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FANTOM5 CAGE profiles of human and mouse samples

  • Scientific Data 4, Article number: 170112 (2017)
  • doi:10.1038/sdata.2017.112
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In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells. Non-overlapping peaks over the CAGE profiles, approximately 200,000 and 150,000 peaks for the human and mouse genomes, were identified and annotated to provide precise location of known promoters as well as novel ones, and to quantify their activities.

Design Type(s)
  • organism part comparison design
  • species comparison design
  • cell type comparison design
  • organism development design
Measurement Type(s)
  • DNA-templated transcription, initiation
Technology Type(s)
  • cap analysis of gene expression
Factor Type(s)
  • Species
  • Organism Part
  • life cycle stage
  • cell type
Sample Characteristic(s)
  • Mus musculus
  • Homo sapiens
  • cerebellum
  • visual cortex
  • ileum
  • Peyer's patch
  • stomach
  • axillary lymph node
  • aorta
  • substantia nigra
  • hippocampal formation
  • brain
  • heart
  • liver
  • meningeal cluster
  • bone marrow
  • spinal cord
  • raphe nuclei
  • corpus striatum
  • cortex
  • peripheral nervous system
  • kidney
  • neural system
  • hemolymphoid system
  • blood
  • spleen
  • mesoderm
  • hematopoietic system
  • ventral wall of dorsal aorta
  • placenta
  • ganglion
  • spiral organ of cochlea
  • small intestine
  • intestine
  • adrenal gland
  • eyeball of camera-type eye
  • pituitary gland
  • thymus
  • lung
  • female gonad
  • testis
  • bone tissue
  • diencephalon
  • muscle organ
  • medulla oblongata
  • forelimb
  • pancreas
  • gonad
  • corpora quadrigemina
  • skin of body
  • tongue
  • colon
  • caecum
  • vesicular gland
  • epididymis
  • amnion
  • mammary gland
  • uterus
  • submandibular gland
  • prostate gland
  • intestinal mucosa
  • urinary bladder
  • vagina
  • oviduct

Background & Summary

Since the completion of the human genome sequencing, role of individual bases has been a central question. An international collaborative effort, FANTOM (Functional ANnoTation Of Mammalian Genome)1, delineated a complex landscape of transcribed RNAs (transcriptome) and their regulations. The initial key technology driving the project was to make full-length cDNA clones, representing complete primary structure of transcribed RNA molecules. Sequencing of the full-length cDNA clones uncovered unexpected number of long non-coding RNAs as well as protein coding genes2,​3,​4,​5,​6. The CAGE (Cap Analysis Gene Expression)7,8 protocol, combination with high-throughput sequencing, was developed to monitor frequencies of transcription initiation by determining 5′-end of capped RNAs. The technology was devised to uncover complexity of the transcriptome4,​5,​6 and elucidate transcriptional regulatory networks by focusing on promoter elements9,​10,​11,​12. By taking advantage of single molecule sequencer, HeliScopeCAGE was recently developed to provide more sensitive and accurate monitoring of transcription initiation activities7,8.

In the fifth round of the FANTOM projects, FANTOM5, the challenge was to capture the transcriptome of many varieties of cell states as possible, to understand the implication of each genomic bases in different contexts. In the first phase of the FANTOM5 project, we targeted cells in steady state, called ‘snapshot’ samples13. Our central focus was on human primary cells, while cell lines, tissues and mouse samples were chosen to cover cells inaccessible as isolated human primary samples. The resulting data provided an atlas of promoter and enhancer activities in wide range of cell states14, which is a baseline of understanding complex transcriptional regulation. In the second phase, we focused on transitions of cell states by monitoring ‘time course’ samples, such as activations, differentiations, and developments at sequential time points15. The monitored activities of promoters and enhancers demonstrated that enhancer activities is the earliest event during dynamic changes of transcriptome. These data sets are being utilized in many other studies inside and outside of the FANTOM5 consortium.

The data production scheme was implemented based on the FANTOM5 collaboration. Sample collection was performed at individual institutes, since specific types of samples require dedicated systems with special expertise or settings, as well as through purchase from commercial sources. RNA quality was firstly examined at the place where the samples were obtained (the first RNA quality check). The CAGE assay pipeline established in RIKEN GeNAS (Genome Network Analysis Support Facility) employed two workflows of HeliScopeCAGE, a manual workflow for samples with small amount of total RNAs8 and a robotic workflow for samples with standard requirements7. The assay pipeline started with checking RNA quality (the second RNA quality check), which provides a uniform quality assessment of the profiled RNA extracts. The resulting CAGE libraries were sequenced by HeliScope in RIKEN and also in Helicos Biosciences, and the obtained data were processed by the MOIRAI system16. Quality of the resulting CAGE profiles was checked with several statistics as well as manual inspection by using the ZENBU browser17. Finally CAGE profiles were shared among the consortium for further analysis.

In the course of the two phases focused on ‘snapshot’ and ‘time course’ samples, we profiled 1,816 human and 1,016 mouse samples in total, and obtained approximately four millions of single-molecule reads successfully aligned to the genome per sample on average. Based on frequencies of the observed 5′-ends of individual capped RNA molecules at a single base-pair resolution, we identified 201,802 and 158,966 peaks for human and mouse respectively, where promoters are defined as the sequence immediately upstream of the peaks and frequencies of observed CAGE reads reflect activities of the promoters. All data generated during the course of the project were deposited to a public repository (DDBJ Read Archive, DRA) and/or provided at the FANTOM5 web resource ( Here we describe the data with the processing details and quality metrics.


