Summarizing and correcting the GC content bias in high-throughput sequencing.
Summarizing and correcting the GC content bias in high-throughput sequencing.
复制标题
DOI：
10.1093/nar/gks001
复制
发表时间：
2012-05
影响因子：
14.9
通讯作者：
Speed TP
中科院分区：
文献类型：
--
作者：
Benjamini Y;Speed TP
GC含量偏倚描述了在Illumina测序数据中发现的片段计数（读段覆盖度）和GC含量之间的依赖性。这种偏差可以支配关注于测量基因组内片段丰度的分析的感兴趣信号，例如拷贝数估计（DNA-seq）。样本之间的偏倚并不一致;对于在单个样本中消除偏倚的最佳方法也没有达成共识。我们分析了GC偏置模式中的不确定性，并找到了这个单峰曲线族的紧凑描述。完整DNA片段的GC含量，而不仅仅是测序读数，对片段计数影响最大。这种GC效应是单峰的：富含GC的片段和富含AT的片段在测序结果中均未被充分代表。这一经验证据加强了PCR是GC偏倚最重要原因的假设。我们提出了一个模型，在碱基对水平上产生预测，允许链特定的GC效应校正，无论下游平滑或分箱。这些GC建模考虑因素可以为其他高通量测序分析提供信息，例如ChIP-seq和RNA-seq。
GC content bias describes the dependence between fragment count (read coverage) and GC content found in Illumina sequencing data. This bias can dominate the signal of interest for analyses that focus on measuring fragment abundance within a genome, such as copy number estimation (DNA-seq). The bias is not consistent between samples; and there is no consensus as to the best methods to remove it in a single sample. We analyze regularities in the GC bias patterns, and find a compact description for this unimodal curve family. It is the GC content of the full DNA fragment, not only the sequenced read, that most influences fragment count. This GC effect is unimodal: both GC-rich fragments and AT-rich fragments are underrepresented in the sequencing results. This empirical evidence strengthens the hypothesis that PCR is the most important cause of the GC bias. We propose a model that produces predictions at the base pair level, allowing strand-specific GC-effect correction regardless of the downstream smoothing or binning. These GC modeling considerations can inform other high-throughput sequencing analyses such as ChIP-seq and RNA-seq.
登录
查看更多内容
影响因子：
3.7
作者：
Teytelman L;Ozaydin B;Zill O;Lefrançois P;Snyder M;Rine J;Eisen MB
通讯作者：
Eisen MB
影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
通讯作者：
Dudoit S
影响因子：
14.9
作者：
Nakamura K;Oshima T;Morimoto T;Ikeda S;Yoshikawa H;Shiwa Y;Ishikawa S;Linak MC;Hirai A;Takahashi H;Altaf-Ul-Amin M;Ogasawara N;Kanaya S
通讯作者：
Kanaya S
影响因子：
7
作者：
Li, Heng;Ruan, Jue;Durbin, Richard
通讯作者：
Durbin, Richard
影响因子：
12.3
作者：
Langmead B;Trapnell C;Pop M;Salzberg SL
通讯作者：
Salzberg SL