# Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization

Joachim Wolff, Leily Rabbani, Ralf Gilsbach, Gautier Richard, Thomas Manke, Rolf Backofen, Bjorn Grüning

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## ► To cite this version:

Joachim Wolff, Leily Rabbani, Ralf Gilsbach, Gautier Richard, Thomas Manke, et al. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization. Nucleic Acids Research, 2020, 48 (W1), pp.W177-W184. 10.1093/nar/gkaa220. hal-03141913

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# Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization

Joachim Wolff<sup>1,*</sup>, Leily Rabbani<sup>2</sup>, Ralf Gilsbach<sup>3,4,5</sup>, Gautier Richard<sup>6</sup>, Thomas Manke<sup>1,2</sup>, Rolf Backofen<sup>1,7</sup> and Björn A. Grüning<sup>1</sup>

<sup>1</sup>Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany, <sup>2</sup>Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, 79108 Freiburg im Breisgau, Germany, <sup>3</sup>Institute for Cardiovascular Physiology, Goethe University, Frankfurt am Main, Germany, <sup>4</sup>German Center of Cardiovascular Research (DZHK), Partner site RheinMain, Frankfurt am Main, Germany, <sup>5</sup>Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Germany, <sup>6</sup>INRAE, Agrocampus Ouest, Université de Rennes, IGEPP, F-35650 Le Rheu, France and <sup>7</sup>Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schänzlestr. 18, 79104 Freiburg, Germany

Received February 04, 2020; Revised March 11, 2020; Editorial Decision March 20, 2020; Accepted March 24, 2020

## ABSTRACT

The Galaxy HiCExplorer provides a web service at https://hicexplorer.usegalaxy.eu. It enables the integrative analysis of chromosome conformation by providing tools and computational resources to pre-process, analyse and visualize Hi-C, Capture Hi-C (cHi-C) and single-cell Hi-C (scHi-C) data. Since the last publication, Galaxy HiCExplorer has been expanded considerably with new tools to facilitate the analysis of cHi-C and to provide an in-depth analysis of Hi-C data. Moreover, it supports the analysis of scHi-C data by offering a broad range of tools. With the help of the standard graphical user interface of Galaxy, presented workflows, extensive documentation and tutorials, novices as well as Hi-C experts are supported in their Hi-C data analysis with Galaxy HiCExplorer.

## INTRODUCTION

Chromosome conformation capture (3C) (1) and its successors 4C (2,3), 5C (4) and Hi-C (5) have developed into the standard technologies used in studying the 3D conformation of chromatin. They can provide insights into the processes involved in chromatin folding and gene regulation. Hi-C technology is a well established method to study genome wide interaction of data and can detect large-scale chromosome structures, such as active and inactive (A/B) compartments (5,6), topological associated domains (TADs) (7,8), chromatin loop structures (9) or ratios of short to long range interaction counts. Although Hi-C is

a powerful approach for studying the 3D structure of chromatin globally, it is limited in its ability to investigate location specific interactions, such as promoter-enhancer interactions, due to the need for high coverage and sequencing costs. Moreover, Hi-C is unable to capture protein-DNA interactions in the chromatin conformation context. To overcome these shortcomings, capture Hi-C (cHi-C) techniques have been developed. These assays are generating data, which are enriched for the predefined targets, such as promoter regions (Promoter cHi-C) (10), proteins or protein modifications (HiChIP) (11); HiChIP is able to capture chimeric protein-DNA interactions, including transcription factors or histone modifications. The location specific enrichment provides a significantly better signal-to-noise ratio and can therefore be used for a more location sensitive analysis. Capture Hi-C data cannot be analysed with established Hi-C algorithms and need their own tools. With the rise of single-cell sequencing technologies, the single-cell Hi-C (scHi-C) approach has been developed to allow for a deeper insight into the chromatin conformation dynamics between cell types, for instance during the cell cycle (12). For a review on the abilities and current developments of Hi-C and related techniques, the reviews of McCord et al. (13), Kempfer and Pombo (14) or Bonev and Cavalli (15) are recommended. The scHi-C analyses are much more resource intensive than Hi-C analyses and need specialized algorithms for dimension reduction. Galaxy HiCExplorer meets these requirements by providing efficient and easy to use tools for the analysis of Hi-C, cHi-C and scHi-C through a comprehensive and unified web server accessible at https://hicexplorer.usegalaxy.eu. It provides computational capabilities for even the most demanding analyses. Additionally, Galaxy HiCExplorer is easy to deploy locally

