Publicly open databases, software and protocols contributed by RIKEN IMS researchers (Summary)Category
Database,
datasetDatabase,
datasetDatabase,
datasetDatabase,
datasetDatabase,
datasetDatabase,
dataset(2023/8)

Title

Description (English)

URL

FANTOM5

Atlases of mammalian promoters, enhancers, lncRNAs and miRNAs in FANTOM5

https://fantom.gsc.riken.jp/5/

A suite of web interfaces and databases for accessing to the FANTOM5 dataset
FANTOM5 web resource(SSTAR, FANTOM CAT Browser, FANTOM miRNA atlas, ZENBU (FANTOM5), Cellhttps://fantom.gsc.riken.jp/5/

Connectome Visualization, TET)

Related reference
https://doi.org/10.1186/s13059-014-0560-6
https://doi.org/10.1093/nar/gkaa1054

Resources, related information, orcomments
FANTOM5 Data resources:
https://fantom.gsc.riken.jp/5/data
files/

https://doi.org/10.1186/s13059-014-0560-6

FANTOM views:

https://doi.org/10.1093/nar/gkaa1054

https://fantom.gsc.riken.jp/views/
FANTOM6 Data resources:

FANTOM6

Expression profiles with large-scale lncRNA KD experiments in FANTOM6

https://fantom.gsc.riken.jp/6/

https://doi.org/10.1093/nar/gkaa1054

https://fantom.gsc.riken.jp/6/data
files/

FANTOM6 web resource
refTSSSCPortalen

A suite of web interfaces and databases for accessing to the FANTOM6 dataset(FANTOM6 Experimental Index, ZENBU-Reports (FANTOM6))
Reference data set of transcriptoin start sites in human and mouse
A cell-centric single-cell RNA-seq data repository leveraging cutting-edge QC
techniques and primary processing

https://fantom.gsc.riken.jp/6/

https://doi.org/10.1093/nar/gkaa1054

https://reftss.riken.jp/

https://doi.org/10.1016/j.jmb.2019.04.045

https://single-cell.riken.jp/

https://doi.org/10.1093/nar/gkx949

FANTOM views:
https://fantom.gsc.riken.jp/views/
refTSS data files:
https://reftss.riken.jp/datafiles/
SCPortalen web site: http://singlecell.riken.jp/
UCSC Genome Browser mirro

Database,

UCSC Genome Browser (asiandatasetmirror)

An office asian mirror of UCSC Genome Browser

sites:

https://genome-asia.ucsc.edu/

https://genome.ucsc.edu/mirror.ht
mlDatabase,

Single cell RNA-seq of Japanesedataset

COVID-19https://humandbs.biosciencedb
Large-scale single cell RNA-seq resource of the Japanese COVID-19 patientsc.jp/en/hum0197v15#JGAS000543

https://doi.org/10.1038/s41588-023-01375-1
https://doi.org/10.1038/s41586-022-05163-5
https://doi.org/10.1038/s41588-021-00931-x
https://doi.org/10.1038/s41586-022-05163-5
https://doi.org/10.1038/s41588-022-01213-w
https://doi.org/10.1038/s41467-022-32005-9

Database,dataset

Pheweb.jp

GWAS database of Biobank Japan and cross-population studies

https://pheweb.jp/

https://doi.org/10.1136/annrheumdis-2022-222460
https://doi.org/10.1038/s41467-021-21011-y
https://doi.org/10.1038/s41562-019-0805-1
https://doi.org/10.1093/hmg/ddab361
https://doi.org/10.1038/nature12873
https://doi.org/10.1101/2021.09.03.21262975

https://humandbs.biosciencedb
Database,
datasetc.jp/en/hum0197-v12#micro-

JMAG (Japanese Metagenome
Assembled Genomes Platform)

Gut metagenome and virome catalogue of the Japanese population

JVD (Japanese Virus Database)maghttps://humandbs.biosciencedb

https://doi.org/10.1016/j.xgen.2022.100241

c.jp/en/hum0197-v12#microvirus

Database,
datasethttps://humandbs.biosciencedb
Serum metabolome of Japanese

Serum metabolome data of the type 2 diabetes patients of Japanesec.jp/en/hum0372-v1 jgas000572

https://doi.org/10.1038/s43856-022-00231-3

Database,dataseteQTL & sQTL databases ofperipheral blood of Japanese COVID- eQTL & sQTL databases of peripheral blood obtained from the Japanese COVID-19
19Database,microRNA eQTL database ofdataset

peripheral blood of Japanese

Database,
datasetSoftware
SoftwareHuman reads in metagenome

Human read analysis tool in metagenome data
KIR gene variant calling pipeline

OMARU (Omnibus MetagenomeSoftwarewide Association study with

Metagenome-wide association study (MWAS) pipeline

RobUstness)
Software
Software
Software
Software
SoftwareTrans-Phar (Integration of TWAS andpharmacological database)
DEEP*HLA (DEEP learning for HLA
allelic imputation)

