SeqFold: genome-scale reconstruction of RNA secondary structure integrating high-throughput sequencing data.
SeqFold: genome-scale reconstruction of RNA secondary structure integrating high-throughput sequencing data.
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DOI：
10.1101/gr.138545.112
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发表时间：
2013-02
期刊：
影响因子：
7
通讯作者：
Chang HY
中科院分区：
文献类型：
--
作者：
Ouyang Z;Snyder MP;Chang HY
我们提出了一种综合的方法，SeqFold，它结合了高通量RNA结构分析数据与计算预测的RNA二级结构的基因组规模重建。SeqFold将实验RNA结构信息转换为结构偏好谱（SPP），并使用它来选择代表结构系综的稳定RNA结构候选者。在高维分类框架下，SeqFold有效地将给定的SPP与从Boltzmann加权系综中采样的最可能的结构集群相匹配。SeqFold能够整合不同类型的RNA结构分析数据，包括RNA结构的平行分析（PARS）、通过引物延伸测序（SHAPE-Seq）分析的选择性2′-羟基酰化、通过深度测序生成的片段化测序（FragSeq）数据以及常规SHAPE数据。使用广泛的mRNA和非编码RNA的已知结构作为基准，我们证明了SeqFold在准确性上优于或匹配现有方法，并且对实验数据中的噪声更具鲁棒性。应用SeqFold重建酵母转录组的二级结构揭示了RNA二级结构对基因调控的多种影响，包括翻译效率，转录起始和蛋白质-RNA相互作用。SeqFold可以很容易地适应任何新类型的高通量RNA结构分析数据，并广泛适用于分析任何转录组中的RNA结构。
We present an integrative approach, SeqFold, that combines high-throughput RNA structure profiling data with computational prediction for genome-scale reconstruction of RNA secondary structures. SeqFold transforms experimental RNA structure information into a structure preference profile (SPP) and uses it to select stable RNA structure candidates representing the structure ensemble. Under a high-dimensional classification framework, SeqFold efficiently matches a given SPP to the most likely cluster of structures sampled from the Boltzmann-weighted ensemble. SeqFold is able to incorporate diverse types of RNA structure profiling data, including parallel analysis of RNA structure (PARS), selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq), fragmentation sequencing (FragSeq) data generated by deep sequencing, and conventional SHAPE data. Using the known structures of a wide range of mRNAs and noncoding RNAs as benchmarks, we demonstrate that SeqFold outperforms or matches existing approaches in accuracy and is more robust to noise in experimental data. Application of SeqFold to reconstruct the secondary structures of the yeast transcriptome reveals the diverse impact of RNA secondary structure on gene regulation, including translation efficiency, transcription initiation, and protein-RNA interactions. SeqFold can be easily adapted to incorporate any new types of high-throughput RNA structure profiling data and is widely applicable to analyze RNA structures in any transcriptome.
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影响因子：
16
作者：
Kanhere A;Viiri K;Araújo CC;Rasaiyaah J;Bouwman RD;Whyte WA;Pereira CF;Brookes E;Walker K;Bell GW;Pombo A;Fisher AG;Young RA;Jenner RG
通讯作者：
Jenner RG
影响因子：
1.8
作者：
HOFACKER, IL;FONTANA, W;SCHUSTER, P
通讯作者：
SCHUSTER, P
影响因子：
14.9
作者：
Griffiths-Jones, S;Bateman, A;Eddy, SR
通讯作者：
Eddy, SR
影响因子：
14.9
作者：
Giegerich, R;Voss, B;Rehmsmeier, M
通讯作者：
Rehmsmeier, M
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y