<footnote>*To whom correspondence should be addressed. Tel: +49 761 2037460; Email: wolffj@informatik.uni-freiburg.de</footnote>
thanks to the installer for a local Galaxy instance. Moreover, a command line version is provided by conda and is available via the bioconda channel (16).

## RELATED WORKS

Galaxy HiCExplorer is designed as an easy-to-use online service which is accessible through a web browser. Thus, no installation is required. By embedding it into Galaxy (17) and the https://usegalaxy.eu environment, it facilitates reproducible, shareable research as well as easily accessible data analysis. With Galaxy HiCExplorer, researchers can focus on their data analysis without facing any computational limitation or software dependency issue. To offer more flexibility, it is also possible to install Galaxy HiC-Explorer on a local Galaxy instance. Hi-C data processing and downstream analysis are supported by many tool suites, such as Juicer (18), HiCUP (19), HOMER (20), HiC-Pro (21), HiFive (22) and the recently published HiCeekR (23). Juicer, HiC-Pro and HiCeekR offer several tools but are limited to a local installation. HiFive offers a Galaxy integration, but lacks the support of external data formats like cool file format (24). HiCUP and HOMER support only certain parts of Hi-C data analysis. Among the above tools, HiC-Pro is the only one with the ability to analyse cHiC and HiChIP data. scHiCNorm (25) and scHiCluster (26) provide support for single-cell Hi-C data normalization and clustering, but suffer from the lack of a tool suite to guide researchers through the workflow of processing single-cell Hi-C data from the raw FASTQ files to the clustering of cells, including methods for building interaction matrices, quality control, dimension reduction and visualization. scHiC-Norm and scHiCluster use text files to store the scHi-C interaction matrices, which are particularly space consuming, not easily shareable and prone to error accumulation. Galaxy HiCExplorer addresses all these shortcomings by providing a tool suite to support the analysis of Hi-C, captured Hi-C (e.g. Promoter cHi-C, HiChIP) and single-cell Hi-C data from the raw input data to publication ready results, as shown in Figure 2. Most importantly, none of the mentioned tools provide large computational resources to support Hi-C, cHi-C and single-cell Hi-C data analysis.

## GALAXY HICEXPLORER

Galaxy HiCExplorer offers a large collection of tools to pre-process, analyse and visualize Hi-C, cHi-C and scHi-C data. In addition to its assay-specific modules, users can benefit from the external pre-processing software for quality control of raw data and mappers such as BWA-MEM or Bowtie2 which are provided on the https://hicexplorer.usegalaxy.eu web server as well as the computational resources available. Moreover, for interactive Hi-C matrix exploration we have recently integrated HiGlass (27) into Galaxy. In the following, we briefly describe the new modules which have been added since our original publications on HiCExplorer 1 (28) and 2 (29).

### HiCExplorer

HiCExplorer provides a variety of tools for a complete Hi-C data analysis, starting with tools to control the quality of

data to create, adjust, normalize and correct interaction matrices. Furthermore, it provides tools for downstream analysis of Hi-C data such as identification of A/B compartments, TADs, loops or the computation of short versus long range contact ratios per chromosome. Finally, HiCExplorer has many options available for data visualisation such as plotting the interaction matrices, visualization of the detected TADs with pyGenomeTracks or creating aggregated contacts images. The workflow of Hi-C data analysis with Galaxy HiCExplorer is shown in Figure 1A. MultiQC, as shown in Figure 1A, supports HiCExplorer. If the structure of the quality report is changed, an update for MultiQC is necessary and the non-updated MultiQC might not work with the most recent quality report version.