Drug discovery tool utilizing transcriptome-wide association study (TWAS)
Deep learning-based HLA imputation software

Obelisc (Observational linkage scan) Non-parametric linkage analysis utilizing IBD stretch
GREP (Genome for REPositioning
drugs)MIGWAS (miRNA-target gene
networks enrichment on GWAS)Drug discovery tool utilizing disease risks genesBiomarker microRNA screening tool based on GWAS resultseLD (entropy-based Linkage
Software

Disequilibrium index between

Entropy-based linkage disequilibrium (LD) index

multiallelic sites)
Software

Grimon (Graphical interface tovisualize multi-layer omics networks)

Software

MENTR

Software

MOPLINE

Graphical interface to visualize multi-layer omics networks

SkewC

Moirai2

Software

HDRGenome

https://doi.org/10.1093/hmg/ddab361

combined with high-confidence SV call selection and genotyping using short-read wholeA software suite for analysis of transcribed cis-regulatory elements in single cells

cells in scRNA-seq experiments. The method relies on the measure of skewness of theuman_reads_in_metagenome
https://github.com/saorisakaue/KIR_project
https://github.com/toshikishikawa/OMARU
https://github.com/konumat/Tr
ans-Pharhttps://github.com/tatsuhikonai
to/DEEP-HLA
https://github.com/qsonehara/
Obelischttps://github.com/saorisakaue/GREP
https://github.com/saorisakaue/MIGWASwww.sg.med.osakau.ac.jp/files/eLD.v1.0.zip
https://github.com/mkanai/grimonhttps://github.com/chunglab/SCAFE
https://github.com/LSBDT/SkewC
https://github.com/moirai2/moi
rai2development project with Dr. Okazaki in Juntendo University

https://doi.org/10.1093/nargab/lqac019

https://doi.org/10.1093/hmg/ddab049
https://doi.org/10.1038/s41467-021-21975-x
https://doi.org/10.1093/bioinformatics/btaa940
https://doi.org/10.1093/bioinformatics/btz166
https://doi.org/10.1093/nar/gky1066

https://doi.org/10.1038/s41439-018-0030-x

https://doi.org/10.1093/bioinformatics/bty488
https://doi.org/10.1038/s41551-022-00961-8

lab/MOPline

reconstructed software from Moirai1 with more functionalities
sequence data with Hamming Distance Ratio (HDR) method, which is a collaborative

https://doi.org/10.1016/j.xgen.2022.100101

https://github.com/stat-A scientific workflow system for large-scale biomedical datasets. Moirai2 is a fully
A tool to identify patient specirfic structure variants and deletions using whole genome

https://doi.org/10.1016/j.xgen.2022.100101

https://github.com/ytomofuji/HTR

gene coverage of each single cell as a quality measure
Softwarec.jp/en/hum0114-v3

https://github.com/koido/MENSkewC is a novel quality-assessment method to identify poor quality or affecting singleSoftware

https://humandbs.biosciencedb

expression at the cell type level

genome sequencing (WGS) dataSCAFE

c.jp/en/hum0197-v15 -MENTR is a machine-learning model which reliably links genome sequence and ncRNA
MOPLINE is a computational algorithm (MOPline) that includes missing call recovery

Softwarehttps://doi.org/10.1038/s41467-022-32276-2

hum0197.v6.eqtl.v1
KIR gene variant imputation reference panel

Receptor variant Analytical Platform)c.jp/en/hum0343-v2 - qtl
https://humandbs.biosciencedb

microRNA eQTL dabase of peripheral blood obtained from Japanese

KIR imputation reference panel

KIRAP (Killer Immunoglobulin-like

https://humandbs.biosciencedb

https://github.com/LSBDT/HDRGenome

SCAFE source code on Github:https://doi.org/10.1093/bioinformatics/btac644

https://github.com/chunglab/SCAFE

https://doi.org/10.1016/j.isci.2022.103777

https://doi.org/10.1186/1471-2105-15-144

Ready-to-use environment:
Software

Software
Software
Software
Software
SoftwarescDeepInsight

DeepInsight-3D
DeepFeature
DeepInsight
IMSindelDRAGON

A computational tool for analyzing single-cell RNA-seq data to identify cell types

