 N2798d046450843bb9fd98785d2f6a2af schema:name Springer Nature - SN SciGraph project
    88 rdf:type schema:Organization
    89 N2cc0d12f265743feadf701cbf5a89074 schema:name readcube_id
    90 schema:value a6e0184ccb20914e91c57d607ada0a4c7197426ef233ea41d685e95a8b6deb5b
    91 rdf:type schema:PropertyValue
    92 N3b76524c292948e98f52144ad5d9de01 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    93 schema:name Gene Expression
    94 rdf:type schema:DefinedTerm
    95 N3ccd515767524c35ab7dc1db37730f5c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    96 schema:name Oligonucleotide Array Sequence Analysis
    97 rdf:type schema:DefinedTerm
    98 N3df179fb84aa42abbbbaa319d6058f78 rdf:first sg:person.0737532665.63
    99 rdf:rest Nce40c69b0bb147c584c6b980cacc8442
    100 N4b236edc07014f5d97161903489c8a57 schema:name pubmed_id
    101 schema:value 24564186
    102 rdf:type schema:PropertyValue
    103 N4eee80c644ed4801afa90fac3adb2769 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    104 schema:name Cluster Analysis
    105 rdf:type schema:DefinedTerm
    106 N5598139f6614452bb135950161956daf rdf:first sg:person.0751677712.41
    107 rdf:rest N943b64bd2dc1405c974c33aefe6d8ebc
    108 N57f996aa97b04515aed8c0c03c0c9935 rdf:first sg:person.01016772265.00
    109 rdf:rest N5598139f6614452bb135950161956daf
    110 N856571a3c31d43a6b50c68990e0eed97 schema:name dimensions_id
    111 schema:value pub.1035031118
    112 rdf:type schema:PropertyValue
    113 N85b3ab9a34a644b885faa2f707547c5d schema:name doi
    114 schema:value 10.1186/1471-2164-15-154
    115 rdf:type schema:PropertyValue
    116 N866f683b51d34299bcd3392951177df3 schema:volumeNumber 15
    117 rdf:type schema:PublicationVolume
    118 N86b916c5802a41bf9ad17f208b4063a2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    119 schema:name Gene Expression Profiling
    120 rdf:type schema:DefinedTerm
    121 N8819fff4644842f4b020dccb66cd82fd rdf:first sg:person.0603756261.83
    122 rdf:rest Nb3409068b3144dac95e6681d3ce0881f
    123 N943b64bd2dc1405c974c33aefe6d8ebc rdf:first sg:person.01162106766.55
    124 rdf:rest Ne0265ae7e6fb44e3a8731c8be715fe9a
    125 N94ba903e67ab44c38b42fa70dde7e797 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    126 schema:name Brain
    127 rdf:type schema:DefinedTerm
    128 Na49de4a1f0cc4c47976bb87586039abe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    129 schema:name Reproducibility of Results
    130 rdf:type schema:DefinedTerm
    131 Na6633baf1941494c8455998c6e3e519e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    132 schema:name Transcriptome
    133 rdf:type schema:DefinedTerm
    134 Nabb871ec5b964206b29820ca8ddfb3f9 schema:name nlm_unique_id
    135 schema:value 100965258
    136 rdf:type schema:PropertyValue
    137 Naf52ca73e0a842258e2518dd75dd428e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    138 schema:name Humans
    139 rdf:type schema:DefinedTerm
    140 Nb3409068b3144dac95e6681d3ce0881f rdf:first sg:person.01312472674.51
    141 rdf:rest Nf6bb8f188b2e43f0b5a747859a0065fb
    142 Nb8e6ca293c1148b7b2b948c2ba7c5ad1 rdf:first sg:person.011061777727.47
    143 rdf:rest rdf:nil
    144 Nbc72650c3a40443bb7cbc8831b617fe5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    145 schema:name Computational Biology
    146 rdf:type schema:DefinedTerm
    147 Nc99ed3e835bb4d2cbe6d90c104005c35 schema:issueNumber 1
    148 rdf:type schema:PublicationIssue
    149 Nce40c69b0bb147c584c6b980cacc8442 rdf:first sg:person.011413554527.71
    150 rdf:rest N8819fff4644842f4b020dccb66cd82fd
    151 Ne0265ae7e6fb44e3a8731c8be715fe9a rdf:first sg:person.01023220540.11
    152 rdf:rest N3df179fb84aa42abbbbaa319d6058f78
    153 Ne144f7d2e2d642edadea2e16f38cc0c9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    154 schema:name Sequence Analysis, RNA
    155 rdf:type schema:DefinedTerm
    156 Nf6bb8f188b2e43f0b5a747859a0065fb rdf:first sg:person.013277307467.70
    157 rdf:rest Nb8e6ca293c1148b7b2b948c2ba7c5ad1
    158 Nfb1cd2613771406ca94ee4c6a91d5e38 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    159 schema:name High-Throughput Nucleotide Sequencing
    160 rdf:type schema:DefinedTerm
    161 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    162 schema:name Biological Sciences
    163 rdf:type schema:DefinedTerm
    164 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    165 schema:name Genetics
    166 rdf:type schema:DefinedTerm
    167 sg:journal.1023790 schema:issn 1471-2164
    168 schema:name BMC Genomics
    169 rdf:type schema:Periodical
    170 sg:person.01016772265.00 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    171 schema:familyName Miller
    172 schema:givenName Jeremy A
    173 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01016772265.00
