NBDC Research ID: hum0351.v1
SUMMARY
Aims: To unravel the genetic and environmental background of moyamoya disease and to understand the pathophysiology of the disease by utilizing radiological evaluations. We will identify susceptibility genes other than RNF213. We also assess whether genetics affects radiological characteristics including collateral development, tortuosity of the main trunk of the arteries, and the extent of stenosis.
Methods: DNAs were extracted from the fecal samples obtained from participants using NucleoSpin DNA Stool Kit. DNAs was amplified using the 2-step tailed PCR to target the V3-V4 regions of bacterial 16S rRNA. The PCR amplicons were pooled to construct the sequencing library and the quality of the library was confirmed using Fragment Analyzer and dsDNA915 Reagent Kit. Sequencing was performed using the MiSeq Reagent Kit v3.
Participants/Materials: Fecal samples were collected from 27 patients with moyamoya disease, 7 patients with non-moyamoya intracranial large artery disease, and 15 control individuals.
| Dataset ID | Type of Data | Criteria | Release Date |
|---|---|---|---|
| JGAS000540 | Metagenome | Controlled-access (Type I) | 2022/09/08 |
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MOLECULAR DATA
| Participants/Materials: |
27 patients with moyamoya disease (ICD10: I675) 7 patients with non-moyamoya intracranial large artery disease 15 control individuals |
| Targets | Metagenome (16S rRNA) |
| Target Loci for Capture Methods | V3-V4 |
| Platform | Illumina [MiSeq] |
| Library Source | DNA extracted from fecal samples |
| Cell Lines | - |
| Library Construction (kit name) | 2-step tailed PCR (Ex Taq HS) |
| Fragmentation Methods | - |
| Spot Type | Paired-end |
| Read Length (without Barcodes, Adaptors, Primers, and Linkers) | 300 bp |
| QC |
- Removal of primers by FASTXToolkit - Removal of low quality by sickle |
| Bacteria Identification Methods | Greengene (ver.13_8) |
| Japanese Genotype-phenotype Archive Dataset ID | JGAD000659 |
| Total Data Volume | 1.3 GB (txt) |
| Comments (Policies) | NBDC policy |
DATA PROVIDER
Principal Investigator: Yohei Mineharu
Affiliation: Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University
Project / Group Name： -
Funds / Grants (Research Project Number):
| Name | Title | Project Number |
|---|---|---|
| - | - | - |
PUBLICATIONS
| Title | DOI | Dataset ID | |
|---|---|---|---|
| 1 | |||
| 2 |
USRES (Controlled-access Data)
| Principal Investigator | Affiliation | Country/Region | Research Title | Data in Use (Dataset ID) | Period of Data Use |
|---|---|---|---|---|---|