### Pre-processing

**hicQuickQC.** The creation of Hi-C interaction matrices, as well as the investigation of the quality of the data afterwards, may require a long processing time and is also resource intensive. To get a swift insight into the quality of Hi-C data, hicQuickQC has been introduced. It computes a quick summary of the Hi-C data quality using only a small subset of reads. The computation time to create the quality report with hicQuickQC for the first 1 million reads takes <3 min. The quality report is equal to the quality report of hicBuildMatrix and the only difference is that it is based only on the first 1 million reads instead of the full dataset.

**hicFindRestSites.** Hi-C interaction matrices with fixed size bins are not always the best representation of the data. In fact, with a sufficient sequencing depth, bins of a restriction fragment size are a better alternative. To generate such matrices, this tool generates a list of restriction sites for user-defined enzymes. This list can be used as an input to hicBuildMatrix to create restriction site resolution Hi-C matrices.

**hicConvertFormat.** Support for external interaction matrix data formats is missing in most Hi-C data analysis software. This makes it difficult to compare matrices which have been built with different software and to directly use them for further analysis. Instead, the matrices need to be built from scratch, which is time consuming and potentially error prone. This tool supports loading matrices of cool, HiCExplorers h5, Juicers hic, Homer and HiCPro format and can convert them to cool, h5, Homer and ginteractions (30) format.

**hicNormalize.** Normalization is a crucial step to be able to compare the interaction matrices obtained with a different sequencing depth. For this purpose, hicNormalize supports three normalization methods: (a) to the depth of the matrix with the least read coverage, (b) to the value range of 0 to 1 and (c) to a user defined scaling factor. For details on the normalization methods consult our Supplementary materials.

**hicCorrectMatrix.** Correcting the Hi-C interaction matrices is a necessary step to remove technical biases. In addition to the iterative correction (ICE) algorithm from
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Analysis workflow for Hi-C (A), cHi-C (B) and scHi-C (C). All the workflows use the *hicBuildMatrix* to create the individual contact matrices. Hi-C and cHi-C supports HiCExplorer's h5 and cool interaction matrix file format; however, scHi-C pipeline creates one cool file per cell. These files can then be merged into a single multi-cool (scool) matrix with *scHiMergeToSCool*.</figcaption></figure>

Imakaev (31), HiCExplorer also offers the Knight-Ruiz correction (32), first used for Hi-C matrices by (9). The method is more memory efficient, is faster than the ICE algorithm and better suited for the analysis of high-resolution and deep read coverage interaction matrices.

### Analysis.

**hicDetectLoops.** Chromatin loops are long range chromatin interactions and present in Hi-C matrices as enriched regions in comparison to their local neighborhood. Depending on the read coverage and the resolution of the Hi-C interaction matrix, it is for instance possible to detect enhancer-promoter interactions. Due to its sensitivity to the read coverage it is recommended to run the loop detection on different resolutions and to merge them afterwards, using *hicMergeLoops*, into one loop file. By merging, overlapping loops are pooled into one loop. In addition, the tool *hicValidateLocations* can be used to confirm that the detected loops are correlated with detected locations of a protein of interest. For example, CTCF is known as a loop binding factor in mammals (7,9) and should therefore be present at many loop locations. Finally, the detected loops can be visualised with *hicPlotMatrix*, see Figure 2A. For details regarding the algorithm and benchmarks, consider (33).

**hicCompartmentalization.** This tool supports the analysis of interactions at the level of (active and inactive) compartments. These two large chromosomal domains can be defined through a principal component analysis (5) and are provided in Galaxy HiCExplorer by the existing *hicPCA* module. To visualize the difference in the interaction frequencies within and between the different compartments, a polarization plot can be generated using a method which was first introduced by (6). See Figure 2D.

