mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Huijuan Feng, Xuegong Zhang, Chaolin Zhang

ABSTRACT

The volume of RNA-Seq data sets in public repositories has been expanding exponentially, providing unprecedented opportunities to study gene expression regulation. Because degraded RNA samples, such as those collected from post-mortem tissues, can result in distinct expression profiles with potential biases, a particularly important step in mining these data is quality control. Here we develop a method named mRIN to directly assess mRNA integrity from RNA-Seq data at the sample and individual gene level. We systematically analyse large-scale RNA-Seq data sets of the human brain transcriptome generated by different consortia. Our analysis demonstrates that 3' bias resulting from partial RNA fragmentation in post-mortem tissues has a marked impact on global expression profiles, and that mRIN effectively identifies samples with different levels of mRNA degradation. Unexpectedly, this process has a reproducible and gene-specific component, and transcripts with different stabilities are associated with distinct functions and structural features reminiscent of mRNA decay in living cells. More... »

PAGES

7816

References to SciGraph publications

  • 2007-02. The highways and byways of mRNA decay in NATURE REVIEWS MOLECULAR CELL BIOLOGY
  • 2003-05. DAVID: Database for Annotation, Visualization, and Integrated Discovery in GENOME BIOLOGY
  • 2003-09. DAVID: Database for Annotation, Visualization, and Integrated Discovery in GENOME BIOLOGY
  • 2014-12. RNA-seq: impact of RNA degradation on transcript quantification in BMC BIOLOGY
  • 2011-10. Spatio-temporal transcriptome of the human brain in NATURE
  • 2011-10. The evolution of gene expression levels in mammalian organs in NATURE
  • 2008-07. Mapping and quantifying mammalian transcriptomes by RNA-Seq in NATURE METHODS
  • 2006-12. The RIN: an RNA integrity number for assigning integrity values to RNA measurements in BMC MOLECULAR BIOLOGY
  • 2014-09. Detecting and correcting systematic variation in large-scale RNA sequencing data in NATURE BIOTECHNOLOGY
  • 2010-05. Modeling non-uniformity in short-read rates in RNA-Seq data in GENOME BIOLOGY
  • 2011-05. Global quantification of mammalian gene expression control in NATURE
  • 2013-07. Comparative analysis of RNA sequencing methods for degraded or low-input samples in NATURE METHODS
  • 2009-01. RNA-Seq: a revolutionary tool for transcriptomics in NATURE REVIEWS GENETICS
  • 2006-07. RNA-quality control by the exosome in NATURE REVIEWS MOLECULAR CELL BIOLOGY
  • 2013-12. NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data in BMC BIOINFORMATICS
  • 2007-06. Discovery of tissue-specific exons using comprehensive human exon microarrays in GENOME BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ncomms8816

