GAGrank: Software for Glycosaminoglycan Sequence Ranking Using a Bipartite Graph Model.
GAGrank: Software for Glycosaminoglycan Sequence Ranking Using a Bipartite Graph Model.
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DOI：
10.1016/j.mcpro.2021.100093
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Zaia J
中科院分区：
文献类型：
--
作者：
Hogan JD;Wu J;Klein JA;Lin C;Carvalho L;Zaia J
硫酸化糖胺聚糖（GAG）是长的线性多糖链，通常作为蛋白聚糖的聚糖部分存在。这些GAG的特征在于沿着链具有可变硫酸化和乙酰化模式的重复二糖单元。GAG长度和修饰模式对许多生理过程的生长因子信号传导机制具有深远影响。电子活化解离串联质谱法是一种非常有效的技术，用于分配的结构的糖胺聚糖酶;然而，手动解释所产生的复杂的串联质谱是一个困难和耗时的过程，推动了计算方法的发展，准确和有效的测序。我们最近发表了GAGfinder，这是第一个专为GAG串联质谱设计的峰拾取和元素组成分配算法。在这里，我们提出了GAGrank，一种新的基于网络的方法，用于确定GAG结构使用串联质谱使用GAGfinder提取的信息。GAGrank基于Google的PageRank算法，用于对搜索引擎输出的网站进行排名。特别是，它是BiRank的实现，是二分网络PageRank的扩展。在我们的实施方案中，两个分区包含给定GAG组成的每种可能的序列和使用GAG finder发现的串联MS片段。如果序列连接到许多重要片段，则给予它们更高的排名。利用模拟退火概率优化技术，对10个训练序列进行了GAGrank参数优化。然后，我们在三个验证序列上验证了GAGrank的性能。我们还证明了GAGrank的能力，序列异构体混合物使用两种混合物在五个不同的比例。GAGfinder分配糖胺聚糖EDD和NETD产物离子。GAGrank从串联MS分配最可能的序列。GAGrank使用二分网络的结构对节点进行排名。GAGrank根据糖胺聚糖序列在网络中的重要性对其进行排名。我们展示了GAGrank，一种使用二分图模型对EDD或NETD串联质谱中的糖胺聚糖进行测序的算法。该过程涉及首先使用GAGfinder算法分配糖胺聚糖产物离子。第二步是使用GAGrank对可能的序列进行排名。我们表明GAGrank的能力，序列异构混合物。
The sulfated glycosaminoglycans (GAGs) are long, linear polysaccharide chains that are typically found as the glycan portion of proteoglycans. These GAGs are characterized by repeating disaccharide units with variable sulfation and acetylation patterns along the chain. GAG length and modification patterns have profound impacts on growth factor signaling mechanisms central to numerous physiological processes. Electron activated dissociation tandem mass spectrometry is a very effective technique for assigning the structures of GAG saccharides; however, manual interpretation of the resulting complex tandem mass spectra is a difficult and time-consuming process that drives the development of computational methods for accurate and efficient sequencing. We have recently published GAGfinder, the first peak picking and elemental composition assignment algorithm specifically designed for GAG tandem mass spectra. Here, we present GAGrank, a novel network-based method for determining GAG structure using information extracted from tandem mass spectra using GAGfinder. GAGrank is based on Google’s PageRank algorithm for ranking websites for search engine output. In particular, it is an implementation of BiRank, an extension of PageRank for bipartite networks. In our implementation, the two partitions comprise every possible sequence for a given GAG composition and the tandem MS fragments found using GAGfinder. Sequences are given a higher ranking if they link to many important fragments. Using the simulated annealing probabilistic optimization technique, we optimized GAGrank’s parameters on ten training sequences. We then validated GAGrank’s performance on three validation sequences. We also demonstrated GAGrank’s ability to sequence isomeric mixtures using two mixtures at five different ratios. GAGfinder assigns glycosaminoglycan EDD and NETD product ions. GAGrank assigns the most probable sequence from the tandem MS. GAGrank ranks nodes using a bipartite network’s structure. GAGrank ranks glycosaminoglycan sequences based on their importance in the network. We demonstrate GAGrank, an algorithm that uses a bipartite graph model for sequencing glycosaminoglycans from EDD or NETD tandem mass spectra. The process involves first assigning glycosaminoglycan product ions using the GAGfinder algorithm. The second step is to rank possible sequences using GAGrank. We show GAGrank’s ability to sequence isomeric mixtures.
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