**hicAverageRegions.** The comparison of specific regions between different samples can pose a challenge. One typical

use case could be the comparison between multiple detected TADs on a wild type and a treatment sample. This tool extracts Hi-C submatrices corresponding to the upstream and downstream regions of reference anchors (e.g. a subset of TAD boundaries, promoter regions or any predefined positions of interest). It computes the average contacts of these submatrices and uses them to detect the potential differences of contact patterns located around these anchors, see Supplementary materials. The average of collected submatrices can be visualized with *hicPlotAverageRegions*, as shown in Figure 2C.

**hicPlotSVL.** Comparing the ratio of short range interaction to long range interaction between Hi-C matrices obtained in various experimental conditions can guide the understanding of chromatin topology and its folding principles. To this end, this tool computes the ratio per chromosome and plots it per sample as a boxplot, as shown in Figure 2B. For the mathematical details, please consult our Supplementary material.

**pyGenomeTracks.** The visualization tool *hicPlotTADs* which, was mentioned in the previous publication (29), came to the attention of many of our users. However, there was always some confusion as to whether or not it is for Hi-C data only which was never the case. To solve this, *hic-PlotTADs* was renamed to *pyGenomeTracks* and is independently developed.

### Capture Hi-C

The cHi-C modules of HiCExplorer are designed for analysing Promoter cHi-C and HiChIP. HiCExplorer will also accept data from other Capture Hi-C methods, including ChiA-PET (34). if dedicated preprocessing steps were performed to obtain compatible mapping data. Furthermore, it can be used to generate virtual 4C plots from Hi-C data. As for Hi-C data, cHi-C interaction matrices are
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (A) Detected loops on *GM12878* *primary* data from (9), computed by *hicDetectLoops* and visualised by *hicPlotMatrix*. (B) Short to long range contact interaction ratios created by *hicPlotSVL* on *GM12878* *primary*, *IMR90* and *HMEC* data from (9). (C) Average regions of detected TADs from *hicFindTADs* on *GM12878* *primary*, chromosome 1; data from (9). (D) The level of compartments separation on *GM12878* *primary* data from (9), computed by *hicCompartmentalization*. (E) Quality control plot for *FL-E13-5* and *MB-E10-5* showing the sparsity distribution, data from (42). (F) Quality control plot for single-cell Hi-C data by (36). It shows the read coverage per cell, cells with <100 000 reads are discarded. (G) Consensus matrix plot for single-cell Hi-C data on 1 Mb resolution. Cells are dimension reduced by computing A/B compartments per cell and clustered with k-means. The consensus matrix of a cluster is the average of all interaction matrices of the cluster members. Data from (36). (H) Single-cell Hi-C cluster profile, created after dimension reduction by *schicClusterMinHash* and spectral clustering on 1 Mb single-cell Hi-C data from (36). (I) Viewpoint of the gene *MSTN* on *FL-E13-5* and *MB-E10-5* with mean background and p-values per relative distance via continuous negative binomial distributions, data from (42).</figcaption></figure>

built with *hicBuildMatrix*. The regions of interest in these protocols, such as the location of the promoters in cHi-C or the binding sites of the target protein for HiChIP, are referred to as *reference points*. In the case of HiChIP, reference points are either annotated with peak calling tools, such as MACS2 (35) using either the HiChIP mapping file or ChIP-seq data, or regions (e.g. promoters) are manually selected. The region defined up- and downstream of a reference point is referred to as a *viewpoint*. Figure 2I illustrates all the up- and downstream distances within a viewpoint by their *relative distance* to a specific reference point. A background model is created which takes interactions per relative distance from all viewpoints into account. It is in the downstream analysis used to detect higher interactions as expected for a relative distance. These interactions are potentially different between a treatment and a control sample and therefore can be used for a differential test. The cHi-C workflow of Galaxy HiCExplorer is shown in Figure 1B. Please consult our Supplementary material concerning details of the presented cHi-C methods.

