Reference Genome Choice Shapes RNA-Seq Quantification and Downstream Interpretation in Chinese Breast Cancer Patients
- 1China National Center for Bioinformation, , Chinese Academy of Sciences, Beijing 100101, China; (R.Z.); (Y.C.); (C.S.); (Z.H.); (Y.Y.); (J.C.)
- 2Breast Center, , Beijing 100044, China; (Y.P.); (M.Z.)
Abstract
Background/Objectives: Reference genome choice can introduce systematic bias into cancer transcriptomic analyses, particularly for populations underrepresented in standard references. However, its impact on downstream biological interpretation remains insufficiently characterized. Methods: We compared GRCh38, T2T-CHM13, and the East Asian-matched T2T-YAO using bulk RNA-seq data from 148 Chinese breast cancer patients and single-cell RNA-seq data comprising 65,968 cells from paired primary tumors and lymph node metastases. We assessed reference-dependent differences in read mapping, gene expression, and downstream transcriptomic analyses. Results: Compared with GRCh38, YAO reduced multi-mapped reads approximately three-fold and increased confidently exon-assigned reads. Re-mapping YAO-assigned reads to GRCh38 showed that 19.91% changed gene assignment, with a mean of 1016 genes per sample exhibiting more than ten-fold expression differences. Across the cohort, 8432 genes showed such discrepancies, and 68.49% overlapped low-mappability regions in YAO. These genes were enriched in receptor, ion channel, and drug metabolism functions. Reference-dependent expression differences propagated to differential expression, immune cell inference, fusion detection, and classifier performance. In single-cell analyses, T2T references retained more cells after quality filtering and resolved a fibroblast subpopulation missed by GRCh38. Conclusions: Reference genome selection substantially influences RNA-seq quantification and downstream biological interpretation in breast cancer. Population-matched and complete T2T reference genomes can reduce mapping ambiguity and improve the resolution of transcriptomic features, highlighting reference genome choice as an important and underappreciated source of systematic bias in cancer transcriptomics.
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BibTeX
@article{zhang2026reference,
title = {{Reference Genome Choice Shapes RNA-Seq Quantification and Downstream Interpretation in Chinese Breast Cancer Patients}},
author = {Ruiyang Zhang and Yuan Peng and Mengmeng Zhang and Yanan Chu and Changjun Shao and Zhuo Huang and Yiji Yang and Jing Chen and Yu Kang and Shu Wang},
journal = {Biomedicines},
year = {2026},
month = oct,
doi = {10.3390/biomedicines14102296},
url = {https://europepmc.org/article/PMC/PMC13701484},
note = {CC BY},
}
RIS
TY - JOUR
TI - Reference Genome Choice Shapes RNA-Seq Quantification and Downstream Interpretation in Chinese Breast Cancer Patients
AU - Ruiyang Zhang
AU - Yuan Peng
AU - Mengmeng Zhang
AU - Yanan Chu
AU - Changjun Shao
AU - Zhuo Huang
AU - Yiji Yang
AU - Jing Chen
AU - Yu Kang
AU - Shu Wang
PY - 2026
DA - 2026/10/01
JO - Biomedicines
DO - 10.3390/biomedicines14102296
UR - https://europepmc.org/article/PMC/PMC13701484
AB - Background/Objectives: Reference genome choice can introduce systematic bias into cancer transcriptomic analyses, particularly for populations underrepresented in standard references. However, its impact on downstream biological interpretation remains insufficiently characterized. Methods: We compared GRCh38, T2T-CHM13, and the East Asian-matched T2T-YAO using bulk RNA-seq data from 148 Chinese breast cancer patients and single-cell RNA-seq data comprising 65,968 cells from paired primary tumors and lymph node metastases. We assessed reference-dependent differences in read mapping, gene expression, and downstream transcriptomic analyses. Results: Compared with GRCh38, YAO reduced multi-mapped reads approximately three-fold and increased confidently exon-assigned reads. Re-mapping YAO-assigned reads to GRCh38 showed that 19.91% changed gene assignment, with a mean of 1016 genes per sample exhibiting more than ten-fold expression differences. Across the cohort, 8432 genes showed such discrepancies, and 68.49% overlapped low-mappability regions in YAO. These genes were enriched in receptor, ion channel, and drug metabolism functions. Reference-dependent expression differences propagated to differential expression, immune cell inference, fusion detection, and classifier performance. In single-cell analyses, T2T references retained more cells after quality filtering and resolved a fibroblast subpopulation missed by GRCh38. Conclusions: Reference genome selection substantially influences RNA-seq quantification and downstream biological interpretation in breast cancer. Population-matched and complete T2T reference genomes can reduce mapping ambiguity and improve the resolution of transcriptomic features, highlighting reference genome choice as an important and underappreciated source of systematic bias in cancer transcriptomics.
SN - 2227-9059
ER -