Sample collection

Sample collection was performed as described previously13,15. Briefly, primary cells were purchased as purified RNAs or frozen cells, or obtained as described previously19,​20,​21,​22,​23,​24 through collaboration in the consortium. Purchased cells were cultured according to the manufacturer’s instructions and miRNeasy kit (QIAGEN) was used for RNA extraction. Human post mortem tissue RNAs were purchased or obtained through the Dutch Brain bank. Tissues collected through the consortium were snap-frozen in liquid nitrogen, transferred into Lysing Matrix D tubes (MP Biomedicals, Santa Ana, CA) containing chilled Trizol (Gibco), homogenized by FastPrep Homogenizer (Thermo Savant), and centrifuged. miRNeasy kit (QIAGEN) was used for RNA extraction from cultured cell lines as well as frozen cell line stocks.

For the purchased samples, lot or catalogue numbers were recorded where available. Of the collected RNAs, those with more than 1 μg, were measured by Agilent BioAnalyzer (Agilent Technologies, Santa Clara, CA) and Nanodrop spectrophotometer (Thermo Fisher Scientific, Wilmington, DE) to check RIN (RNA integrity) score and the absorbance ratio of A260/A230 and A260/A280. The rest of the samples were directly subjected to the CAGE library production to avoid wasting material. All 2,832 profiled samples are summarized in Table 1.

Table 1: Summary of FANTOM5 phase 1 and phase 2 samples.

Single molecule CAGE and data processing

HeliScopeCAGE libraries were prepared, sequenced, and processed as described previously13,15. Most of the RNAs were subjected to the automated HeliScopeCAGE protocol7, except for RNAs with less than 1 μg that were subjected to the manual protocol optimized for low quantity RNAs8. The resulting libraries were measured by OliGreen fluorescence assay kit (Life Technologies), and sequenced by following the manufacturer’s instructions (LB-016_01, LB-017_01, and LB-001_04 (ref. 13). RNAs extracted from mouse whole body embryo E17.5 (called internal control) were systematically subjected to this workflow, with one per a sequencing run.

The produced data were processed as previously described13,15. Briefly, reads corresponding to ribosomal RNA were removed by using the program rRNAdust (, remaining reads were aligned to the reference genome of human and mouse (hg19 or mm9) by using Delve25, and alignments with a quality of less than 20 (<99% chance of true) or a sequence identity of less than 85% were discarded. Frequencies of the CAGE read 5′ ends were counted to give a unit of CAGE tag start site (CTSS), a single base-pair on the reference genome. The entire flow of the data is illustrated in Fig. 1, and the number of CAGE profiles (equivalent to CTSS files) is summarized in Table 2.

Figure 1: Data processing scheme.
Figure 1

Data processing scheme from sample preparation to CAGE peak expression and annotation. Sky blue and beige color indicate locations storing the data, the FANTOM5 data archive (Data Citation 1: figshare, Data Citation 10: LSDB Archive and in DDBJ Sequence Read Archive (Data Citation 2: DDBJ Sequence Read Archive DRA000991Data Citation 9: DDBJ Sequence Read Archive DRA002748) respectively.

Table 2: Sequence files (CTSS files).

Identification of peaks and their annotations

Non-overlapping peaks based on the all CAGE profiles were identified by using DPI (decomposition-based peak identification, method and annotated as previously described13,15. A ‘robust’ threshold, for which a peak must include a CTSS with more than 10 read counts and 1 TPM (tags per million) at least one sample, was employed to define a stringent subset of the CAGE peaks. The robust peaks were associated with known transcripts, such as RefSeq26, UCSC known gene27, GENCODE28, Ensembl29, and mRNAs (full-length cDNA clones), based on their 5′-end proximity to the peaks. Official gene symbols, Entrez Gene IDs, and protein (UniProt) IDs associated with the transcripts were retrieved and assigned as part of annotation. In addition to these associations, human readable names and descriptions were assigned to each of the CAGE peaks. Peaks were given a name in the form pN@GENE, where GENE indicates gene symbol or transcript name and N indicates the rank in the ranked list of promoter activities for that gene. For example, p1@SPI1 represent the peak with the highest number of observation (that is, read counts) in all of the FANTOM5 CAGE profiles, among the peaks associated with SPI1 gene.

Peak identification with the same method and the same threshold was performed two times; the first was for ‘snapshot’ samples (phase 1), and the second was for the entire samples from both the ‘snapshot’ and ‘time course’ studies (phase 2). We integrated these two peak sets into a hybrid set consisting of all the phase 1 peaks over the robust threshold and a subset of phase 2 peaks that did not overlap with the phase 1 peaks. Annotation of phase1 peaks was used in the hybrid set, called phase 1+2 peaks, which provide a consistent reference in the definition of promoters.