### Pre-processing.

**chicQualityControl.** This module is designed to investigate the quality of every single viewpoint, taking the sparsity of the interaction counts into account. A viewpoint will be removed if the sparsity of the data at this viewpoint is lower than a given threshold. To help users in setting an appropriate threshold, the tool generates several quality plots from which one is presented in Figure 2E.

**chicViewpointBackground.** The background model per relative distance is computed by taking all interaction

counts of a relative distance over all viewpoints and samples into account. Based on this model, interactions with higher counts than an expected count will be identified during the downstream analysis.

### Analysis.

**chicViewpoint.** This tool extracts the interaction counts of each viewpoint from the interaction matrix, associates additional information and writes the viewpoint data to a file. Based on the background model, a *P*-value for each interaction count is computed. The *P*-value is an indicator if a specific count at a relative distance is in an expected range or higher.

**chicSignificantInteractions.** Using the *P*-values of a viewpoint, this tool decides via a threshold if an interaction at a relative distance is significant.

**chicAggregateStatistic.** The differential testing investigates if solitary interactions of two viewpoints have a different interaction frequency. These solitary interactions are either provided by a predefined target file or detected with *chicSignificantInteractions*. This tool aggregates the provided interactions from two viewpoints and prepares them as input for *chicDifferentialTest*.

**chicDifferentialTest.** The differential testing examines one solitary interaction between two viewpoints, under consideration of the interaction frequency at the reference points. As a differential test either chi²-test or Fisher's test can be used under the null hypothesis that the interaction frequency is equal.
Visualization.

**chicPlot Viewpoint.** To visualize one or several viewpoints, *chicPlot Viewpoint* has been introduced with the possibility of adding a mean background signal and highlighting the significant or differential interactions. Moreover, the computed p-values can be added as an additional heatmap as seen as in Figure 2I.

Single-cell Hi-C

Single-cell Hi-C explores how chromatin is being folded and which elements contribute to its regulation on a single-cell scale. While analyzing Hi-C data is computationally expensive, this can increase drastically for scHi-C data. The reason for this is the increase in the number of Hi-C interaction matrices that need to be analysed from one to several thousand, with a corresponding increase in runtime and memory. The read coverage of scHi-C data is currently not high (36) and 1 megabase (Mb) resolution matrices are used to avoid generating highly sparse matrices. However, as sequencing costs decline, resolutions of 10 kb may be achievable and the demand for dimension reduction techniques, such as those presented here, will be indispensable. With scHiCExplorer, a software suite is provided to process single-cell Hi-C data offering tools for demultiplexing, matrix handling, correction, dimension reduction, clustering and visualisation. Figure 1C shows the workflow of single-cell Hi-C data analysis with Galaxy HiCExplorer. scHiCExplorer can be used for general processing of single-cell Hi-C data as long as the forward and reverse strand for each cell are provided as a BAM/SAM file. All pre-processing steps like adapter and/or barcode trimming, demultiplexing and mapping needs to be applied by third-party tools.

Pre-processing.

**scHicDemultiplex.** Raw FASTQ data from a single-cell experiment usually contains reads from multiple cells which are encoded with different barcodes. This tool supports demultiplexing of an interleaved FASTQ file into one FASTQ file per cell. The demultiplexing is implemented to support the method which has been introduced in Nagano (36) for barcoding. Due to the lack of a standard method on how to encode barcodes, presently, demultiplexing is limited to FASTQ files with the same barcoding method as in (36). Other demultiplexing tools are part of the general Galaxy tool suite.

**scHicMergeToSCool.** Every single-cell interaction matrix can be created with *hicBuildMatrix*. *scHicMergeToSCool* can merge individual matrices into a joint matrix in multi-cool format (24), which will be used in all subsequent downstream analysis and visualization tools. While using the API of cooler, the data is not stored with multiple resolutions as it is defined by (24). The cool file is used as a container format for the individual cool files of the Hi-C matrices. For this reason, the format is referred to as *scool*.