    174 rdf:type schema:Person
    175 sg:person.01023220540.11 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    176 schema:familyName Kaykas
    177 schema:givenName Ajamete
    178 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023220540.11
    179 rdf:type schema:Person
    180 sg:person.011061777727.47 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    181 schema:familyName Hawrylycz
    182 schema:givenName Mike J
    183 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011061777727.47
    184 rdf:type schema:Person
    185 sg:person.011413554527.71 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    186 schema:familyName Smith
    187 schema:givenName Kimberly A
    188 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011413554527.71
    189 rdf:type schema:Person
    190 sg:person.01162106766.55 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    191 schema:familyName Goldy
    192 schema:givenName Jeff
    193 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01162106766.55
    194 rdf:type schema:Person
    195 sg:person.01312472674.51 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    196 schema:familyName Phillips
    197 schema:givenName John W
    198 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312472674.51
    199 rdf:type schema:Person
    200 sg:person.013277307467.70 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    201 schema:familyName Lein
    202 schema:givenName Ed S
    203 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013277307467.70
    204 rdf:type schema:Person
    205 sg:person.0603756261.83 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    206 schema:familyName Shen
    207 schema:givenName Elaine H
    208 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0603756261.83
    209 rdf:type schema:Person
    210 sg:person.0737532665.63 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    211 schema:familyName Lee
    212 schema:givenName Chang-Kyu
    213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0737532665.63
    214 rdf:type schema:Person
    215 sg:person.0751677712.41 schema:affiliation https://www.grid.ac/institutes/grid.417881.3
    216 schema:familyName Menon
    217 schema:givenName Vilas
    218 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0751677712.41
    219 rdf:type schema:Person
    220 sg:pub.10.1038/nature11405 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044384575
    221 https://doi.org/10.1038/nature11405
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1038/nbt1385 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053259870
    224 https://doi.org/10.1038/nbt1385
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1038/nmeth.1613 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009798986
    227 https://doi.org/10.1038/nmeth.1613
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1038/nmeth756 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039053552
    230 https://doi.org/10.1038/nmeth756
    231 rdf:type schema:CreativeWork
    232 sg:pub.10.1038/npp.2008.172 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003610244
    233 https://doi.org/10.1038/npp.2008.172
    234 rdf:type schema:CreativeWork
    235 sg:pub.10.1038/nprot.2009.236 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004430683
    236 https://doi.org/10.1038/nprot.2009.236
    237 rdf:type schema:CreativeWork
    238 sg:pub.10.1038/nrd2826 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000699125
    239 https://doi.org/10.1038/nrd2826
    240 rdf:type schema:CreativeWork
    241 sg:pub.10.1038/nrn1518 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006061461
    242 https://doi.org/10.1038/nrn1518
    243 rdf:type schema:CreativeWork
    244 sg:pub.10.1186/1471-2105-11-94 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053091615
    245 https://doi.org/10.1186/1471-2105-11-94
    246 rdf:type schema:CreativeWork
    247 sg:pub.10.1186/1471-2105-12-322 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013163514
    248 https://doi.org/10.1186/1471-2105-12-322
    249 rdf:type schema:CreativeWork
    250 sg:pub.10.1186/1471-2164-11-282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008198713
    251 https://doi.org/10.1186/1471-2164-11-282
    252 rdf:type schema:CreativeWork
    253 sg:pub.10.1186/1471-2164-13-629 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017696038
    254 https://doi.org/10.1186/1471-2164-13-629
    255 rdf:type schema:CreativeWork
    256 sg:pub.10.1186/1748-7188-7-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031497141
    257 https://doi.org/10.1186/1748-7188-7-5
    258 rdf:type schema:CreativeWork
    259 sg:pub.10.1186/1755-8794-5-28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020484619