Quantification of promoter activities

All the obtained CAGE profiles were subjected to the peak identification, even if they have some issues in quality, since all of them still represent independent observations of RNA 5′-ends. However promoter activities (that is, expression levels of CAGE peaks) were quantified only in the samples satisfying the following criteria: RIN score greater than 6, more than 500,000 successfully aligned reads to the genome, and more than 50% of the successful alignments are close to 5′-end of RefSeq gene model, for expression analysis requiring reliable quantification. After discarding a few CAGE profiles of low quality, read counts for individual CTSSs belonging to the same peak were summed up, normalization (or scaling) factors were calculated with RLE (Relative Log Expression)30 method by edgeR31, and tags per million (that is, counts per million) was computed as expression levels.

The RLE normalization was first performed within the phase 1 samples. The naïve application of this to the entire data sets, consisting of phase 1 and phase 2 samples, might cause inconsistencies in expression levels between the two normalizations. To avoid this, we took the geometric mean of CAGE peak read counts across the phase 1 samples and used it as the reference expression for a normalization factor calculation in the same manner as RLE method. This enabled us to keep the expression levels of phase 1 as they were, and to adjust the expression levels of the phase 2 samples to be comparable15.

Code availability

All software used in this study are publicly available. rRNAdust, for removing ribosomal RNA, is available at Mapping software Delve is available at The program to perform DPI, decomposition-based peak identification, method is available at

Data Records

Data record 1: Metadata

Two types of metadata are available at figshare and LSDB Archive (Data Citation 1: figshare, 10). One is for the samples, including their origins and extracted RNA. The other is for the CAGE assay, including the result of RNA quality check, library production, and post-processing of the CAGE tag sequences. Both of them are described in SDRF (Sample and Data Relationship Format)32. Sample metadata for human and mouse are ‘HumanSamples2.0.sdrf.xlsx’ and ‘MouseSamples2.0.sdrf.xlsx’, respectively. The metadata for the CAGE assay are available as ‘*sdrf.txt’.

Data record 2: CAGE profiles

All of the CAGE sequences, their alignment to the genomes, and CTSS frequencies are available at DDBJ DRA (DDBJ Sequence Read Archive) (Data Citation 2: DDBJ Sequence Read Archive DRA000991Data Citation 9: DDBJ Sequence Read Archive DRA002748). The accession number of each file is summarized in ‘DRA*.txt’ at figshare (Data Citation 1: figshare

Data record 3: CAGE peaks

Genomic coordinates, annotations and expressions of the CAGE peaks are available as ‘*phase1and2combined_coord.bed.gz’, ‘*phase1and2combined_ann.txt.gz’, and ‘*phase1and2combined_tpm.osc.txt.gz’ respectively at figshare (Data Citation 1: figshare Genomic coordinates are formatted in BED format, and the others are formatted in OSCtable (Order Switchable Column table). The detail of the OSCtable format is available at

Technical Validation

RNA quality

Measured RNA qualities at the second check (that is, immediately before the CAGE library production) are shown in Fig. 2a–c. RNA Integrity Number (RIN) score, measured using an Agilent Bioanalyzer, was 8.96 on average (standard deviation 1.19), absorbance ratio of 260/230 nm (A260/A230) and 260/280 nm (A260/A280) were on average 2.01 (standard deviation 0.53) and 2.13 (standard deviation 0.14) respectively. These figures indicate that the majority of the RNAs were processed in good quality.

Figure 2: RNA and mapping quality control.
Figure 2

Distribution of RIN score (a), A260/A230 (b), A260/A280 (c), mapped reads (d), and promoter rate (e) for samples used for FANTOM5 expression analysis.

Mapped reads

The number of CAGE reads successfully aligned with the genome and the ratio of CAGE reads hitting conventional promoters are shown in Fig. 2d,e. The average number of mapped reads is 4,208,291 per CAGE profile. Of the 2,522 profiles, 98.3% (2,478) consists of at least 500,000 successfully aligned reads, which was a criterion of profiles used for expression analysis13. The average ratio of promoter-hitting reads is 76.5, and 98.6% of the all profiles (2,437/2,472) have more than 50% promoter-hitting rate, which was another criterion of profiles used for expression analysis13.

Sample identity

Hierarchical clustering of the 126 mouse primary cells13 within the phase 1 was shown in Fig. 3, and the same clustering of the 571 human primary cells13 was in Supplementary Fig. 1. The average linkage method was applied to log-scale expression (TPM) profiles at promoter-level, and sample identities were assessed by expression of marker genes and also by manual inspection of the hierarchical clustering. The figures show that majority of biological replicates belonged to the same branch of the tree, that is, the same cluster, except for samples with a low number of mapped read counts.

Figure 3: Hierarchical clustering of primary cells.
Figure 3

Hierarchical clustering of primary cell samples of mouse based on logarithm of expression (TPM). Color shows anatomical categories of samples.

Usage Notes

As well as providing access to individual data files, we also set up a series of interfaces as described in the FANTOM web resource18,33. TET (Table Extraction Tool) provides an interface to obtain a subset of data by specifying the desired columns and rows. The BioMart interface34, and FANTOM5 SSTAR (Semantic catalog of Samples, Transcription initiation And Regulators) provides the metadata of the profiled samples35. The CAGE profile on the genomic axis is visible in ZENBU17 with its interactive interface and also in the UCSC genome browser36 via track data hub37.

