BMC Bioinformatics. 2014;15 Suppl 9(Suppl 9):S11. doi: 10.1186/1471-2105-15-S9-S11. Epub 2014 Sep 10.
We introduce a novel method, called PuFFIN, that takes advantage of paired-end short reads to build genome-wide nucleosome maps with larger numbers of detected nucleosomes and higher accuracy than existing tools. In contrast to other approaches that require users to optimize several parameters according to their data (e.g., the maximum allowed nucleosome overlap or legal ranges for the fragment sizes) our algorithm can accurately determine a genome-wide set of non-overlapping nucleosomes without any user-defined parameter. This feature makes PuFFIN significantly easier to use and prevents users from choosing the "wrong" parameters and obtain sub-optimal nucleosome maps.
PuFFIN builds genome-wide nucleosome maps using a multi-scale (or multi-resolution) approach. Our algorithm relies on a set of nucleosome "landscape" functions at different resolution levels: each function represents the likelihood of each genomic location to be occupied by a nucleosome for a particular value of the smoothing parameter. After a set of candidate nucleosomes is computed for each function, PuFFIN produces a consensus set that satisfies non-overlapping constraints and maximizes the number of nucleosomes.
We report comprehensive experimental results that compares PuFFIN with recently published tools (NOrMAL, TEMPLATE FILTERING, and NucPosSimulator) on several synthetic datasets as well as real data for S. cerevisiae and P. falciparum. Experimental results show that our approach produces more accurate nucleosome maps with a higher number of non-overlapping nucleosomes than other tools.
我们引入了一种新方法,称为 PuFFIN,它利用配对末端短读取来构建全基因组核小体图谱,与现有工具相比,可检测到更多的核小体,并且具有更高的准确性。与其他需要用户根据其数据优化多个参数的方法(例如,允许的最大核小体重叠或片段大小的合法范围)不同,我们的算法可以在无需任何用户定义参数的情况下准确确定全基因组范围内的非重叠核小体。此功能使 PuFFIN 的使用变得更加容易,并且可以防止用户选择“错误”的参数并获得次优的核小体图谱。
PuFFIN 使用多尺度(或多分辨率)方法构建全基因组核小体图谱。我们的算法依赖于一组不同分辨率水平的核小体“景观”函数:每个函数代表基因组位置在特定平滑参数值下被核小体占据的可能性。为每个函数计算了一组候选核小体之后,PuFFIN 会生成一个满足非重叠约束并最大程度增加核小体数量的共识集。
我们报告了综合实验结果,该结果将 PuFFIN 与最近发布的工具(NOrMAL、TEMPLATE FILTERING 和 NucPosSimulator)在几个合成数据集以及 S. cerevisiae 和 P. falciparum 的真实数据上进行了比较。实验结果表明,我们的方法产生的核小体图谱更准确,具有更多的非重叠核小体。