Result files

Quality control

hicBuildMatrix was used to generate the matrices for the Hi-C data, doing some quality control in the process. The quality control results are available as a html file in each folder.

To preview the html files, prepend the following to the URL of the hicQC.html in each folder: https://html-preview.github.io/?url=

Or click the links below:

Preview sample SRR6502335 QC results

Name	Count	Percent
Sequenced reads	244,003,806	100
Mappable, highQ+uniq	164,715,495	67.5
Hi-C contacts	110,459,727	45.27
Inter chromosomal	28,350,411	25.67
Intra short	14,384,188	13.02
Intra long	67,725,128	61.31

Preview sample SRR6502336 QC results

Name	Count	Percent
Sequenced reads	217,066,567	100
Mappable, highQ+uniq	147,553,720	68
Hi-C contacts	94,317,987	43.45
Inter chromosomal	24,058,490	25.51
Intra short	12,514,652	13.27
Intra long	57,744,845	61.22

Preview sample SRR6502337 QC results

Name	Count	Percent
Sequenced reads	175,897,321	100
Mappable, highQ+uniq	117,483,977	66.79
Hi-C contacts	79,573,279	45.24
Inter chromosomal	22,500,348	28.28
Intra short	9,800,706	12.32
Intra long	47,272,225	59.41

Preview sample SRR6502338 QC results

Name	Count	Percent
Sequenced reads	174,596,497	100
Mappable, highQ+uniq	119,036,178	68.18
Hi-C contacts	84,296,486	48.28
Inter chromosomal	23,802,080	28.24
Intra short	10,189,916	12.09
Intra long	50,304,490	59.68

Preview sample SRR6502339 QC results

Name	Count	Percent
Sequenced reads	96,286,472	100
Mappable, highQ+uniq	65,355,035	67.88
Hi-C contacts	41,072,908	42.66
Inter chromosomal	11,586,845	28.21
Intra short	5,180,947	12.61
Intra long	24,305,116	59.18

Guidelines from HiCExplorer:

The hicBuildMatrix command generates a bam file and a matrix file for the Hi-C data. The bam file contains only the valid Hi-C read pairs, which can be used to assess the quality of the Hi-C library on the genome browser. A good Hi-C library should have an abundance of reads near the restriction fragment sites.

In the QC folder, an html file is saved with plots that provide useful information for the quality control of the Hi-C sample. This includes metrics such as the number of valid pairs, duplicated pairs, and self-ligations. Typically, only 25%-40% of the reads are considered valid and used to build the Hi-C matrix, as reads on repetitive regions are discarded.

One important quality control measurement is the interchromosomal fraction of reads, which indirectly measures random Hi-C contacts. A good Hi-C library should have less than 10% interchromosomal contacts. The hicQC module can be used to compare the quality control measures across different samples.