    DOI

    http://dx.doi.org/10.1038/ncomms8816

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1002886830

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26234653


    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": "Autopsy", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Brain", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Databases, Nucleic Acid", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Electrophoresis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression Profiling", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Machine Learning", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "RNA Stability", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "RNA, Messenger", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sequence Analysis, RNA", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Columbia University", 
              "id": "https://www.grid.ac/institutes/grid.21729.3f", 
              "name": [
                "MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China", 
                "Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, New York 10032, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Feng", 
            "givenName": "Huijuan", 
            "id": "sg:person.01145554724.75", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145554724.75"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Tsinghua University", 
              "id": "https://www.grid.ac/institutes/grid.12527.33", 
              "name": [
                "MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Xuegong", 
            "id": "sg:person.0636731555.29", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636731555.29"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Columbia University", 
              "id": "https://www.grid.ac/institutes/grid.21729.3f", 
              "name": [
                "Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, New York 10032, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Chaolin", 
            "id": "sg:person.013000166437.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013000166437.00"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1093/nar/gki1012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001073893"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bts196", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002683055"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btq696", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003723450"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/30.1.207", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005297170"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.3000", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005329637", 
              "https://doi.org/10.1038/nbt.3000"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.997703", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007971682"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.molcel.2010.06.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009403120"
            ], 
            "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/10.1093/dnares/dsn030", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014322004"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.95.25.14863", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020882317"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2003-4-5-p3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021292424", 
              "https://doi.org/10.1186/gb-2003-4-5-p3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1038/sj.embor.7400572", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021300383"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1038/sj.embor.7400572", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021300383"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0091851", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022378738"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1741-7007-12-42", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023296553", 
              "https://doi.org/10.1186/1741-7007-12-42"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkm959", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023437147"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.1272403", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024692204"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bts034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025078414"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bts356", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025907389"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gni054", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026036449"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pcbi.1000770", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029097230"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1262110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029707840"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg2484", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030687647", 
              "https://doi.org/10.1038/nrg2484"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btq643", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030748565"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature10532", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032765524", 
              "https://doi.org/10.1038/nature10532"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.2483", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033028450", 
              "https://doi.org/10.1038/nmeth.2483"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrm1964", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033576549", 
              "https://doi.org/10.1038/nrm1964"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrm1964", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033576549", 
              "https://doi.org/10.1038/nrm1964"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature10098", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033777014", 
              "https://doi.org/10.1038/nature10098"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrm2104", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033804673", 
              "https://doi.org/10.1038/nrm2104"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cell.2009.01.019", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033847828"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.molcel.2010.06.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033966244"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.1082320", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034783868"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkq990", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036307269"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btq033", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036892131"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.112128.110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037403939"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2199-7-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040062143", 
              "https://doi.org/10.1186/1471-2199-7-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2003-4-9-r60", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041539408", 
              "https://doi.org/10.1186/gb-2003-4-9-r60"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.biopsych.2003.10.013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041985913"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkp542", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042357646"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2010-11-5-r50", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043554856", 
              "https://doi.org/10.1186/gb-2010-11-5-r50"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.1226", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045381177", 
              "https://doi.org/10.1038/nmeth.1226"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2007-8-4-r64", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046258686", 
              "https://doi.org/10.1186/gb-2007-8-4-r64"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1096/fj.04-2591hyp", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046584561"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.82.16.5328", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047825189"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1096/fj.04-3552fje", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047992426"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-14-220", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048105947", 
              "https://doi.org/10.1186/1471-2105-14-220"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1097/nen.0b013e3181c7e32f", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049458556"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature10523", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050723090", 
              "https://doi.org/10.1038/nature10523"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1214/aoms/1177730256", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064402019"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1074976404", 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015-12", 
        "datePublishedReg": "2015-12-01", 
        "description": "The volume of RNA-Seq data sets in public repositories has been expanding exponentially, providing unprecedented opportunities to study gene expression regulation. Because degraded RNA samples, such as those collected from post-mortem tissues, can result in distinct expression profiles with potential biases, a particularly important step in mining these data is quality control. Here we develop a method named mRIN to directly assess mRNA integrity from RNA-Seq data at the sample and individual gene level. We systematically analyse large-scale RNA-Seq data sets of the human brain transcriptome generated by different consortia. Our analysis demonstrates that 3' bias resulting from partial RNA fragmentation in post-mortem tissues has a marked impact on global expression profiles, and that mRIN effectively identifies samples with different levels of mRNA degradation. Unexpectedly, this process has a reproducible and gene-specific component, and transcripts with different stabilities are associated with distinct functions and structural features reminiscent of mRNA decay in living cells. ", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/ncomms8816", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2441894", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1043282", 
            "issn": [
              "2041-1723"
            ], 
            "name": "Nature Communications", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "6"
          }
        ], 
        "name": "mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data", 
        "pagination": "7816", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "0dbf75f54f639a9fe574656ec564c26674ad0edce38fe989cdd5d34accff06b9"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "26234653"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "101528555"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/ncomms8816"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1002886830"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/ncomms8816", 
          "https://app.dimensions.ai/details/publication/pub.1002886830"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T19:46", 
        "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_8681_00000435.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://www.nature.com/articles/ncomms8816"
      }
    ]
     

    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.10.1038/ncomms8816'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/ncomms8816'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/ncomms8816'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/ncomms8816'


     

