SOFTWARE Open Access

HiFive: a tool suite for easy and efficientHiC and 5C data analysisMichael EG Sauria1, Jennifer E. Phillips-Cremins2, Victor G. Corces3 and James Taylor1*

Abstract

The chromatin interaction assays 5C and HiC have advanced our understanding of genomic spatial organization,but analysis approaches for these data are limited by usability and flexibility. The HiFive tool suite provides efficientdata handling and a variety of normalization approaches for easy, fast analysis and method comparison. Integrationof MPI-based parallelization allows scalability and rapid processing time. In addition to single-command analysis ofan entire experiment from mapped reads to interaction values, HiFive has been integrated into the open-source,web-based platform Galaxy to connect users with computational resources and a graphical interface. HiFive isopen-source software available from http://taylorlab.org/software/hifive/.

Keywords: Chromatin conformation, Normalization, Software, Spatial organization, Scalable, Galaxy

BackgroundIn the more than a decade since the vast majority of thehuman genome was first sequenced, it has become clearthat sequence alone is insufficient to explain the complexgene and RNA regulatory patterns seen over time andacross cell types in eukaryotes. The context of specific se-quences – whether from combinations of DNA-bindingtranscription factors (TFs) [1–3], methylation of the DNAitself [4, 5], or local histone modifications [4, 6] – is inte-gral to how the cell utilizes each sequence element. Al-though we have known about the potential roles thatsequentially distant but spatially proximal sequences andtheir binding and epigenetic contexts play in regulatingexpression and function, it has only been over the pastdecade that new sequencing-based techniques have en-abled high-throughput analysis of higher-order structuresof chromatin and investigation into how these structuresinteract among themselves and with other genomic ele-ments to influence cellular function.Several different sequencing methods for assessing

chromatin interactions have been devised, all based onpreferentially ligating spatially close DNA sequence frag-ments. These approaches include ChIA-Pet [7], tetheredchromosome capture [8], and the chromatin conformation

capture technologies of 3C, 4C, 5C, and HiC [9–12](Additional file 1: Figure S1). While these assays haveallowed a rapid expansion of our understanding of thenature of genome structure, they also have presentedsome formidable challenges.In both HiC and 5C, systematic biases resulting from

the nature of the assays have been observed [13, 14],resulting in differential representation of sequences inthe resulting datasets. While analyses at a larger scaleare not dramatically affected by these biases due to thelarge number of data points being averaged over,higher-resolution approaches must first address thesechallenges. This is becoming more important as theresolution of experiments is increasing [15]. Severalanalysis methods have been described in the literatureand applied to correcting biases in HiC [14–21] and 5Cdata [22–24]. There is still room for improving ourability to remove this systematic noise from the dataand resolve finer-scale features and, perhaps more im-portantly, for improving the usability and reproducibil-ity of normalization methodologies.A second challenge posed by data from these types of

assays is one of resources. Unlike other next-generationsequencing assays where even single-base resolution islimited to a few billion data points, these assays assesspairwise combinations, potentially increasing the size ofthe dataset by several orders of magnitude. For a threebillion base pair genome cut with a six-base restriction

* Correspondence: james@taylorlab.org1Departments of Biology and Computer Science, Johns Hopkins University,Baltimore, MD 21218, USAFull list of author information is available at the end of the article

© 2015 Sauria et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Sauria et al. Genome Biology (2015) 16:237 DOI 10.1186/s13059-015-0806-y

enzyme (RE), the number of potential interaction pairsis more than half a trillion (if considering both fragmentends) while a four-base RE can yield more than two anda half quadrillion interaction pairs. Even allowing thatthe vast majority of those interactions will be absentfrom the sequencing data, the amount of informationthat needs to be handled and the complexity of normal-izing these data still pose a major computational hurdle,especially for investigators without access to substantialcomputational resources.Here we describe HiFive, a suite of tools developed for

handling both HiC and 5C data using a combination ofempirically determined and probabilistic signal modeling.HiFive has performance on par or better than other avail-able methodologies while showing superior speed andefficient memory usage through parallelization and datamanagement strategies. In addition to providing a simpleinterface with no preprocessing or reformatting require-ments, HiFive offers a variety of normalization approachesincluding versions of all commonly used algorithmicapproaches allowing for straightforward optimizationand method comparison within a single application. Inaddition to its command line interface, HiFive is also avail-able through Galaxy, an open-source web-based platform,connecting users with computational resources and theability to store and share their 5C and HiC analyses. All of

these aspects of HiFive make it simple to use and fast, andmake its analyses easily reproducible.

The HiFive analysis suiteHiFive was designed with three goals: first, to provide asimple-to-use interface for flexible chromatin interactiondata analysis; second, to provide well-documented supportfor 5C analysis; and third, to improve performance overexisting methodologies while reducing analysis runtimes.These are accomplished through a stand-alone programbuilt on a Python library designed for customizable ana-lysis and supported under the web-based platform Galaxy.

