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DBTSS specifications


Unique identifier OMICS_01860
Alternative name DataBase of Transcriptional Start Sites
Restrictions to use None
Community driven No
Data access Browse
User data submission Not allowed
Version 10.1
Maintained Yes


  • Primates
    • Homo sapiens
  • Rodents
    • Mus musculus
    • Rattus norvegicus




  • person_outline Yutaka Suzuki

Additional information


Publications for DataBase of Transcriptional Start Sites

DBTSS citations


Rotating night work, lifestyle factors, obesity and promoter methylation in BRCA1 and BRCA2 genes among nurses and midwives

PLoS One
PMCID: 5464581
PMID: 28594926
DOI: 10.1371/journal.pone.0178792

[…] hole blood samples using QIAamp DNA Blood Mini Kit (Qiagen), according to the manufacturer’s instructions.The promoter region with the transcriptional start site of BRCA1 and BRCA2 was analyzed using DBTSS database (http://dbtss.hgc.jp) for further CpG island identification. Chemical modification of 500 ng of genomic DNA isolated from whole blood was performed with the use of Cells-to-CpG™ Bisulfi […]


Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks

PLoS One
PMCID: 5291440
PMID: 28158264
DOI: 10.1371/journal.pone.0171410

[…] et []. We also observed apparent better quality of a promoter identification program when using EPDnew data. For example, for CNN predictor computed on 1083 mouse TATA promoter regions extracted from DBTSS [], we also reached a pretty good performance on a test set of 271 promoters: Sn = 0.94, Sp = 0.94 and CC = 0.86. However, CNN model trained using mouse TATA promoters regions from EPDnew demons […]


Identification of Epigenetic Biomarkers of Lung Adenocarcinoma through Multi Omics Data Analysis

PLoS One
PMCID: 4820141
PMID: 27042856
DOI: 10.1371/journal.pone.0152918

[…] We used ChIP-seq data of 26 lung adenocarcinoma cell lines and SAEC from the DBTSS database () []. To identify the regions of histone modifications, “peaks” of ChIP-Seq tags were called by MACS2 for the six types of histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K9me3, H3 […]


Building the Frequency Profile of the Core Promoter Element Patterns in the Three ChromHMM Promoter States at 200bp Intervals: A Statistical Perspective

PMCID: 4742326
PMID: 26865847
DOI: 10.5808/GI.2015.13.4.152

[…] e the ChromHMM annotation of 9 cell lines (K562, GM12878, H1HESC, HEPG2, HMEC, HSMM, HUVEC, NHEK, and NHLF). The last two fields indicate the existence of DataBase of Human Transcription Start Sites (dbTSS) data [], and the GC ratio, respectively, where the hg19promoter file for dbTSS data was used for indexing (ftp://ftp.hgc.jp/pub/hgc/db/dbtss/dbtss_ver8/Sanger_data/). shows the summary of our p […]


The impact of sequence length and number of sequences on promoter prediction performance

BMC Bioinformatics
PMCID: 4686783
PMID: 26695879
DOI: 10.1186/1471-2105-16-S19-S5

[…] the studies conducted in this work, promoter and non-promoter sequences derived from human genome were used for datasets construction.Promoters were obtained from a set of sequences available in the DBTSS database [] (version 8.0), which has already been used in several other works [,,,], and is a set of approximately 98,000 experimentally validated promoter sequences with active TSS, where each […]


The Retinoblastoma Tumor Suppressor Transcriptionally Represses Pak1 in Osteoblasts

PLoS One
PMCID: 4640669
PMID: 26555075
DOI: 10.1371/journal.pone.0142406

[…] t site. The TRED accession numbers for the human and mouse Pak1 promoters are 114750 and 76974, respectively. Sequence alignment and identification of transcriptional start sites were performed using DBTSS (http://dbtss.hgc.jp). Genomatrix (www.genomatix.de/en/index.html) was used to identify transcription factor binding sites within the human and mouse Pak1 promoters. […]


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DBTSS institution(s)
Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Chiba, Japan; Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Chiba, Japan; Computational Regulatory Genomics Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan; Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan; Department of Pathology, Keio University School of Medicine, Tokyo, Japan; Institute of Molecular and Cellular Biosciences, the University of Tokyo, Tokyo, Japan; Division of Epigenomics and Development, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan; Department of Human Genetics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Chiba, Japan; Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
DBTSS funding source(s)
Supported by a Grant-in-Aid for Publication of Scientific Research Results (Databases) by Japan Society for the Promotion of Science, a Grant-in-aid for Scientific Research on Innovative Areas ‘Platform for Advanced Genome Science’ [16H06279] from the Ministry of Education, Culture, Sports, Science and Technology of Japan; Database Integration Coordination Program from Japan Science and Technology Agency; CREST/IHEC, Japan Agency for Medical Research and Development. Funding for open access charge: CREST/IHEC, Japan Agency for Medical Research and Development.

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