Additional Information

How to cite this article: Noguchi, S. et al. FANTOM5 CAGE profiles of human and mouse samples. Sci. Data 4:170112 doi: 10.1038/sdata.2017.112 (2017).

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    , & Paradigm shifts in genomics through the FANTOM projects. Mamm Genome 26, 391–402 (2015).

  2. 2.

    The RIKEN Genome Exploration Research Group Phase II Team and the FANTOM Consortium. Functional annotation of a full-length mouse cDNA collection. Nature 409, 685–690 (2001).

  3. 3.

    The FANTOM Consortium and the RIKEN Genome Exploration Research Group Phase I & II Team. Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature 420, 563–573 (2002).

  4. 4.

    RIKEN Genome Exploration Research Group and Genome Science Group (Genome Network Project Core Group) and the FANTOM Consortium. Antisense transcription in the mammalian transcriptome. Science 309, 1564–1566 (2005).

  5. 5.

    The FANTOM Consortium and RIKEN Genome Exploration Research Group and Genome Science Group (Genome Network Project Core Group). The Transcriptional Landscape of the Mammalian Genome. Science 309, 1559–1563 (2006).

  6. 6.

    et al. Genome-wide analysis of mammalian promoter architecture and evolution. Nat Genet 38, 626–635 (2006).

  7. 7.

    et al. Automated Workflow for Preparation of cDNA for Cap Analysis of Gene Expression on a Single Molecule Sequencer. PLoS ONE 7, e30809 (2012).

  8. 8.

    et al. Unamplified Cap Analysis of Gene Expression on a single-molecule sequencer. Genome Res 21, 1150–1159 (2011).

  9. 9.

    The FANTOM Consortium and the Riken Omics Science Center. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat Genet 41, 553–562 (2009).

  10. 10.

    et al. Tiny RNAs associated with transcription start sites in animals. Nat Genet 41, 572–578 (2009).

  11. 11.

    et al. The regulated retrotransposon transcriptome of mammalian cells. Nat Genet 41, 563–571 (2009).

  12. 12.

    et al. An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man. Cell 140, 744–752 (2010).

  13. 13.

    The FANTOM Consortiumand the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

  14. 14.

    et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

  15. 15.

    et al. Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science 347, 1010–1014 (2015).

  16. 16.

    , , , & MOIRAI: a compact workflow system for CAGE analysis. BMC Bioinformatics 15, 144 (2014).

  17. 17.

    et al. Interactive visualization and analysis of large-scale sequencing datasets using ZENBU. Nat Biotechnol 32, 217–219 (2014).

  18. 18.

    et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol 16, 22 (2015).

  19. 19.

    et al. Perlecan Domain IV Peptide Stimulates Salivary Gland Cell Assembly In Vitro. Tissue Eng Part A 15, 3309–3320 (2009).

  20. 20.

    , , , & Vitamin D increases expression of cathelicidin in cultured sebocytes. Arch Dermatol Res 304, 627–632 (2012).

  21. 21.

    , , , & In Vitro Characterization of the Cytokine Profile of the Epithelial Cell Rests of Malassez. J Periodontol 79, 912–919 (2008).

  22. 22.

    , , & Growth and differentiation of mouse tracheal epithelial cells: selection of a proliferative population. Am J Physiol Lung Cell Mol Physiol 283, L1315–L1321 (2002).

  23. 23.

    , , , & Hepatocyte growth factor promotes lymphatic vessel formation and function. EMBO J 24, 2885–2895 (2005).

  24. 24.

    , & Control of regulatory T cell development by the transcription factor Foxp3. Science 299, 1057–1061 (2003).

  25. 25.

    et al. Landscape of transcription in human cells. Nature 489, 101–108 (2012).

  26. 26.

    , , & NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res 40, D130–D135 (2012).

  27. 27.

    et al. The UCSC known genes. Bioinformatics 22, 1036–1046 (2006).

  28. 28.

    et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol 7(Suppl 1): S4.1–S9 (2006).

  29. 29.

    et al. Ensembl 2011. Nucleic Acids Res 39, 800–806 (2011).

  30. 30.

    & Differential expression analysis for sequence count data. Genome Biol 11, R106 (2010).

  31. 31.

    , & edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  32. 32.

    et al. A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB. BMC Bioinformatics 7, 489 (2006).

  33. 33.

    et al. Update of the FANTOM web resource: high resolution transcriptome of diverse cell types in mammals. Nucleic Acids Res 45, D737–D743 (2017).

  34. 34.

    et al. The BioMart community portal: An innovative alternative to large, centralized data repositories. Nucleic Acids Res 43, W589–W598 (2015).

  35. 35.

    et al. FANTOM5 transcriptome catalog of cellular states based on Semantic MediaWiki. Database 2016, article ID baw105 (2016).

  36. 36.

    et al. The UCSC Genome Browser database: 2016 update. Nucleic Acids Res 44, D717–D725 (2016).

  37. 37.

    et al. Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser. Bioinformatics 30, 1003–1005 (2014).

Download references

Data Citations

  1. 1.

    Noguchi, S. figshare (2017)

  2. 2.

    DDBJ Sequence Read Archive DRA000991 (2013)

  3. 3.

    DDBJ Sequence Read Archive DRA001026 (2013)

  4. 4.