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

    290 TRIPLES      21 PREDICATES      88 URIs      31 LITERALS      19 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/ncomms8816 schema:about N1145d0355e8246a8b7ca91bd3f6cd2ec
    2 N3a13f9c0a6cc4388ba156831e9b398be
    3 N65cd7f8305ed45998b4e6e60dfb3f0ae
    4 N713c6a1236cd458a8e76cc23181fac57
    5 N78e1c1c5121c4fbea7ba31cdd3da7928
    6 N904fa417f15a4508b0dea60876d7d8c5
    7 Nac9f66761ab745e7a979508b3320bce0
    8 Nbaffec9f278244e99112ef4ada37cc23
    9 Nda85240e9ded417a977403e71615e8e4
    10 Ne05c9bdef3334a2a971a7d4afbd1a14a
    11 anzsrc-for:06
    12 anzsrc-for:0604
    13 schema:author Nae9e0c6ef9664c5787c6ad0e6a62ee1b
    14 schema:citation sg:pub.10.1038/nature10098
    15 sg:pub.10.1038/nature10523
    16 sg:pub.10.1038/nature10532
    17 sg:pub.10.1038/nbt.3000
    18 sg:pub.10.1038/nmeth.1226
    19 sg:pub.10.1038/nmeth.2483
    20 sg:pub.10.1038/nrg2484
    21 sg:pub.10.1038/nrm1964
    22 sg:pub.10.1038/nrm2104
    23 sg:pub.10.1186/1471-2105-14-220
    24 sg:pub.10.1186/1471-2199-7-3
    25 sg:pub.10.1186/1741-7007-12-42
    26 sg:pub.10.1186/gb-2003-4-5-p3
    27 sg:pub.10.1186/gb-2003-4-9-r60
    28 sg:pub.10.1186/gb-2007-8-4-r64
    29 sg:pub.10.1186/gb-2010-11-5-r50
    30 https://app.dimensions.ai/details/publication/pub.1074976404
    31 https://doi.org/10.1016/j.biopsych.2003.10.013
    32 https://doi.org/10.1016/j.cell.2009.01.019
    33 https://doi.org/10.1016/j.molcel.2010.06.001
    34 https://doi.org/10.1016/j.molcel.2010.06.005
    35 https://doi.org/10.1038/sj.embor.7400572
    36 https://doi.org/10.1073/pnas.82.16.5328
    37 https://doi.org/10.1073/pnas.95.25.14863
    38 https://doi.org/10.1093/bioinformatics/btp120
    39 https://doi.org/10.1093/bioinformatics/btq033
    40 https://doi.org/10.1093/bioinformatics/btq643
    41 https://doi.org/10.1093/bioinformatics/btq696
    42 https://doi.org/10.1093/bioinformatics/bts034
    43 https://doi.org/10.1093/bioinformatics/bts196
    44 https://doi.org/10.1093/bioinformatics/bts356
    45 https://doi.org/10.1093/dnares/dsn030
    46 https://doi.org/10.1093/nar/30.1.207
    47 https://doi.org/10.1093/nar/gki1012
    48 https://doi.org/10.1093/nar/gkm959
    49 https://doi.org/10.1093/nar/gkp542
    50 https://doi.org/10.1093/nar/gkq990
    51 https://doi.org/10.1093/nar/gni054
    52 https://doi.org/10.1096/fj.04-2591hyp
    53 https://doi.org/10.1096/fj.04-3552fje
    54 https://doi.org/10.1097/nen.0b013e3181c7e32f
    55 https://doi.org/10.1101/gr.112128.110
    56 https://doi.org/10.1101/gr.1272403
    57 https://doi.org/10.1101/gr.997703
    58 https://doi.org/10.1126/science.1082320
    59 https://doi.org/10.1126/science.1262110
    60 https://doi.org/10.1214/aoms/1177730256
    61 https://doi.org/10.1371/journal.pcbi.1000770
    62 https://doi.org/10.1371/journal.pone.0091851
    63 schema:datePublished 2015-12
    64 schema:datePublishedReg 2015-12-01
    65 schema:description The volume of RNA-Seq data sets in public repositories has been expanding exponentially, providing unprecedented opportunities to study gene expression regulation. Because degraded RNA samples, such as those collected from post-mortem tissues, can result in distinct expression profiles with potential biases, a particularly important step in mining these data is quality control. Here we develop a method named mRIN to directly assess mRNA integrity from RNA-Seq data at the sample and individual gene level. We systematically analyse large-scale RNA-Seq data sets of the human brain transcriptome generated by different consortia. Our analysis demonstrates that 3' bias resulting from partial RNA fragmentation in post-mortem tissues has a marked impact on global expression profiles, and that mRIN effectively identifies samples with different levels of mRNA degradation. Unexpectedly, this process has a reproducible and gene-specific component, and transcripts with different stabilities are associated with distinct functions and structural features reminiscent of mRNA decay in living cells.
    66 schema:genre research_article
    67 schema:inLanguage en
    68 schema:isAccessibleForFree true
    69 schema:isPartOf N5f38653c61384cee90c917441cbbd8e8
    70 N67d4d907bf1d4601a8c6fa08637e52be
    71 sg:journal.1043282
    72 schema:name mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data
    73 schema:pagination 7816
