【三维基因组】Hi-C call loops？-choose juicer ! 之 step2

【Juicer loop】HiCCUPS

企业微信截图_c04d145e-7d8f-4626-ae7c-109264a23a2d.png

HiCCUPS call loop的算法如上。

用法如下：
hiccups [-m matrixSize] [-c chromosome(s)] [-r resolution(s)]
[-k normalization (NONE/VC/VC_SQRT/KR)] [-f fdr]
[-p peak width] [-i window] [-t thresholds]
[-d centroid distances] [specified_loop_list]
重要参数说明：
-r ：指定call loop 的resolution，多个分辨率以 , 分割 如5000,10000 注意最高分辨率为5000,默认5000,10000, 25000
HiC file ：为输入文件即.hic files
outputDirectory：输出结果目录
-f ：fdr 阈值 多个阈值要用逗号分割如：“- f 0.1,0.15" fdr默认为 10%
简单示例:
java -jar juicer_tools.1.7.5_linux_x64_jcuda.0.8.jar hiccups GM.chr10.hic GM/chr10/
集群实例:

java -Xmx25000m -Xms8000m -jar  juicer_tools.1.7.5_linux_x64_jcuda.0.8.jar  hiccups -r 5000  sam1.chr10.hic   /sam1/chr10

输出结果目录：

• enriched_pixels_5000
• fdr_thresholds_5000
• merged_loops
• postprocessed_pixels_5000

重要结果文件说明:
merged loop
merged loop文件共有20列，如下所示

chr1  x1  x2  chr2  y1  y2  color observed  expectedBL  expectedDonut expectedH expectedV fdrBL fdr Donut  fdrH  fdrV  numCollapsed  centroid1     centroid2 radius

详细说明如下：

• chromosome = the chromosome that the loop is located on
• x1,x2 = the coordinates of the upstream locus corresponding to the peak pixel
• y1,y2 = the coordinates of the downstream locus corresponding to the peak pixel
• color = the color that the feature will be rendered as if loaded in Juicebox
• observed = the raw observed counts at the peak pixel
• expected_[bottom_left, donut, horizontal, vertical] = the expected counts calculated using the [bottom_left, donut, horizontal, vertical] filter
• fdr_[bottom_left, donut, horizontal, vertical] = the q-value of the loop calculated using the [bottom_left, donut, horizontal, vertical] filter
• number_collapsed = the number of pixels that were clustered together as part of the loop call
• centroid1 = the upstream coordinate of the centroid of the cluster of pixels corresponding to the loop
• centroid2 = the downstream coordinate of the centroid of the cluster of pixels corresponding to the loop
• radius = the Euclidean distance from the centroid of the cluster of pixels to the farthest pixel in the cluster of pixels