DBKERO: Regulatory Omics Databasefor Analyzing Transcriptional Consequences of Human SNVs

ABSTRACT〇Yutaka Suzuki1, Hiroyuki Wakaguri2, Riu Yamashita3, Shin Kawano4, Katsuya Tsuchihara5,Kenta Nakai6 and Sumio Sugano1
1Department of Medical Genome Sciences, Graduate School of Frontier Sciences, The University of Tokyo,
2Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo,
3Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan,
4Database Center for Life Science, Research Organization of Information and Systems, Chiba,
5Division of TR, The Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Chiba
6Human Genome Center, The Institute of Medical Science, The University of Tokyo

DBTSS (http://dbtss.hgc.jp/) was originally constructed as acollection of uniquely determined transcriptional start sites(TSSs) in humans and some other species in 2002. Since then, it
has been regularly updated and in recent updates epigenetic
information has also been incorporated because suchinformation is useful for characterizing the biological relevanceof these TSSs/downstream genes. In the newest release, Release 9,
we further integrated public and original single nucleotide
variation (SNV) data into our database. For our original data,
we generated SNV data from genomic analyses of various cancer
types, including 97 lung adenocarcinomas and 57 lung small cell
carcinomas from Japanese patients as well as 26 cell lines of lungcancer origin. In addition, we obtained publically available SNV
data from other cancer types and germline variations in total of
11,322 individuals. With these updates, users can examine theassociation between sequence variation pattern in clinical lungcancers with its corresponding TSS-seq, RNA-seq, ChIP-seq andBS-seq data. Consequently, DBTSS is no longer a mere storagesite for TSS information but has evolved into an integrative
platform of a variety of genome activity data.

Kashiwa Encyclopedia of Regulatory Omics(KERO@http://dbtss.hgc.jp/)

Data Contents of the KERO (Summary)Single Cell and Long-read Datasets
Database Schema and New Datasets(A) Overall structure of the database is illustrated. How the Japanese clinical omics information is associated with comprehensive omics information from the model systems isshown. This database also included information newly available from single cell and long read technologies and multi-omics perturbation by chemical compounds.
Different categories of the datasets are shown in different colors.
(B) The tours of the MT1A gene. The guide how to show the similar results in the database are illustrated. For more details see the web(http://dbtss.hgc.jp/docs/help_2017.html).
Follow the link as illustrated in Supplementary Figure 3:
1.
Input ‘MT1A’ to the keyword field box at the top left part of the top page.
2.
Select the ‘GemCode Phasing Patterns’ of the H1975 cell line. Find a mutation (chr16:56,638,440, C>CG) in the haplotype 2 of the MT1A upstream region (upper leftpanel).
3.
Add epigenome and transcriptome information of the H1975 cell line around the mutation (upper right panel on blue background). Select H3K27ac ChIP-seq and DNA
methylation of BS-seq for epigenome patterns, and TSS and rpkm of RNA-seq for transcriptome patterns.
4.
To view the data of expression variation in individual single cells, display the ‘rpkm distribution’ of the C1, bead-seq and Chromium single cell platforms. To view thedistribution of the expression levels of the MT1A gene in each cell, select ‘the C1 system’ (lower left panel on yellow background). For information of a large number of cells,
select ‘the Chromium system’. Go to the single cell viewer from ‘the summary’ link and see the expression variation of MT1A gene on the two dimensional t-SNE plot (lowerright panel on yellow background).

AB

CTop page of KERO.
A simple search for‘TSS Viewer’ and‘Genome Viewer’ canbemadeby
specifying a keyword,such as a gene name
‘BRAF’intheDatabase Search atthe left frame (redbox). Search by‘SNV Summary inCancers’
and‘Pathway Map’ canbe made from thepositions indicated byorange and purple
boxes, respectively.

DBTSS Browser(A) A part of the TSS Viewer display for the BRAF gene. The overview and the detailed positions of the TSSs are shown in the upper and lower panels,respectively. Many of the fields are expandable. (B) The default display of Genome Viewer for the BRAF gene. Displayed items are as indicated in themargin. The displayed items can be controlled from the panels located under the ‘Select track items’ headline. (C) A sample output of SNV information for
the BRAF gene. Surrounding region of a previously reported cancer driver mutation (V600E of the BRAF gene; highlighted in red box), is displayed.

RDF for Integration of the Databases

AB

Figure 2 Pathway Searchand Promoter Search(A) Upper panel: A part of theMutation frequency table for
the BRAF gene. Enrichedfields are as highlighted;
lower panel: Summary ofmulti-omics
data
mainlycollected from cell lines. (B)Pathway Map representationof characteristic genes. In thisexample, gene expression
level (in RPKM) of nodegenes
inalung
adenocarcinoma cell line,
LC2/ad, in the ErbB/HER
signaling pathway is shown.
Further links will appear when
the users click the circle
corresponding to each gene.

Figure 3 Schema for RDF of ChIP data

All contents and raw data freely downloadable from DBTSS at:
http://kero.hgc.jp/

Licensed under a Creative Commons表示4.0国際ライセンス(c)2017 Yutaka Suzuki （Graduate School of Frontier Sciences, TheUniversity of Tokyo）