    DDBJ Sequence Read Archive DRA001027 (2013)

  5. 5.

    DDBJ Sequence Read Archive DRA001028 (2013)

  6. 6.

    DDBJ Sequence Read Archive DRA002216 (2014)

  7. 7.

    DDBJ Sequence Read Archive DRA002711 (2014)

  8. 8.

    DDBJ Sequence Read Archive DRA002747 (2014)

  9. 9.

    DDBJ Sequence Read Archive DRA002748 (2014)

  10. 10.

    LSDB Archive (2016)


FANTOM5 was made possible by a Research Grant for RIKEN Omics Science Center from MEXT to Y.H. and a grant of the Innovative Cell Biology by Innovative Technology (Cell Innovation Program) from the MEXT, Japan to Y.H. It was also supported by Research Grants for RIKEN Preventive Medicine and Diagnosis Innovation Program to Y.H. and RIKEN Centre for Life Science Technologies, Division of Genomic Technologies (from the MEXT, Japan).

Author information


  1. Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa 230-0045, Japan

    • Shuhei Noguchi
    • , Takahiro Arakawa
    • , Masaaki Furuno
    • , Akira Hasegawa
    • , Fumi Hori
    • , Sachi Ishikawa-Kato
    • , Tsugumi Kawashima
    • , Miki Kojima
    • , Ri-ichiroh Manabe
    • , Mitsuyoshi Murata
    • , Sayaka Nagao-Sato
    • , Hiromi Nishiyori-Sueki
    • , Shohei Noma
    • , Mizuho Sakai
    • , Naoko Suzuki
    • , Michihira Tagami
    • , Shoko Watanabe
    • , Alessandro Bonetti
    • , Yuki Hasegawa
    • , Yuri Ishizu
    • , Jay W. Shin
    • , Imad Abugessaisa
    • , Erik Arner
    • , Jayson Harshbarger
    • , Atsushi Kondo
    • , Timo Lassmann
    • , Marina Lizio
    • , Serkan Sahin
    • , Jessica Severin
    • , Harukazu Suzuki
    • , Naoto Kondo
    • , Masayoshi Itoh
    • , Carsten O. Daub
    • , Takeya Kasukawa
    • , Hideya Kawaji
    • , Piero Carninci
    •  & Alistair R.R. Forrest
  2. RIKEN Omics Science Center, Yokohama, Kanagawa 230-0045, Japan

    • Takahiro Arakawa
    • , Shiro Fukuda
    • , Masaaki Furuno
    • , Akira Hasegawa
    • , Fumi Hori
    • , Sachi Ishikawa-Kato
    • , Kaoru Kaida
    • , Ai Kaiho
    • , Mutsumi Kanamori-Katayama
    • , Tsugumi Kawashima
    • , Miki Kojima
    • , Atsutaka Kubosaki
    • , Ri-ichiroh Manabe
    • , Mitsuyoshi Murata
    • , Sayaka Nagao-Sato
    • , Kenichi Nakazato
    • , Noriko Ninomiya
    • , Hiromi Nishiyori-Sueki
    • , Shohei Noma
    • , Eri Saijyo
    • , Akiko Saka
    • , Mizuho Sakai
    • , Christophe Simon
    • , Naoko Suzuki
    • , Michihira Tagami
    • , Shoko Watanabe
    • , Shigehiro Yoshida
    • , Alessandro Bonetti
    • , Yuki Hasegawa
    • , Yuri Ishizu
    • , Sugata Roy
    • , Alka Saxena
    • , Jay W. Shin
    • , Naoko Takahashi
    • , Erik Arner
    • , Jayson Harshbarger
    • , Atsushi Kondo
    • , Timo Lassmann
    • , Marina Lizio
    • , Serkan Sahin
    • , Thierry Sengstag
    • , Jessica Severin
    • , Hisashi Shimoji
    • , Masanori Suzuki
    • , Harukazu Suzuki
    • , Jun Kawai
    • , Naoto Kondo
    • , Masayoshi Itoh
    • , Carsten O. Daub
    • , Hideya Kawaji
    • , Piero Carninci
    • , Alistair R.R. Forrest
    •  & Yoshihide Hayashizaki
  3. Department of Medicine, Karolinska Institutet, 141 86, Stockholm, Sweden

    • Peter Arner
    • , Anna Ehrlund
    •  & Niklas Mejhert
  4. Karolinska University Hospital, Center for Metabolism and Endocrinology, 141 86, Stockholm, Sweden

    • Peter Arner
    • , Anna Ehrlund
    •  & Niklas Mejhert
  5. Scottish Centre for Regenerative Medicine, University of Edinburgh, 5 Little France Drive, Edinburgh EH16 4UU, UK

    • Richard A. Axton
    •  & Lesley M. Forrester
  6. Department of Dermatology and Allergy, Charite University Medicine Berlin, Charitéplatz 1, 10117 Berlin, German

    • Magda Babina
    •  & Sven Guhl
  7. The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, Midlothian EH25 9RG, UK

    • J. Kenneth Baillie
    • , Ailsa J. Carlisle
    • , Lynsey Fairbairn
    • , Malcolm E. Fisher
    • , David A. Hume
    • , Kim M. Summers
    •  & Andru Tomoiu
  8. Australian Infectious Diseases Research Centre, The University of Queensland, St Lucia, QLD 4072, Australia