https://pypi.org/project/scdeep
insight/

A computational tool for predicting response to anticancer drugs using multi-omics andhttps://github.com/alok-ai-

deep learning

lab/DeepInsight3D_pkg

A computational tool for extracting features from non-image data, such as genes, using

https://alok-ai-

deep learning

lab.github.io/deepfeature/

A computational analysis tool to transform non-image data such as genes for deep

https://alok-ai-learning

lab.github.io/DeepInsight/

A computational tool for analyzing RNA-seq data to detect intermediate size insertions

https://github.com/NCGG-and deletions

MGC/IMSindel/

DRAGON: Matlab package of DRAGON clustering approach

http://emu.src.riken.jp/DRAGO
N/

https://hub.docker.com/r/shangru
https://doi.org/10.1093/bib/bbad266

jia/scdeepinsightThe entire code base, including

https://doi.org/10.1038/s41598-023-29644-3
https://doi.org/10.1093/bib/bbab297
https://doi.org/10.1038/s41598-019-47765-6
https://doi.org/10.1038/s41598-018-23978-zhttps://doi.org/10.1186/s12859-017-1965-5

the implementation and the prehttps://github.com/alok-ailab/DeepInsight3D_pkg
https://alok-ailab.github.io/deepfeature/
http://emu.src.riken.jp/DeepInsig
ht/
https://github.com/NCGGMGC/IMSindel/
http://emu.src.riken.jp/DRAGON/

We present 2D-EM, a clustering algorithm approach designed for small sample size andhigh-dimensional datasets. To employ information corresponding to data distribution
Software2D-EM

and facilitate visualization, the sample is folded into its two-dimension (2D) matrix form

http://emu.src.riken.jp/2D-EM/ https://doi.org/10.1186/s12859-017-1970-8

http://emu.src.riken.jp/2D-EM/

http://emu.src.riken.jp/SIML/

https://doi.org/10.1186/s12859-016-1184-5

http://emu.src.riken.jp/SIML/

http://emu.src.riken.jp/HML/

https://doi.org/10.1109/TBME.2016.2542212

http://emu.src.riken.jp/HML/

http://emu.src.riken.jp/MCV/

https://doi.org/10.1038/ng.3547

http://emu.src.riken.jp/MCV/(or feature matrix). The maximum likelihood estimate is then estimated using a modified
expectation-maximization (EM) algorithm.
Considering the nature of biological data, we proposed a maximum likelihood clustering
approach using a stepwise iterative procedure. The advantage of this proposed method
SoftwareSIML

is that it not only uses the distance information, but also incorporate varianceinformation for clustering. This method is able to cluster when data appeared inoverlapping and complex forms.
Taking into consideration the nature of biological data, we propose a maximum
likelihood clustering scheme based on a hierarchical framework. Results: This method

SoftwareHML

can perform clustering even when the data belonging to different groups overlap. It canalso perform clustering when the number of samples is lower than the data
dimensionality.

SoftwareMCV

Software

ARETE

Pipeline to identify somatic substitutions and indels with VAF (variant allele frequency)from whole genome sequencing
Arete - an analysis toolkit for network-based gene prioritisation

http://emu.src.riken.jp/arete/arete.html

https://doi.org/10.1186/s13040-017-0141-9

http://emu.src.riken.jp/arete/arete.html

TFBIND : Software for searching transcription factor binding sites (including TATA
Software

TFBIND

Software

VCMM

boxes, GC boxes, CCAAT boxes, transcription start sites (TSS)). This tool uses weightmatrix in transcription factor database TRANSFAC R.3.4 developed by Dr. Wingender ethttps://tfbind.hgc.jp

https://doi.org/10.1093/bioinformatics/15.7.622

https://tfbind.hgc.jp

al, and the cut-offs originally estimated by our research.
VCMM: Variant Caller with Multinomial probabilistic Model

http://emu.src.riken.jp/VCMM/ https://doi.org/10.1038/srep02161

http://emu.src.riken.jp/VCMM/

http://emu.src.riken.jp/k_softwa

http://emu.src.riken.jp/k_software

res/s/

http://emu.src.riken.jp/k_softwa

http://emu.src.riken.jp/k_software

res/s/

Tools for identifying SNV (single nucleotide variations) and indels (insertions anddeletions) from short read sequence data. Open source code for k computer. ThisSoftware

NGS analyzer

program uses BWA for mapping, samtools for removing PCR duplication and VCMM
(Shigemizu et al submitted) for variant calling. Developed by Tesuo Abe, Eiji Nakamura,
Akihiro Fujimoto and Tatsuhiko Tsunoda.
Tools for association study with interaction. Open source code for k and other

Software

ExRAT

computers. Developed by Eiji Nakamura, Reiichiro Nakamichi, Takashi Morizono, andTatsuhiko Tsunoda.

