PO.BCS01.10 · 生物信息与计算
特定于方案且基于覆盖度的RNA-seq指标刻画跨队列的RNA完整性特征
Protocol-specific and coverage-based RNA-seq metrics characterize RNA integrity signatures across cohorts
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：RNA降解深刻影响转录本定量与下游生物学解释。现有的RNA质量评估方法在很大程度上局限于poly(A)富集的mRNA-seq，无法推广到全RNA-seq，导致跨测序方案的质量评估不一致。我们假设碱基分辨率的RNA-seq覆盖度谱能够捕获特定于方案的质量特征。通过对这些覆盖度模式中的意外变异进行建模，可以在针对各方案量身定制的共同分析框架内，对mRNA-seq和全RNA-seq（包括冷冻与FFPE样本）准确评估RNA-seq质量。
方法：对于mRNA-seq和全RNA-seq，我们通过显式建模与降解相关及其他低质量模式，量化每个碱基覆盖度中的异常变异。具体而言，对于mRNA-seq，我们开发了降解评分（Degradation Score，DS），用于估计转录本上读取覆盖度的位置性衰减。对于全RNA-seq，我们引入了窗口变异系数（window Coefficient of Variation，wCV），这是CV指标的一种变体，用于捕获覆盖度的不均匀性，反映降解样本在基因体上表现出更高变异性的倾向。我们将这些指标应用于超过2,600份跨越多种测序策略与队列的RNA-seq谱，包括TCGA（mRNA-seq）、CALGB（新鲜冷冻全RNA-seq）与ALCHEMIST（FFPE全RNA-seq）。
结果：在各数据集中，我们基于覆盖度的指标与常规QC测量的相关性（mRNA-seq中|r|=0.52-0.59，全RNA-seq中0.43-0.89；p<0.001）强于它们彼此之间的相关性（mRNA-seq中|r|=0.24-0.44，全RNA-seq中0.44-0.88；p<0.001）。我们还识别出一组样本，其常规QC标记为高降解但覆盖度谱看似完整，反之亦然，凸显了依赖方案的差异。人工检查证实，DS高（mRNA-seq）或wCV高（全RNA-seq）的样本中异常变异明显更大。使用RNA质量分层间标准化的类内平方和，我们发现被我们的指标归类为高质量的样本在各种归一化方法下始终保持已知的亚型结构。
结论：这些特定于方案、基于覆盖度的测量方法为跨测序策略与实验条件评估RNA完整性提供了一个连贯且可重复的框架。将降解感知的QC纳入RNA-seq流程可提高大型癌症基因组学研究中转录组分析的可解释性、可比性与生物学保真度。
查看英文原文 English abstract
Background: RNA degradation profoundly impacts transcript quantification and downstream biological interpretation. Existing RNA quality assessment methods are largely limited to poly(A)-selected mRNA-seq and do not generalize to total RNA-seq, resulting in inconsistent quality evaluations across sequencing protocols. We hypothesize that base-resolution RNA-seq coverage profiles capture protocol-specific quality signatures. By modeling unexpected variation in these coverage patterns, RNA-seq quality can be accurately assessed for both mRNA-seq and total RNA-seq (including frozen and FFPE samples) within a common analytical framework tailored to each protocol.
Methods: For both mRNA-seq and total RNA-seq, we quantify abnormal variations in per-base coverage by explicitly modeling degradation-related and other low-quality patterns. For mRNA-seq, specifically, we developed the Degradation Score (DS), which estimates the positional decay in read coverage along transcripts. For total RNA-seq, we introduced the window Coefficient of Variation (wCV), a variant of CV metric that captures coverage nonuniformity, reflecting the tendency of degraded samples to show elevated variability across gene bodies. We applied these metrics to over 2,600 RNA-seq profiles spanning multiple sequencing strategies and cohorts, including TCGA (mRNA-seq), CALGB (fresh frozen total RNA-seq), and ALCHEMIST (FFPE total RNA-seq).
Results: Across datasets, our coverage-based metrics showed stronger correlations with conventional QC measures (|r|=0.52-0.59 in mRNA-seq, 0.43-0.89 in total RNA-seq; p<0.001) than the correlations among themselves (|r|=0.24-0.44 in mRNA-seq, 0.44-0.88 in total RNA-seq; p<0.001). We also identified a set of samples in which conventional QC flagged high degradation but coverage profiles appeared intact, and vice versa, highlighting protocol-dependent discrepancies. Manual inspection confirmed markedly greater aberrant variability in samples with high DS (mRNA-seq) or high wCV (total RNA-seq). Using standardized within class sum of squares across RNA quality strata, we found that samples classified as high-quality by our metrics consistently preserved known subtype structure across normalization methods.
Conclusions: These protocol-specific, coverage-based measures provide a coherent and reproducible framework for evaluating RNA integrity across sequencing strategies and experimental conditions. Incorporating degradation-aware QC into RNA-seq pipelines improves interpretability, comparability, and biological fidelity of transcriptomic analyses in large cancer genomics studies.
利益披露 Disclosure
M. Yeon, None.