Visualisation and analysis of RNA-Seq assembly graphs
Visualisation and analysis of RNA-Seq assembly graphs
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RNA-Seq 组装图的可视化和分析
DOI：
10.1101/409573
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Nazarie F
中科院分区：
文献类型：
--
作者：
Nazarie F
RNA-Seq是一种功能强大的转录组分析技术，能够发现和定量转录本。虽然最常用于基因水平的定量，但数据可用于分析转录异构体。然而，当底层转录物组装复杂时，当前的可视化方法可能是有限的，剪接事件是解释的挑战。在这里，我们报告了一种基于图形的可视化方法的发展，作为从短读RNA-Seq数据中理解转录多样性的补充方法。在将读数映射到参考基因组之后，对映射到给定基因的所有读数进行读数间比较，产生读数之间的加权相似性矩阵。这用于生成RNA组装图，其中节点表示读段，边表示它们之间的相似性得分。所得到的图形在3D空间中可视化，以更好地理解它们有时庞大而复杂的拓扑结构，其他信息覆盖在节点上，例如转录模型。在这里，我们展示了这种方法的实用性，包括这些图的不寻常的结构，以及它们如何用于识别组装中的问题，转录本和剪接变体内的重复序列。我们相信这种方法有可能显着提高我们对转录本复杂性的理解。
RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript diversity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce anRNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.
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影响因子：
12.3
作者：
Conesa A;Madrigal P;Tarazona S;Gomez-Cabrero D;Cervera A;McPherson A;Szcześniak MW;Gaffney DJ;Elo LL;Zhang X;Mortazavi A
通讯作者：
Mortazavi A
影响因子：
7
作者：
Huang, Weichun;Marth, Gabor
通讯作者：
Marth, Gabor
影响因子：
3
作者：
Barann M;Zimmer R;Birzele F
通讯作者：
Birzele F
影响因子：
11.8
作者：
Starostina, Natalia G.;Simpliciano, Jennifer M.;McGuirk, Michael A.;Kipreos, Edward T.
通讯作者：
Kipreos, Edward T.
DOI：
10.1007/978-1-4939-3167-5_14
发表时间：
2016-01-01
期刊：
PLANT BIOINFORMATICS: METHODS AND PROTOCOLS, 2ND EDITION
影响因子：
--
作者：
Milne, Iain;Bayer, Micha;Marshall, David
通讯作者：
Marshall, David