    74 schema:productId N31d343105b8d4102b1d3d0cf9f9e361a
    75 N5f808ac0df5c4246a4be44e52311084a
    76 N6bd259dc53e8428d86d27c6ce0c093c1
    77 Nd03df52511e24d8482017292a7e57d3d
    78 Ndb588422889147fe9577353b5afb0b83
    79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002886830
    80 https://doi.org/10.1038/ncomms8816
    81 schema:sdDatePublished 2019-04-10T19:46
    82 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    83 schema:sdPublisher Nce8f8f36cb91490ab81c0d9bdec5765d
    84 schema:url https://www.nature.com/articles/ncomms8816
    85 sgo:license sg:explorer/license/
    86 sgo:sdDataset articles
    87 rdf:type schema:ScholarlyArticle
    88 N06d851e8d4bc49c390fc97955efa8174 rdf:first sg:person.013000166437.00
    89 rdf:rest rdf:nil
    90 N0b1e1bec34574814b574e31462e9cb86 rdf:first sg:person.0636731555.29
    91 rdf:rest N06d851e8d4bc49c390fc97955efa8174
    92 N1145d0355e8246a8b7ca91bd3f6cd2ec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    93 schema:name Humans
    94 rdf:type schema:DefinedTerm
    95 N31d343105b8d4102b1d3d0cf9f9e361a schema:name pubmed_id
    96 schema:value 26234653
    97 rdf:type schema:PropertyValue
    98 N3a13f9c0a6cc4388ba156831e9b398be schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    99 schema:name RNA Stability
    100 rdf:type schema:DefinedTerm
    101 N5f38653c61384cee90c917441cbbd8e8 schema:issueNumber 1
    102 rdf:type schema:PublicationIssue
    103 N5f808ac0df5c4246a4be44e52311084a schema:name dimensions_id
    104 schema:value pub.1002886830
    105 rdf:type schema:PropertyValue
    106 N65cd7f8305ed45998b4e6e60dfb3f0ae schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    107 schema:name Brain
    108 rdf:type schema:DefinedTerm
    109 N67d4d907bf1d4601a8c6fa08637e52be schema:volumeNumber 6
    110 rdf:type schema:PublicationVolume
    111 N6bd259dc53e8428d86d27c6ce0c093c1 schema:name doi
    112 schema:value 10.1038/ncomms8816
    113 rdf:type schema:PropertyValue
    114 N713c6a1236cd458a8e76cc23181fac57 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    115 schema:name RNA, Messenger
    116 rdf:type schema:DefinedTerm
    117 N78e1c1c5121c4fbea7ba31cdd3da7928 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    118 schema:name Gene Expression Profiling
    119 rdf:type schema:DefinedTerm
    120 N904fa417f15a4508b0dea60876d7d8c5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    121 schema:name Autopsy
    122 rdf:type schema:DefinedTerm
    123 Nac9f66761ab745e7a979508b3320bce0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    124 schema:name Databases, Nucleic Acid
    125 rdf:type schema:DefinedTerm
    126 Nae9e0c6ef9664c5787c6ad0e6a62ee1b rdf:first sg:person.01145554724.75
    127 rdf:rest N0b1e1bec34574814b574e31462e9cb86
    128 Nbaffec9f278244e99112ef4ada37cc23 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    129 schema:name Sequence Analysis, RNA
    130 rdf:type schema:DefinedTerm
    131 Nce8f8f36cb91490ab81c0d9bdec5765d schema:name Springer Nature - SN SciGraph project
    132 rdf:type schema:Organization
    133 Nd03df52511e24d8482017292a7e57d3d schema:name nlm_unique_id
    134 schema:value 101528555
    135 rdf:type schema:PropertyValue
    136 Nda85240e9ded417a977403e71615e8e4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    137 schema:name Electrophoresis
    138 rdf:type schema:DefinedTerm
    139 Ndb588422889147fe9577353b5afb0b83 schema:name readcube_id
    140 schema:value 0dbf75f54f639a9fe574656ec564c26674ad0edce38fe989cdd5d34accff06b9
    141 rdf:type schema:PropertyValue
    142 Ne05c9bdef3334a2a971a7d4afbd1a14a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    143 schema:name Machine Learning
    144 rdf:type schema:DefinedTerm
    145 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    146 schema:name Biological Sciences
    147 rdf:type schema:DefinedTerm
    148 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    149 schema:name Genetics
    150 rdf:type schema:DefinedTerm
    151 sg:grant.2441894 http://pending.schema.org/fundedItem sg:pub.10.1038/ncomms8816
    152 rdf:type schema:MonetaryGrant
    153 sg:journal.1043282 schema:issn 2041-1723
    154 schema:name Nature Communications
    155 rdf:type schema:Periodical
    156 sg:person.01145554724.75 schema:affiliation https://www.grid.ac/institutes/grid.21729.3f
    157 schema:familyName Feng
