HiC-Pro is an optimized and flexible pipeline for processing Hi-C data from raw reads to normalized contact maps. HiC-Pro maps reads, detects valid ligation products, performs quality controls and generates intra- and inter-chromosomal contact maps. It includes a fast implementation of the iterative correction method and is based on a memory-efficient data format for Hi-C contact maps. In addition, HiC-Pro can use phased genotype data to build allele-specific contact maps. We applied HiC-Pro to different Hi-C datasets, demonstrating its ability to easily process large data in a reasonable time. Source code and documentation are available at -Pro.
As with any genome-wide sequencing data, Hi-C usually requires several millions to billions of paired-end sequencing reads, depending on genome size and on the desired resolution. Managing these data thus requires optimized bioinformatics workflows able to extract the contact frequencies in reasonable computational time and with reasonable resource and storage requirements. The overall strategy to process Hi-C data is converging among recent studies [9], but there remains a lack of stable, flexible and efficient bioinformatics workflows to process such data. Solutions such as the HOMER [10], HICUP [11], HiC-inspector [12], HiCdat [13] and HiCbox [14] pipelines are already available for Hi-C data processing. HOMER offers several functions to analyze Hi-C data but does not perform the mapping of reads nor the correction of systematic biases. HiCdat, HiC-inspector and HiCbox do not allow chimeric reads to be rescued during the mapping of reads. HICUP provides a complete pipeline until the detection of valid interaction products. Using HICUP together with the SNPsplit program [15] allows the extraction of allele-specific interaction products whereas all other solutions do not allow allele-specific analysis. The HiCdat and HiCbox packages offer a means of correcting contact maps for systematic biases. Finally, none of these software were designed to process very large amounts of data in a parallel mode. The hiclib package is currently the most commonly used solution for Hi-C data processing. However, hiclib is a Python library that requires programming skills, such as knowledge of Python and advanced Linux command line, and cannot be used in a single command-line manner. In addition, parallelization is not straightforward and it has limitations with regard to the analysis and normalization of very high-resolution data (Table 1).
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Here, we present HiC-Pro, an easy-to-use and complete pipeline to process Hi-C data from raw sequencing reads to normalized contact maps. HiC-Pro allows the processing of data from Hi-C protocols based on restriction enzyme or nuclease digestion such as DNase Hi-C [4] or Micro-C [16]. When phased genotypes are available, HiC-Pro is able to distinguish allele-specific interactions and to build both maternal and paternal contact maps. It is optimized and offers a parallel mode for very high-resolution data as well as a fast implementation of the iterative correction method [17].
In order to compare our results with the hiclib library, we ran HiC-Pro on the same dataset, and without initial read splitting, using eight CPUs. HiC-Pro performed the complete analysis in less than 15 hours compared with 28 hours for the hiclib pipeline. The main difference in speed is explained by our two-step mapping strategy compared with the iterative mapping strategy of hiclib, which aligned the 35 base pair (bp) reads in four steps. Optimization of the binning process and implementation of the normalization algorithm led to a three-fold decrease in time to generate and normalize the genome-wide contact map.
Comparison of HiC-Pro and hiclib processing. a Both pipelines generate concordant results across processing steps. The fraction of uniquely aligned read pairs is calculated on the total number of initial reads. Self-circle and dangling-end fractions are calculated on the total number of aligned read pairs. Intra- and inter-chromosomal contacts are calculated as a fraction of filtered valid interactions. b Boxplots of the Spearman correlation coefficients of intra- and inter-chromosomal maps generated at different resolutions by both pipelines. c Chromosome 6 contact maps generated by hiclib (top) and HiC-Pro (bottom) at different resolutions. The chromatin interaction data generated by the two pipelines are highly similar
In theory, the raw contact counts are expected to be proportional to the true contact frequency between two loci. As for any sequencing experiment, however, it is known that Hi-C data contain different biases mainly due to GC content, mappability and effective fragment length [18, 19]. An appropriate normalization method is therefore mandatory to correct for these biases. Over the last few years, several methods have been proposed using either an explicit-factor model for bias correction [19] or implicit matrix balancing algorithm [17, 27]. Among the matrix balancing algorithm, the iterative correction of biases based on the Sinkhorn-Knopp algorithm has been widely used by recent studies due to its conceptual simplicity, parameter-free nature and ability to correct for unknown biases, although its assumption of equal visibility across all loci may require further exploration. In theory, a genome-wide interaction matrix is of size O(N2), where N is the number of genomic bins. Therefore, applying a balancing algorithm on such a matrix can be difficult in practice, as it requires a significant amount of memory and computational time. The degree of sparsity of the Hi-C data is dependent on the bin size and on the sequencing depth of coverage. Even for extremely large sequencing coverage, the interaction frequency between intra-chromosomal loci is expected to decrease as the genomic distance between them increases. High-resolution data are therefore usually associated with a high level of sparsity. Exploiting matrix sparsity in the implementation can improve the performance of the balancing algorithm for high-resolution data. HiC-Pro proposes a fast sparse-based implementation of the iterative correction method [17], allowing normalization of genome-wide high-resolution contact matrices in a short time and with reasonable memory requirements.
As the Hi-C technique is maturing, it is now important to develop bioinformatics solutions which can be shared and used for any project. HiC-Pro is a flexible and efficient pipeline for Hi-C data processing. It is freely available under the BSD licence as a collaborative project at -Pro. It is optimized to address the challenge of processing high-resolution data and provides an efficient format for contact map sharing. In addition, for ease of use, HiC-Pro performs quality controls and can process Hi-C data from the raw sequencing reads to the normalized and ready-to-use genome-wide contact maps. HiC-Pro can process data generated from protocols based on restriction enzyme or nuclease digestion. The intra- and inter-chromosomal contact maps generated by HiC-Pro are highly similar to the ones generated by the hiclib package. In addition, when phased genotyping data are available, HiC-Pro allows the easy generation of allele-specific maps for homologous chromosomes. Finally, HiC-Pro includes an optimized version of the iterative correction algorithm, which substantially speeds up and facilitates the normalization of Hi-C data. The code is also available as a standalone package ( ).
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