    • Timothy C. Barnett
    •  & Kelly J. Hitchens
  9. School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, QLD 4072, Australia

    • Timothy C. Barnett
  10. Bio-Rad Laboratories Pty Ltd, Hercules, California 94547, USA

    • Anthony G. Beckhouse
  11. The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD 4102 Australia

    • Antje Blumenthal
  12. IRCCS Fondazione Santa Lucia, Via del Fosso di Fiorano 64, 00143 Rome, Italy

    • Beatrice Bodega
    •  & Valerio Orlando
  13. Australian Institute for Bioengineering and Nanotechnology (AIBN), University of Queensland, Brisbane, St Lucia, QLD 4072, Australia

    • James Briggs
    • , Kelly J. Hitchens
    • , Dmitry A. Ovchinnikov
    •  & Ernst Wolvetang
  14. Division of Immunology, Institute of Infectious Diseases and Molecular Medicine (IDM), University of Cape Town, Anzio Road, Observatory 7925, Cape Town, South Africa

    • Frank Brombacher
    • , Reto Guler
    • , Suzana Savvi
    •  & Anita Schwegmann
  15. Immunology of Infectious Diseases, Faculty of Health Sciences, South African Medical Research Council (SAMRC), University of Cape Town, Anzio Road, Observatory 7925, Cape Town, South Africa

    • Frank Brombacher
    • , Reto Guler
    • , Suzana Savvi
    •  & Anita Schwegmann
  16. International Centre for Genetic Engineering and Biotechnology, Cape Town Component, Anzio Road, Observatory 7925, Cape Town, South Africa

    • Frank Brombacher
    • , Reto Guler
    • , Suzana Savvi
    •  & Anita Schwegmann
  17. Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands

    • Hans C. Clevers
    •  & Marc van de Wetering
  18. University Medical Centre Utrecht, Postbus 85500, 3508 GA Utrecht, The Netherlands

    • Hans C. Clevers
  19. Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11797, USA

    • Carrie A. Davis
    •  & Thomas Gingeras
  20. Institute of Pharmaceutical Sciences, ETH Zurich, Vladimir-Prelog-Weg 3, HCI H 303, 8093 Zurich, Switzerland

    • Michael Detmar
    •  & Sarah Klein
  21. Gastroenterology, Research Center for Hepatitis and Immunology, Research Institute National Center for Global Health and Medicine, Ichikawa, Chiba 272-8516, Japan

    • Taeko Dohi
    •  & Yuki I. Kawamura
  22. Department of Otology and Laryngology, Harvard Medical School, Boston, Massachusetts 02114, USA

    • Albert S.B. Edge
    •  & Judith S. Kempfle
  23. Department of Internal Medicine III, University Hospital Regensburg, F.-J.-Strauss Allee 11, D-93053 Regensburg, Germany

    • Matthias Edinger
    • , Michael Rehli
    •  & Christian Schmidl
  24. RCI Regensburg Centre for Interventional Immunology, University Hospital Regensburg, F.-J.-Strauss Allee 11, D-93053 Regensburg, Germany

    • Matthias Edinger
    •  & Michael Rehli
  25. Department of Biosciences and Nutrition, Karolinska Institutet, Halsovagen 7-9, SE-141 83 Huddinge, Sweden

    • Karl Ekwall
    • , Juha Kere
    • , Andreas Lennartsson
    •  & Carsten O. Daub
  26. RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan

    • Mitsuhiro Endoh
    • , Jun-ichi Furusawa
    • , Tomokatsu Ikawa
    • , Hiroshi Kawamoto
    • , Haruhiko Koseki
    • , Shigeo Koyasu
    • , Kazuyo Moro
    • , Hiroshi Ohno
    •  & Mariko Okada-Hatakeyama
  27. Laboratory for Neuronal Differentiation and Regeneration, RIKEN Center for Developmental Biology, Chuou-ku, Kobe 650-0047, Japan

    • Hideki Enomoto
    • , Mitsuru Morimoto
    •  & Yohei Yonekura
  28. Department of Bioinformatics, Medical Research Institute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan

    • Afsaneh Eslami
    •  & Hiroshi Tanaka
  29. F.M. Kirby Neurobiology Center, Children's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Michela Fagiolini
  30. The University of Texas Health Science Center at Houston, Houston, TX 77251-1892, USA

    • Mary C. Farach-Carson
  31. Cancer Biology Program, Mater Medical Research Institute, South Brisbane, Queensland 4101, Australia

    • Geoffrey J. Faulkner
  32. Berlin Institute for Medical Systems Biology, Max Delbrueck Center, Robert Roessle Str.10, 13125 Berlin, Germany

    • Carmelo Ferrai
    • , Kelly J. Morris
    •  & Ana Pombo
  33. Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, Sendai, Miyagi 980-8575, Japan

    • Rie Fujita
    • , Hironori Satoh
    • , Jun Takai
    •  & Masayuki Yamamoto
  34. Experimental Immunology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands

    • Teunis B. Geijtenbeek
    •  & Linda M. van den Berg
  35. Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, Vancouver, British Columbia V5Z 4H4, Canada