    158 schema:givenName Huijuan
    159 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145554724.75
    160 rdf:type schema:Person
    161 sg:person.013000166437.00 schema:affiliation https://www.grid.ac/institutes/grid.21729.3f
    162 schema:familyName Zhang
    163 schema:givenName Chaolin
    164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013000166437.00
    165 rdf:type schema:Person
    166 sg:person.0636731555.29 schema:affiliation https://www.grid.ac/institutes/grid.12527.33
    167 schema:familyName Zhang
    168 schema:givenName Xuegong
    169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636731555.29
    170 rdf:type schema:Person
    171 sg:pub.10.1038/nature10098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033777014
    172 https://doi.org/10.1038/nature10098
    173 rdf:type schema:CreativeWork
    174 sg:pub.10.1038/nature10523 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050723090
    175 https://doi.org/10.1038/nature10523
    176 rdf:type schema:CreativeWork
    177 sg:pub.10.1038/nature10532 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032765524
    178 https://doi.org/10.1038/nature10532
    179 rdf:type schema:CreativeWork
    180 sg:pub.10.1038/nbt.3000 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005329637
    181 https://doi.org/10.1038/nbt.3000
    182 rdf:type schema:CreativeWork
    183 sg:pub.10.1038/nmeth.1226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045381177
    184 https://doi.org/10.1038/nmeth.1226
    185 rdf:type schema:CreativeWork
    186 sg:pub.10.1038/nmeth.2483 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033028450
    187 https://doi.org/10.1038/nmeth.2483
    188 rdf:type schema:CreativeWork
    189 sg:pub.10.1038/nrg2484 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030687647
    190 https://doi.org/10.1038/nrg2484
    191 rdf:type schema:CreativeWork
    192 sg:pub.10.1038/nrm1964 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033576549
    193 https://doi.org/10.1038/nrm1964
    194 rdf:type schema:CreativeWork
    195 sg:pub.10.1038/nrm2104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033804673
    196 https://doi.org/10.1038/nrm2104
    197 rdf:type schema:CreativeWork
    198 sg:pub.10.1186/1471-2105-14-220 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048105947
    199 https://doi.org/10.1186/1471-2105-14-220
    200 rdf:type schema:CreativeWork
    201 sg:pub.10.1186/1471-2199-7-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040062143
    202 https://doi.org/10.1186/1471-2199-7-3
    203 rdf:type schema:CreativeWork
    204 sg:pub.10.1186/1741-7007-12-42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023296553
    205 https://doi.org/10.1186/1741-7007-12-42
    206 rdf:type schema:CreativeWork
    207 sg:pub.10.1186/gb-2003-4-5-p3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021292424
    208 https://doi.org/10.1186/gb-2003-4-5-p3
    209 rdf:type schema:CreativeWork
    210 sg:pub.10.1186/gb-2003-4-9-r60 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041539408
    211 https://doi.org/10.1186/gb-2003-4-9-r60
    212 rdf:type schema:CreativeWork
    213 sg:pub.10.1186/gb-2007-8-4-r64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046258686
    214 https://doi.org/10.1186/gb-2007-8-4-r64
    215 rdf:type schema:CreativeWork
    216 sg:pub.10.1186/gb-2010-11-5-r50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043554856
    217 https://doi.org/10.1186/gb-2010-11-5-r50
    218 rdf:type schema:CreativeWork
    219 https://app.dimensions.ai/details/publication/pub.1074976404 schema:CreativeWork
    220 https://doi.org/10.1016/j.biopsych.2003.10.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041985913
    221 rdf:type schema:CreativeWork
    222 https://doi.org/10.1016/j.cell.2009.01.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033847828
    223 rdf:type schema:CreativeWork
    224 https://doi.org/10.1016/j.molcel.2010.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033966244
    225 rdf:type schema:CreativeWork
    226 https://doi.org/10.1016/j.molcel.2010.06.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009403120
    227 rdf:type schema:CreativeWork
    228 https://doi.org/10.1038/sj.embor.7400572 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021300383
    229 rdf:type schema:CreativeWork
