利用长读长测序解码细胞身份博士研究员
PhD Studentship: One read, one cell: Decoding cell identity with long-read sequencing
原帖优先：申请材料、截止时间与资格以原帖和学校官方说明为准。
AI 中文速览
- 研究内容
- 本项目旨在开发和应用计算工具，通过分析长读长测序数据（如UK Biobank中的~50,000个Oxford Nanopore基因组），在无需物理分离细胞的情况下解析复杂人类组织的表观基因组。主要任务包括评估预测准确性、建立参考细胞类型甲基化图谱、将其应用于大型生物样本库数据集，并开展细胞类型特异性的全表观基因组关联研究。
- 申请条件
- 申请人需要具备坚实的数学和编程技能、对长读长测序等前沿生物技术有浓厚兴趣，并愿意结合细胞生物学来解释研究发现。
- 待遇
- 提供2026/7年度的学费资助以及每年21,805英镑的生活津贴（stipend）。
- 申请方式
- 原文未说明具体的申请方式与截止提交细节。
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
岗位信息
- 截止
- （Europe/London） 剩 39 天
- 学科
- 生命科学
- 合同类型
- 雇佣合同
- 原文薪资
- GBP 21,805 / 年（税前）
- 税后月薪（估）
- ¥13,700；房租后 ¥5,400
- 估算假设
- 单身、无子女、雇佣合同的粗略估算，以学校 offer 为准；扣除率 18%；汇率日期 2026-10-01
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- 内容更新
- 来源
- jobs.ac.uk（英国博士项目及学术招聘） · 最近核对 2026-10-29
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判定依据（原文摘录）
- is_phd
PhD Studentship: One read, one cell: Decoding cell identity with long-read sequencing
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The student will work across two world-leading research groups, the Complex Disease Epigenetics Group at Exeter and the Birney Group at EBI.
原文
Background
What if we could resolve the epigenome of individual cell types from complex human tissues without ever having to physically separate the cells?
The majority of disease-associated genetic variants exert their effects by modulating gene expression, driving considerable interest in profiling epigenomic variation at scale. Epigenomic patterns, chemical modifications to DNA and chromatin that controls which genes are active, are highly cell-type specific. When studying epigenetics, the tissue choice strongly influences the findings. A critical limitation of existing studies is that bulk tissues (such as whole blood) comprise a mixture of cell types, potentially masking true biological signals.
Historically, resolving this has required the physical isolation of individual cell populations, costly, low-throughput and impractical for population-level studies. Long-read sequencing changes this paradigm by detecting epigenetic modifications alongside genetic sequencing, and by capturing continuous stretches of a single DNA molecule so that each read provides a snapshot of its cell type of origin. We have developed a framework that classifies cell type for individual reads by comparing methylation patterns to reference profiles for different cell types. This enables the generation of cell type-specific epigenetic profiles directly from long-read sequencing data, bypassing the need for experimental cell separation. The project will exploit ~50,000 Oxford Nanopore long-read genomes available in UK Biobank, providing a unique opportunity to resolve cell-type-specific epigenetic variation at population scale.
Objectives
• Evaluate the predictive accuracy and optimal conditions for computational cell-type-of-origin prediction from long-read sequencing data.
• Curate a comprehensive atlas of reference cell-type methylomes to generalise the approach across multiple tissues.
• Apply the framework to large population biobank datasets to generate cellular composition estimates and cell-type-specific epigenetic profiles at scale.
• Leverage these cell-type-resolved profiles to conduct epigenome-wide association studies in large cohorts, identifying epigenetic effects linked to disease in specific cell types.
Methodology
The student will apply our suite of computational tools to blood-based long-read sequencing datasets, benchmarking cell-type-of-origin predictions against reference purified datasets to determine optimal conditions for the approach. This phase will include systematic fine-tuning of the prediction framework. The validated tools will then be applied to large biobank cohorts to generate cell composition predictions and cell-type-specific epigenetic profiles at population scale. Finally, these profiles will be used to perform a cell-type-specific epigenome-wide association study, identifying methylation changes associated with phenotypes of health and disease such as smoking and age, that are resolved to individual cell types.
Interdisciplinarity, Facilities and Resources
The supervisory team work at the intersection of functional genomics, mathematics and computer science, with expertise in large complex genomic datasets, method development, and advanced data science and AI methodologies. The student will work across two world-leading research groups, the Complex Disease Epigenetics Group at Exeter and the Birney Group at EBI. They will need strong mathematical and programming skills, a genuine interest in cutting-edge biotechnologies such as long-read sequencing, and willingness to engage with cell biology to interpret their findings. While predominantly a data-science project, long-read sequencing facilities are available to generate reference datasets where appropriate, and computational work will be supported by local (ISCA) and regional (ISAMBARD 3) HPC clusters. The student will benefit from training through Exeter's Coding for Reproducible Research programme and EBI's suite of resources.
Funded by: MRC Medical Research Council £21,805. The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7.