DANPOS: dynamic analysis of nucleosome position and occupancy by sequencing

Abstract

Recent developments in next-generation sequencing have enabled whole-genome profiling of nucleosome organizations. Although several algorithms for inferring nucleosome position from a single experimental condition have been available, it remains a challenge to accurately define dynamic nucleosomes associated with environmental changes. Here, we report a comprehensive bioinformatics pipeline, DANPOS, explicitly designed for dynamic nucleosome analysis at single-nucleotide resolution. Using both simulated and real nucleosome data, we demonstrated that bias correction in preliminary data processing and optimal statistical testing significantly enhances the functional interpretation of dynamic nucleosomes. The single-nucleotide resolution analysis of DANPOS allows us to detect all three categories of nucleosome dynamics, such as position shift, fuzziness change, and occupancy change, using a uniform statistical framework. Pathway analysis indicates that each category is involved in distinct biological functions. We also analyzed the influence of sequencing depth and suggest that even 200-fold coverage is probably not enough to identify all the dynamic nucleosomes. Finally, based on nucleosome data from the human hematopoietic stem cells (HSCs) and mouse embryonic stem cells (ESCs), we demonstrated that DANPOS is also robust in defining functional dynamic nucleosomes, not only in promoters, but also in distal regulatory regions in the mammalian genome.

Figure 1.

Flowchart of the nucleosome dynamic analysis pipeline in DANPOS. (A) Schematic illustration of nucleosome occupancy calculation. (Purple line) DNA sequence. (Red arrows) Normal reads at both ends of a nucleosome; (gray arrows) clonal reads. (Red areas) Calculated nucleosome occupancy. (B) The alternative data normalization methods. (C) Calculation of differential signals at single nucleotide resolution. (Red and sky-blue areas) Nucleosome occupancies in samples A and B, respectively. (Blue area) Differential signal at single nucleotide resolution between samples A and B. Five statistical tests that can be used to calculate differential signal are listed on the right. (D) Differential nucleosome peaks. (Blue dashed curves) Differential signals at single nucleotide resolution. (Blue bars) Differential nucleosome peaks called based on the differential signals. (E) A cartoon to show the three categories of dynamic nucleosomes. (Red and sky-blue dashed curves) Nucleosome occupancy in samples A and B, respectively. (Red and sky-blue blocks) Baseline nucleosome peaks with differential signals and peaks displayed as in D.

Figure 2.

ROC curves indicate that the default DANPOS algorithm improves the identification of dynamic nucleosomes. Each column assesses the rate of true and false positive dynamic nucleosomes identified by different preliminary data processing methods (left), data normalization methods (middle), or statistical tests (right). Each row presents the results from simulation of nucleosome occupancy changes (top), fuzziness change (middle), and position shift (bottom). The known nucleosome changes were simulated from a nucleosome reference map (see Methods).

Figure 3.

Dynamic nucleosomes identified by DANPOS explain environmental changes. (A) Snapshots of individual dynamic nucleosome regions. Categories of dynamic nucleosomes were labeled on top of each region. Nucleosome occupancies in YPD, Gal, and EtOH conditions were plotted as black areas in the top three tracks, where black bars in each track represent baseline nucleosome peaks. Nucleosome differential signals between YPD and Gal (Gal-YPD) or EtOH (EtOH-YPD) are plotted as black (positive) or gray (negative) areas in the bottom two tracks, and black bars in these tracks represent dynamic nucleosome peaks. (Black arrows) Position and direction of genes. Red and sky-blue dashed lines in the position shift plot indicate the dyads of nucleosomes in the YPD and EtOH conditions, respectively. (B) The percentages of dynamic nucleosomes in intragenic, promoter, and intergenic regions. Promoter is defined as the −350 to +50-bp regions flanking each TSS. Intragenic region is defined as the region from 50 bp downstream from the TSS to the 3′ end of the gene, and all remaining regions are defined as intergenic. (C) Enrichment of biological processes in genes associated with dynamic nucleosomes on promoters. (D) Counts of genes associated with dynamic nucleosomes on promoters.

Figure 4.

DANPOS classifies dynamic nucleosomes as position shifts, fuzziness changes, and occupancy changes. (A) Heatmaps (left and middle) and average profile plot (right) to show nucleosome occupancy in regions containing EtOH-YPD dynamic nucleosomes. Each line in the heatmaps represents a 400-bp region flanking the dyad of a dynamic nucleosome; for position shifts, the heatmap for the EtOH condition is plotted flanking the dyads defined in the YPD condition. Nucleosomes showing EtOH-YPD occupancy increase (top right), fuzziness increase (middle right), or position shift toward the 5′ direction on positive strand (bottom right) were pooled to plot average occupancy at each base pair flanking dyads. (B) Venn diagram for the overlap between different categories of EtOH-YPD dynamic nucleosomes. (C) The percentage of EtOH-YPD dynamic nucleosomes in intergenic, promoter, and intragenic regions, as defined in Figure 2B. (D) Enrichment of biological processes in each category of EtOH-YPD dynamic nucleosomes.

Figure 5.

The current sequencing depth is not saturated for the detection of dynamic nucleosomes. Counts or functional enrichment Q-values of genes containing dynamic nucleosomes on promoters were plotted as a function of sequencing depth. The sequencing depth is estimated by the read counts multiplied by the nucleosome unit size (200 bp) and divided by the yeast genome size (12 Mb). The biological processes “energy generation by precursor metabolites” (GO:0006091) and “hexose metabolic process” (GO:0019318) were used to estimate the function enrichment of EtOH-YPD and Gal-YPD dynamic nucleosomes, respectively.

Figure 6.

DANPOS defines functional dynamic nucleosomes in both promoter proximal and distal regions in the human genome. (A) Average nucleosome occupancy plotted as a function of distance to the nearest yeast TSS. (B) Average nucleosome occupancy plotted as a function of distance to the nearest human TSS. (C) Boxplots showing relative distance of human dynamic nucleosome to the nearest TSS. The dynamic nucleosomes during differentiation of human hematopoietic stem cells (HSCs) to CD36+ cells were divided into three groups. (D) Pie chart showing the number of human dynamic nucleosomes in each group. (E) Enrichment of function terms for each group. GREAT (Mclean et al. 2010) was used to analyze the functional significance of dynamic nucleosomes in each group.