Diversity of <scp>U1</scp> Small Nuclear <scp>RNAs</scp> and Diagnostic Methods for Their Mutations

U1 small nuclear RNA (snRNA) mutations are recurrent non-coding alterations found in various malignancies, yet their identification has proven challenging due to their repetitive nature. We characterized the complex interindividual diversity and genomic architecture of U1 snRNA loci using sequencing data and a pangenome reference. Our analysis uncovered copy number variations and the diversity of single-nucleotide variants in regions not predicted to have significant functional impact. Compared to traditional linear reference-based analyses for mutations, the pangenome graph demonstrated the best accuracy, successfully identifying previously undetectable mutations. This underscores the utility of pangenome graph references for cancer genome research, particularly in repetitive and highly diverse genomic regions. Additionally, we developed mutation detection methods employing targeted capture sequencing, rapid quantitative polymerase chain reaction, and a machine learning approach based on splicing patterns, all exhibiting high precision in identifying U1 snRNA mutations. Our findings elucidate the structural complexity of U1 snRNA loci and establish robust methodologies for precise mutation detection in these regions.

Authors

Institutions

Publication Details

Journal
Cancer Science
Published
2025-05-27
DOI
https://doi.org/10.1111/cas.70110
Citations
1
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
0.81

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Diversity of U1 Small Nuclear RNAs and Diagnostic Methods for Their Mutations

Takako Yoshioka, Fumiharu Ohka, Yonehiro Kanemura, Ryuta Saito et al.
1 citations
Cancer Science
RNA Research and Splicing
0.81
article

Diversity of U1 Small Nuclear RNAs and Diagnostic Methods for Their Mutations

Takako Yoshioka, Fumiharu Ohka, Yonehiro Kanemura, Ryuta Saito, Junko Hirato, Takuma Nakashima, Ryota Takeuchi, Yusuke Funakoshi, T Miyauchi, Yasuhiro Kojima, Yuriko Sugihara, Yoshitaka Narita, Hajime Okita, Sachi Maeda, Y Watanabe, Hiromichi Suzuki
article en
1 citations

Abstract

U1 small nuclear RNA (snRNA) mutations are recurrent non-coding alterations found in various malignancies, yet their identification has proven challenging due to their repetitive nature. We characterized the complex interindividual diversity and genomic architecture of U1 snRNA loci using sequencing data and a pangenome reference. Our analysis uncovered copy number variations and the diversity of single-nucleotide variants in regions not predicted to have significant functional impact. Compared to traditional linear reference-based analyses for mutations, the pangenome graph demonstrated the best accuracy, successfully identifying previously undetectable mutations. This underscores the utility of pangenome graph references for cancer genome research, particularly in repetitive and highly diverse genomic regions. Additionally, we developed mutation detection methods employing targeted capture sequencing, rapid quantitative polymerase chain reaction, and a machine learning approach based on splicing patterns, all exhibiting high precision in identifying U1 snRNA mutations. Our findings elucidate the structural complexity of U1 snRNA loci and establish robust methodologies for precise mutation detection in these regions.

Cancer ScienceVol. 116(8)
Kyorin University (JP), Keio University (JP), National Cancer Center (US), Osaka National Hospital (JP), National Center For Child Health and Development (JP), National Cancer Center Hospital East (JP), National Cancer Research Institute (GB), Nagoya University (JP)
Takeda Science Foundation, Cell Science Research Foundation, Uehara Memorial Foundation, Japan Agency for Medical Research and Development, National Cancer Center Japan, Japan Health Research Promotion Bureau, Astellas Foundation for Research on Metabolic Disorders, Kobayashi Foundation for Cancer Research, MSD Life Science Foundation, Public Interest Incorporated Foundation, Japan Society for the Promotion of Science, Japan Science and Technology Agency
Openalex Percentile: Top 28%
RNA Research and Splicing
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.