Software
Software

SoftwareParaHaplo
hzAnalyzer

CisNetView

SoftwareMotifCombinator

Software

MOCSphaser

Software

CNVphaser

Tools for haplotype-based whole-genome association study using parallel computing.

http://emu.src.riken.jp/k_softwa

http://emu.src.riken.jp/k_software

Open source code for k and other computers. Developed by Kazuharu Misawa.

res/

s/

hzAnalyzer: R and Java for Detection and Analysis of Contiguous Homozygosity in High- http://emu.src.riken.jp/hzAnalyz
density Genotyping Datasets.
CisNetView: Tool to analyze gene networks by integrating genome-wide protein-protein,protein-DNA, and expression data.

er/

https://doi.org/10.1186/gb-2011-12-3-r21

http://emu.yokohama.riken.jp/CNetView/cgi-bin/Draw_GRNet.cgi

bin/Draw_GRNet.cgi
http://emu.src.riken.jp/combina

binding motifs, using regression methods.

torMOCSphaser: Tool to infer haplotypes composed of copy numbers alleles and SNP

http://emu.src.riken.jp/cgi-alleles.

bin/Download_MOCSphaser.cgi

CNVphaser: Tool to infer CNV haplotypes with variations in both copy numbers andhttp://emu.src.riken.jp/cgi-

nucleotide sequences.

bin/Download_CNVphaser.cgi

https://doi.org/10.1186/1471-2105-8-100
https://doi.org/10.1093/bioinformatics/btn242
https://doi.org/10.1016/j.ajhg.2008.06.021

https://figshare.com/articles/so
Software

CNVphaserPro

CNVphaserPro: CNV phasing tool from high-throughput data (with uncertainty).

ftware/CNVphaserPro/2228399 https://doi.org/10.1534/g3.111.000174
2MS-DIAL was launched as a universal program for untargeted metabolomics that
Software

MS-DIALsupports multiple instruments (GC/MS, GC/MS/MS, LC/MS, and LC/MS/MS) and MS
vendors (Agilent, Bruker, LECO, Sciex, Shimadzu, Thermo, and Waters)

ProtocolProtocolProtocolProtocolProtocolProtocolhttp://prime.psc.riken.jp/compms/msdial/main.html

CAGE（Cap Analysis of GeneA method for genome-wide identification of transcription start sites and simultaneously

https://fantom.gsc.riken.jp/protExpression）

measure gene expression

ocols/basic.html

A method to enrich NGS libraries for repeat element sequences prior to sequencing in

https://doi.org/10.1016/j.cell.2022.06.032

order to annotate non-reference repeat-mediated structural variants with downstream

https://doi.org/10.1016/j.xpro.2022.102027

bioinformatic pipelines.

https://doi.org/10.1002/ctm2.1226

CapTrap long read RNA sequencing

A method for genome-wide long read full length cap trapped RNA sequencing withhttps://doi.org/10.1038/s41467-021-23143-7(CTR-seq)

Oxford Nanopore Technology and PacBio.

https://doi.org/10.1101/2023.06.16.543444

SINEUPs

A method for antisense long noncoding RNAs that enhance protein production.

https://dx.doi.org/10.3791/58627

with SkewC

http://emu.src.riken.jp/cgibin/Download_CNVphaser.cgi
https://figshare.com/articles/software/CNVphaserPro/22283992

https://doi.org/10.1007/978-1-0716-1597-3_4

https://doi.org/10.1093/nar/gkaa1054

SkewC-based scRNA-seq QC

bin/Download_MOCSphaser.cgi

https://doi.org/10.1038/nmeth0306-211

localized in the nucleus to understand the function of RNA bound to chromatin

Computational approach to evaluate scRNA-seq data quality and gene body coverage

http://emu.src.riken.jp/cgi-

https://doi.org/10.1073/pnas.2136655100

https://doi.org/10.1038/s41467-020-14337-6

Retrotransposon capture

http://emu.src.riken.jp/combinator

https://doi.org/10.1038/s41587-020-0531-2

A method for comprehensively analyzing the interaction between RNA and chromatin

RADICL-seq/
http://emu.yokohama.riken.jp/Cis

isNetView/cgi-

MotifCombinator: Tool to search for significant combinations of transcription factor-

http://emu.src.riken.jp/hzAnalyzer

https://doi.org/10.1016/j.xpro.2022.102038

FANTOM web resource:
https://fantom.gsc.riken.jp/6/data
files/RADICL-Seq/