    • Daniel Goldowitz
    • , Thomas J. Ha
    •  & Peter G. Zhang
  36. Neuroscience, SISSA, Via Bonomea 265, 34136 Trieste, Italy

    • Stefano Gustincich
    •  & Silvia Zucchelli
  37. Department of Neuroscience and Brian Technologies, Italian Istitute of Technology, Via Morego 30, Genova, Italy

    • Stefano Gustincich
  38. Department of Experimental Immunology, World Premier International Immunology Frontier Research Center, Osaka University, Suita, Osaka 565-0871, Japan

    • Masahide Hamaguchi
    • , Hiromasa Morikawa
    • , Naganari Ohkura
    •  & Shimon Sakaguchi
  39. RIKEN Center for Life Science Technologies, Wako, Saitama 351-0198, Japan

    • Mitsuko Hara
    • , Soichi Kojima
    •  & Xian-Yang Qin
  40. Melanoma Research Center, The Wistar Institute, Philadelphia, Pennsylvania 19104, USA

    • Meenhard Herlyn
  41. German Center for Neurodegenerative Diseases (DZNE)-Tübingen, Otfried Müller Straße 23, 72076 Tübingen, Germany

    • Peter Heutink
    •  & Patrizia Rizzu
  42. Laboratory Animal Research Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan

    • Chieko Kai
    • , Toshiyuki Nakamura
    • , Hiroki Sato
    • , Takaaki Sugiyama
    •  & Misako Yoneda
  43. International Research Center for Infectious Diseases, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan

    • Chieko Kai
  44. Institute of Health and Biomedical Innovation, Queensland University of Technology, Translational Research Institute, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia

    • Tony J. Kenna
  45. Department of Genetics and Molecular Medicine, King's College London, Guy’s St Thomas Street, London, UK

    • Juha Kere
  46. Centre for Vascular Research, University of New South Wales, Sydney, New South Wales 2052, Australia

    • Levon M. Khachigian
    •  & Margaret Patrikakis
  47. Vascular Biology and Translational Research, School of Medical Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia

    • Levon M. Khachigian
  48. Division of Cellular Therapy and Division of Stem Cell Signaling, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo 108-8639, Japan

    • Toshio Kitamura
    •  & Fumio Nakahara
  49. Harry Perkins Institute of Medical Research, Perth, WA 6009, Australia

    • S. Peter Klinken
    • , Louise N. Winteringham
    •  & Alistair R.R. Forrest
  50. Respiratory Medicine, University of Nottingham, Hucknall Road, Nottingham NG5 1PB, UK

    • Alan J. Knox
  51. Dermatology, School of Medicine Kyungpook National University, Jung-gu, Daegu 41944, Korea

    • Weonju Lee
  52. Griffith University, Brisbane, Queensland 4111, Australia

    • Alan Mackay-sim
  53. Division of Functional Genomics and Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama 350-1241, Japan

    • Yosuke Mizuno
    • , Yutaka Nakachi
    •  & Yasushi Okazaki
  54. Center for Radioisotope Sciences, Tohoku University Graduate School of Medicine, Sendai, Miyagi 980-8575, Japan

    • Hozumi Motohashi
    •  & Hiroo Toyoda
  55. Anatomy and Embryology, Leiden University Medical Center, Einthovenweg 20, P.O. Box 9600, 2300 RC Leiden, The Netherlands

    • Christine L. Mummery
    •  & Robert Passier
  56. Division of Translational Research, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama 350-1241, Japan

    • Yutaka Nakachi
    •  & Yasushi Okazaki
  57. Cell Engineering Division, RIKEN BioResource Center, Tsukuba, Ibaraki 305-0074, Japan

    • Yukio Nakamura
  58. Department of Clinical Molecular Genetics, School of Pharmacy, Tokyo University of Pharmacy and Life Sciences, Hachioji, Tokyo 192-0392, Japan

    • Tadasuke Nozaki
  59. Department of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi 980-8573, Japan

    • Soichi Ogishima
  60. Department of Biochemistry, Ohu University School of Pharmaceutical Sciences, Koriyama, Fukushima 963-8611 Japan

    • Mitsuhiro Ohshima
  61. Insitute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan

    • Mariko Okada-Hatakeyama
  62. Environmental Epigenetics Program, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

    • Valerio Orlando
  63. University of Delaware, Newark, DE 19716 USA

    • Swati Pradhan-Bhatt
  64. Hjelt Institute, Department of Forensic Medicine, University of Helsinki, Kytosuontie 11, 003000 Helsinki, Finland

    • Antti Sajantila
  65. Laboratorio Nazionale CIB, Padriciano, 99 34149, Trieste, Italy

    • Claudio Schneider
  66. Department of Orthopedic, Trauma and Reconstructive Surgery, Charite Universitatsmedizin Berlin, Charitéplatz 1, 10117 Berlin, German

    • Gundula G. Schulze-Tanzil
  67. International Research Center for Medical Sciences (IRCMS), Kumamoto University, Chuo-ku, Kumamoto 860-0811, Japan

    • Guojun Sheng
  68. Department of Clinical Study, Center for Advanced Medical Innovation, Kyushu University, Higashi-Ku, Fukuoka 812-8582, Japan