    230 https://doi.org/10.1073/pnas.82.16.5328 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047825189
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.1073/pnas.95.25.14863 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020882317
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.1093/bioinformatics/btp120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012425816
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.1093/bioinformatics/btq033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036892131
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.1093/bioinformatics/btq643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030748565
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1093/bioinformatics/btq696 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003723450
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1093/bioinformatics/bts034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025078414
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.1093/bioinformatics/bts196 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002683055
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1093/bioinformatics/bts356 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025907389
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1093/dnares/dsn030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014322004
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1093/nar/30.1.207 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005297170
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1093/nar/gki1012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001073893
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1093/nar/gkm959 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023437147
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.1093/nar/gkp542 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042357646
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.1093/nar/gkq990 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036307269
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.1093/nar/gni054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026036449
    261 rdf:type schema:CreativeWork
    262 https://doi.org/10.1096/fj.04-2591hyp schema:sameAs https://app.dimensions.ai/details/publication/pub.1046584561
    263 rdf:type schema:CreativeWork
    264 https://doi.org/10.1096/fj.04-3552fje schema:sameAs https://app.dimensions.ai/details/publication/pub.1047992426
    265 rdf:type schema:CreativeWork
    266 https://doi.org/10.1097/nen.0b013e3181c7e32f schema:sameAs https://app.dimensions.ai/details/publication/pub.1049458556
    267 rdf:type schema:CreativeWork
    268 https://doi.org/10.1101/gr.112128.110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037403939
    269 rdf:type schema:CreativeWork
    270 https://doi.org/10.1101/gr.1272403 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024692204
    271 rdf:type schema:CreativeWork
    272 https://doi.org/10.1101/gr.997703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007971682
    273 rdf:type schema:CreativeWork
    274 https://doi.org/10.1126/science.1082320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034783868
    275 rdf:type schema:CreativeWork
    276 https://doi.org/10.1126/science.1262110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029707840
    277 rdf:type schema:CreativeWork
    278 https://doi.org/10.1214/aoms/1177730256 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064402019
    279 rdf:type schema:CreativeWork
    280 https://doi.org/10.1371/journal.pcbi.1000770 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029097230
    281 rdf:type schema:CreativeWork
    282 https://doi.org/10.1371/journal.pone.0091851 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022378738
    283 rdf:type schema:CreativeWork
    284 https://www.grid.ac/institutes/grid.12527.33 schema:alternateName Tsinghua University
    285 schema:name MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China
    286 rdf:type schema:Organization
    287 https://www.grid.ac/institutes/grid.21729.3f schema:alternateName Columbia University
    288 schema:name Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, New York 10032, USA
    289 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China
    290 rdf:type schema:Organization
     




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


    ...