通过系统分析揭示组蛋白修饰模式与转录因子结合之间的关联

转录因子对顺势调控元件的选择性结合,在哺乳动物细胞类型特异的基因表达中扮演重要的角色.这个过程受到染色质表观遗传状态的潜在调控.近期,染色质免疫共沉淀结合测序的研究提供了大量泛基因组水平的数据,阐述转录因子结合以及组蛋白修饰的位点,这为系统地研究转录因子和表观遗传标记之间的空间及调控关系提供了基础.该研究对公共数据库中的染色质免疫共沉淀结合测序数据进行整合分析,涉及5个细胞系中的85种转录因子、9种组蛋白修饰,目的是研究转录因子结合位点与组蛋白修饰模式以及基因表达在泛基因组水平上的关联.作者发现,不同转录因子与组蛋白修饰的共定位模式高度一致,并且组蛋白修饰在距离转录因子结合位点约500碱基对的位置富集.作者还发现,转录因子结合位点的占有率与活性组蛋白修饰的水平和双峰模式正相关,并且启动子区域组蛋白修饰的双峰和共定位模式和基因的高转录水平相一致.组蛋白修饰模式、转录因子结合位点的占有率与基因转录之间的关联暗示了细胞可能利用的基因表达调控机制.     

Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures

Researchers generating new genome-wide data in an exploratory sequencing study can gain biological insights by comparing their data with well-annotated data sets possessing similar genomic patterns. Data compression techniques are needed for efficient comparisons of a new genomic experiment with large repositories of publicly available profiles. Furthermore, data representations that allow comparisons of genomic signals from different platforms and across species enhance our ability to leverage these large repositories. Here, we present a signal processing approach that characterizes protein–chromatin interaction patterns at length scales of several kilobases. This allows us to efficiently compare numerous chromatin-immunoprecipitation sequencing (ChIP-seq) data sets consisting of many types of DNA-binding proteins collected from a variety of cells, conditions and organisms. Importantly, these interaction patterns broadly reflect the biological properties of the binding events. To generate these profiles, termed Arpeggio profiles, we applied harmonic deconvolution techniques to the autocorrelation profiles of the ChIP-seq signals. We used 806 publicly available ChIP-seq experiments and showed that Arpeggio profiles with similar spectral densities shared biological properties. Arpeggio profiles of ChIP-seq data sets revealed characteristics that are not easily detected by standard peak finders. They also allowed us to relate sequencing data sets from different genomes, experimental platforms and protocols. Arpeggio is freely available at http://sourceforge.net/p/arpeggio/wiki/Home/.