**scHicQualityControl.** Since scHi-C data is a very sparse, not all matrices have sufficient read coverage to be considered for the downstream analysis. Thus, the quality control

module removes interaction matrices of cells with total read counts below a user-specified threshold (see Figure 2E) or very sparse interaction matrices.

**scHicCreateBulkMatrix.** This tool supports to pool all matrices stored in the *scool* file to one single Hi-C interaction matrix and enables the analysis like in regular Hi-C.

Several modules of HiCExplorer are also required in single-cell Hi-C data analysis. To provide an equal functionality at the single cell level and to support the *scool* file format, scHiCExplorer reuses these modules from HiCExplorer. These are *scHicNormalize*, *scHicCorrectMatrices*, *scHicAdjustMatrix*, *scHicMergeMatrixBins* and *scHicInfo*. scHiCExplorer adds the functionality of handling the multiple matrices stored in the *scool* file and distributes the computations over several threads.

**Dimension and clustering reduction.** Clustering cells is a common approach to study the difference between them and to learn about their relations from single cell data. scHiCExplorer provides the *k*-means and *spectral* clustering methods. *K*-means was used on scHi-C data by (36) or (26), but the choice of a clustering algorithm is always dependent on the data. For this reason, scHiCExplorer provides additional the spectral clustering and will continue adding standard clustering algorithms in the future. However, reducing the dimensions of the underlying matrices is necessary to be able to cluster cells in a reasonable amount of time and to decrease the memory footprint; as shown in Supplementary Table S1. The usage of dimension reduction is also often necessary to achieve good results (37–39). The results in the Supplementary Figures S1–S4 confirm this. The need to reduce the dimensions becomes obvious when matrices of higher resolutions are used. The combined raw data matrix for a scHi-C dataset has a dimensionality of *cells*\**features*, where *features* = *bins*\**bins* for one matrix. As an example, mapping of the Nagano 2017 (36) data on the mouse mm9 genome and using it to make a 1 Mb resolution matrix, will already return a matrix of 2500\*7.3 million dimensions; this number will increase to 2500\*7.3 billion dimensions if the resolution of the matrix increases to 10 kb.

**scHicCluster.** A principal component analysis (reducing to *samples*\**bins*) or a *k*-nearest neighbors matrix (reducing to *samples*\**samples*) can be chosen as the desired method to reduce the dimensions of data. However, a clustering of the raw data without applying any dimension reduction is also supported.

**scHicClusterMinHash.** Clustering and dimension reduction techniques of *scHicCluster* usually work with low resolutions like 1 Mb but require a large amount of memory (>1 TB) on matrices of higher resolutions such as 10 kb. MinHash (40) is an approximate nearest neighbors method which computes the *k*-nearest neighbors matrix via local sensitive hash functions and reduces the number of dimensions to *samples*\**samples*. MinHash’s approximate computation of the *k*-nearest neighbors makes it possible to process 10 kb resolution scHi-C data. Our implementation runs for just over one hour and needs 53GB of memory, for more details consider (41).
im Breisgau, Germany and Simon Rapple for proof reading the manuscript.

## FUNDING

German Science Foundation [CRC992 'Medical Epigenetics' to L.R., T.M., R.B., B.G.; GI 747/2-1, GI 747/3-1 to R.G.]; German Federal Ministry of Education and Research [031 A538A de.NBI-RBC awarded to R.B.]; German Federal Ministry of Education and Research [031 L0101C de.NBI-epi awarded to B.G.]; R.B. was supported by the German Research Foundation (DFG) under Germany's Excellence Strategy [CIBSS-EXC-2189-Project ID 390939984]. Funding for open access charge: German Federal Ministry of Education and Research.
*Conflict of interest statement.* None declared.

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