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Due to system maintenance, the application system, application review by the Data Access Committee will be unavailable during the following period.
Schedule: October 5th (Mon), 2026, 9:00 - October 7th (Wed), 2026, 15:00 (JST)
We apologize for any inconvenience this may cause and appreciate your understanding.

We are currently receiving a large number of applications for data submission, and the review process is taking longer than usual.We sincerely apologize for the delay and kindly ask for your understanding. When submitting an application, we would greatly appreciate it if you could allow sufficient time for the processing.

Following a change to our organizational structure effective April 1, 2026, this division has been renamed from the "Database Center for Life Science, Joint Support-Center for Data Science Research" to the "Database Division for Life Science (DBCLS), BioData Science Initiative (BSI), National Institute of Genetics (NIG)". Where the former name still appears in the guidelines, please read it as the new name.

Research ID

hum0485-v1Release info

Latest

Research title

Genome sequencing analysis

Research overview

Aims
Immunological and genomic analyses to predict chemotherapy response in gastric cancer
Methods
shallow whole-genome sequencing (sWGS), RNA sequencing (RNA-seq)
Participants/materials
Tumor and non-tumor tissues of 65 Japanese gastric cancer patients
URL
N/A

Datasets

CartDataset IDType of dataAnalysis methodAccess criteriaDate published
JGAD000895NGS (WGS, RNA-seq)
  • WGS
  • RNA-seq
Controlled-access (Type I)2025-04-30

Data provider

Principal investigator
Hidewaki Nakagawa
Affiliation
Laboratory of Genome Sequencing Analysis, RIKEN Center for Integrative Medical Sciences

Research projects

No research projects.

Grants

NameTitleProject number
Project for Cancer Research and Therapeutic Evolution (P-CREATE), Japan Agency for Medical Research and Development (AMED)
Search for seeds for the development of novel immunotherapies and combined immunotherapies by cancer genome analysis
  • JP20cm0106552

Related publications

TitleDOIDataset ID
Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures
https://doi.org/10.1007/s10120-024-01569-4 (opens in a new tab)

Controlled access users

No use of the controlled access data has been recorded.