diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data

BMC Bioinformatics

Aaron T.L. Lun²  Gordon K. Smyth¹ 

[1] Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Melbourne, Australia

[2] Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Melbourne, Australia

关键词: Differential analysis;    Genomic interaction;    Hi-C;   

Others  :  1229842
DOI  :  10.1186/s12859-015-0683-0

 received in 2015-05-11, accepted in 2015-07-22,  发布年份 2015

【 摘 要 】

Background

Chromatin conformation capture with high-throughput sequencing (Hi-C) is a technique that measures the in vivo intensity of interactions between all pairs of loci in the genome. Most conventional analyses of Hi-C data focus on the detection of statistically significant interactions. However, an alternative strategy involves identifying significant changes in the interaction intensity (i.e., differential interactions) between two or more biological conditions. This is more statistically rigorous and may provide more biologically relevant results.

Results

Here, we present the diffHic software package for the detection of differential interactions from Hi-C data. diffHic provides methods for read pair alignment and processing, counting into bin pairs, filtering out low-abundance events and normalization of trended or CNV-driven biases. It uses the statistical framework of the edgeR package to model biological variability and to test for significant differences between conditions. Several options for the visualization of results are also included. The use of diffHic is demonstrated with real Hi-C data sets. Performance against existing methods is also evaluated with simulated data.

Conclusions

On real data, diffHic is able to successfully detect interactions with significant differences in intensity between biological conditions. It also compares favourably to existing software tools on simulated data sets. These results suggest that diffHic is a viable approach for differential analyses of Hi-C data.

【 授权许可】

   
2015 Lun and Smyth.

【 图 表 】

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