    • Daisuke Sugiyama
  69. Graduate School of Pharmaceutical Sciences, Nagoya University, Nagoya, Aichi 464-8601, Japan

    • Hideki Tatsukawa
  70. Laboratorio Nazionale del Consorzio Interuniversitario per le Biotecnologie (LNCIB), Padriciano 99, 34149 Trieste, Italy

    • Roberto Verardo
  71. QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia

    • Dipti Vijayan
  72. Centre for Stem Cell Systems, Department of Anatomy and Neuroscience, MDHS, University of Melbourne, Melbourne, VIC 3010, Australia

    • Christine A. Wells
  73. Department of Biochemistry, Nihon University School of Dentistry, Chiyoda-ku, Tokyo 101-8310, Japan

    • Yoko Yamaguchi
  74. Center for Clinical and Translational Reseach, Kyushu University Hospital, Higashi-Ku, Fukuoka 812-8582, Japan

    • Chiyo Yanagi-Mizuochi
  75. Telethon Kids Institute, the University of Western Australia, Perth, WA, Australia

    • Timo Lassmann
  76. Preventive medicine and applied genomics unit, RIKEN Advanced Center for Computing and Communication, Yokohama, Kanagawa 230-0045, Japan

    • Hisashi Shimoji
    •  & Hideya Kawaji
  77. RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama 351-0198, Japan

    • Jun Kawai
    • , Masayoshi Itoh
    • , Hideya Kawaji
    •  & Yoshihide Hayashizaki


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Samples were provided by P. Arner, R. Axton, M. Babina, J. Baillie, T. Barnett, A. Beckhouse, A. Blumenthal, B. Bodega, A. Bonetti, J. Briggs, F. Brombacher, A. Carlisle, H. Clevers, C. Davis, M. Detmar, T. Dohi, A. Edge, M. Edinger, A. Ehrlund, K. Ekwall, M. Endoh, H. Enomoto, A. Eslami, M. Fagiolini, L. Fairbairn, M. Farach-Carson, G. Faulkner, C. Ferrai, M. Fisher, L. Forrester, R. Fujita, J. Furusawa, T. Geijtenbeek, T. Gingeras, D. Goldowitz, S. Guhl, R. Guler, S. Gustincich, T. Ha, M. Hamaguchi, M. Hara, Y. Hasegawa, M. Herlyn, P. Heutink, K. Hitchens, D. Hume, T. Ikawa, Y. Ishizu, C. Kai, H. Kawamoto, Y. Kawamura, J. Kempfle, T. Kenna, J. Kere, L. Khachigian, T. Kitamura, S. Klein, S. Klinken, A. Knox, S. Kojima, H. Koseki, S. Koyasu, W. Lee, A. Lennartsson, A. Mackay-sim, N. Mejhert, Y. Mizuno, H. Morikawa, M. Morimoto, K. Moro, K. Morris, H. Motohashi, C. Mummery, Y. Nakachi, F. Nakahara, T. Nakamura, Y. Nakamura, T. Nozaki, S. Ogishima, N. Ohkura, H. Ohno, M. Ohshima, M. Okada-Hatakeyama, Y. Okazaki, V. Orlando, D. Ovchinnikov, R. Passier, M. Patrikakis, A. Pombo, S. Pradhan-Bhatt, X. Qin, M. Rehli, P. Rizzu, S. Roy, A. Sajantila, S. Sakaguchi, H. Sato, H. Satoh, S. Savvi, A. Saxena, C. Schmidl, C. Schneider, G. Schulze-Tanzil, A. Schwegmann, G. Sheng, J. Shin, D. Sugiyama, T. Sugiyama, K. Summers, N. Takahashi, J. Takai, H. Tanaka, H. Tatsukawa, A. Tomoiu, H. Toyoda, M. van de Wetering, L. van den Berg, R. Verardo, D. Vijayan, C. Wells, L. Winteringham, E. Wolvetang, Y. Yamaguchi, M. Yamamoto, C. Yanagi-Mizuochi, M. Yoneda, Y. Yonekura, P. Zhang, S. Zucchelli; CAGE data was produced by T. Arakawa, S. Fukuda, M. Furuno, A. Hasegawa, F. Hori, S. Ishikawa-Kato, K. Kaida, A. Kaiho, M. Kanamori-Katayama, T. Kawashima, M. Kojima, A. Kubosaki, R. Manabe, M. Murata, S. Nagao-Sato, K. Nakazato, N. Ninomiya, H. Nishiyori-Sueki, S. Noma, E. Saijyo, A. Saka, M. Sakai, C. Simon, N. Suzuki, M. Tagami, S. Watanabe, S. Yoshida; Data quality was assessed by S. Noguchi, I. Abugessaisa, E. Arner, J. Harshbarger, A. Kondo, T. Lassmann, M. Lizio, S. Sahin, T. Sengstag, J. Severin, H. Shimoji, H. Kawaji, A. Forrest; Data description is achieved by S. Noguchi, T. Kasukawa, H. Kawaji; Project is organized by M. Suzuki, H. Suzuki, J. Kawai, N. Kondo, M. Itoh, C. Daub, T. Kasukawa, H. Kawaji, P. Carninci, A. Forrest, Y. Hayashizaki.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Hideya Kawaji.

Supplementary information

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