    260 https://doi.org/10.1186/1755-8794-5-28
    261 rdf:type schema:CreativeWork
    262 sg:pub.10.1186/gb-2008-9-1-r23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032727814
    263 https://doi.org/10.1186/gb-2008-9-1-r23
    264 rdf:type schema:CreativeWork
    265 https://doi.org/10.1016/j.celrep.2012.08.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019883673
    266 rdf:type schema:CreativeWork
    267 https://doi.org/10.1016/j.neuron.2009.03.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045724594
    268 rdf:type schema:CreativeWork
    269 https://doi.org/10.1016/j.tins.2012.09.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034519187
    270 rdf:type schema:CreativeWork
    271 https://doi.org/10.1016/j.ygeno.2004.01.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012946204
    272 rdf:type schema:CreativeWork
    273 https://doi.org/10.1016/j.ymeth.2009.03.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043922138
    274 rdf:type schema:CreativeWork
    275 https://doi.org/10.1016/s1535-6108(02)00030-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053589488
    276 rdf:type schema:CreativeWork
    277 https://doi.org/10.1073/pnas.012025199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044815609
    278 rdf:type schema:CreativeWork
    279 https://doi.org/10.1073/pnas.0308512100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007985703
    280 rdf:type schema:CreativeWork
    281 https://doi.org/10.1073/pnas.0400782101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002732260
    282 rdf:type schema:CreativeWork
    283 https://doi.org/10.1073/pnas.0605938103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005680522
    284 rdf:type schema:CreativeWork
    285 https://doi.org/10.1073/pnas.112683499 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027699267
    286 rdf:type schema:CreativeWork
    287 https://doi.org/10.1073/pnas.1319700110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037851904
    288 rdf:type schema:CreativeWork
    289 https://doi.org/10.1073/pnas.1530509100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044620917
    290 rdf:type schema:CreativeWork
    291 https://doi.org/10.1073/pnas.96.12.6745 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033514193
    292 rdf:type schema:CreativeWork
    293 https://doi.org/10.1093/bioinformatics/btp120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012425816
    294 rdf:type schema:CreativeWork
    295 https://doi.org/10.1093/bioinformatics/btp692 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045138418
    296 rdf:type schema:CreativeWork
    297 https://doi.org/10.1093/bioinformatics/btq643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030748565
    298 rdf:type schema:CreativeWork
    299 https://doi.org/10.1093/biostatistics/kxj037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016217055
    300 rdf:type schema:CreativeWork
    301 https://doi.org/10.1093/nar/30.1.207 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005297170
    302 rdf:type schema:CreativeWork
    303 https://doi.org/10.1093/nar/gkn889 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048226122
    304 rdf:type schema:CreativeWork
    305 https://doi.org/10.1093/nar/gks1048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018129858
    306 rdf:type schema:CreativeWork
    307 https://doi.org/10.1093/nar/gni179 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040526112
    308 rdf:type schema:CreativeWork
    309 https://doi.org/10.1101/gr.079558.108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045837493
    310 rdf:type schema:CreativeWork
    311 https://doi.org/10.1101/gr.121095.111 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044818605
    312 rdf:type schema:CreativeWork
    313 https://doi.org/10.1126/science.286.5439.531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042995627
    314 rdf:type schema:CreativeWork
    315 https://doi.org/10.1371/journal.pone.0050986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034642791
    316 rdf:type schema:CreativeWork
    317 https://doi.org/10.1371/journal.pone.0051013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050588975
    318 rdf:type schema:CreativeWork
    319 https://doi.org/10.3389/fnins.2011.00098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052748704
    320 rdf:type schema:CreativeWork
    321 https://www.grid.ac/institutes/grid.417881.3 schema:alternateName Allen Institute for Brain Science
    322 schema:name Allen Institute for Brain Science, 551 N 34th Street, 98103, Seattle, WA, USA
    323 rdf:type schema:Organization
     




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


    ...