User interfaceHiFive provides three methods of use: the command line;the Internet; or as a development library. The commandline interface provides users with the ability to performanalyses as a series of steps or as a single unified analysis.The only inputs that HiFive requires are a description ofthe genomic partitioning and interaction data, either dir-ectly as mapped reads or counts of reads associated withthe partitioned genome (for example, fragment pairs andtheir observed reads). HiFive handles all other formattingand data processing. In addition, HiFive has been bundledas a set of tools available through Galaxy (Fig. 1). This notonly provides support with computational resources but

Fig. 1 HiFive’s tool interface through Galaxy. HiFive tools are available through the Galaxy toolshed, providing a graphical interface and showingtool option inter-dependencies

Sauria et al. Genome Biology (2015) 16:237 Page 2 of 10

also ensures simple installation of all prerequisite librariesand packages. HiFive was also created to allow customcreation of analysis methods as a development library forchromatin interaction analysis through extensive docu-mentation and an efficient data-handling framework.

Organization of HiFiveAt its core, HiFive is a series of hierarchical data struc-tures building from general to specific information.There are four primary file types that HiFive creates, allrelying on the Numpy scientific computing Pythonpackage for efficient data arrays and fast data access.These structures include genomic partitioning, ob-served interactions, distance-dependence relationshipand normalization parameters, and heatmaps of ob-served and expected interactions. By separating theseattributes, many datasets can utilize the same genomicpartitioning and multiple analyses can be run using thesame interaction data without the need to reload orprocess information.

Data processing and filteringIn order to process 5C or HiC data, the first step aftermapping is converting the data into a form compatiblewith the desired workflow. HiFive appears to be nearlyalone in its ability to handle mapped data without add-itional processing (the only exception is HiCLib [17]).Reads can be read directly from numerous BAM-formatted files, and this may be done as an independentstep or within the integrated one-step analysis. HiCLibalso possesses the ability to input data directly frommapped read files. In all other cases, reads need to be con-verted to other formats. HiCPipe [14] provides scripts forsome but not all of these processes, while HiCNorm [16]relies on pre-binned reads. In all cases aside from HiFive,a workflow is required to move from mapped reads tonormalization.Filtering is accomplished in two phases, during the ini-

tial processing of reads and during project creation(Additional file 1: Figures S2 and S3). The first phaselimits data to acceptable paired-end combinations. For5C data, this means reads mapping to fragments probedwith opposite-orientation primers. HiC data use two cri-teria, total insert size (a user-specified parameter) andorientation/fragment relationship filtering. In the lattercase, reads originating from non-adjacent fragments orfrom adjacent fragments and in the same orientation areallowed, similar to Jin et al. [19] (Additional file 1: FigureS4). The second phase, common to both 5C and HiC data,is an iterative filtering based on numbers of interactionsper fragment or fragment end (fend). Briefly, total num-bers of interactions for each fragment are calculated, andfragments with insufficient numbers of interaction part-ners are removed along with all of their interactions. This

is repeated until all fragments interact with a sufficientnumber of other non-filtered fragments. This filtering iscrucial for any fragment or fend-specific normalizationscheme to ensure sufficient interdependency betweeninteraction subsets to avoid convergence issues.

Distance-dependence signal estimationOne feature of HiFive that is notably absent from nearly allother available analysis software is the ability to incorporatethe effects of sequential distance into the normalization.One exception to this is HiTC [21], which uses a loess re-gression to approximate the distance-dependence relation-ship of 5C data to genomic distance. This method doesnot, however, allow for any other normalization of 5C data.Another is Fit-Hi-C [25], although this software assignsconfidence estimates to mid-range contact bins rather thannormalizing entire datasets. This feature is of particular im-portance for analysis of short-range interactions such asthis in 5C data, or for making use of counts data ratherthan a binary observed/unobserved indicator. For 5C data,HiFive uses a linear regression to estimate parameters forthe relationship between the log-distance and log-counts (Additional file 1: Figure S5). HiC data require amore nuanced approximation because of the amount ofdata involved and the non-linear relationship over therange of distances queried. To achieve this, HiFive usesa linear piece-wise function to approximate the distance-dependent portion of the HiC signal, similar but distinctfrom that used by Fit-Hi-C. HiFive partitions the totalrange of interactions into equally sized log-transformeddistance bins with the exception of the smallest bin, whoseupper bound is specified by the user. Mean counts andlog-transformed distances are calculated for each bin anda line is used to connect each set of adjacent bin points(Additional file 1: Figure S6). For distances extending pastthe first and last bins, the line segment is simply extendedfrom the last pair of bins on either end. Simultaneously, asimilar distance-dependence function is constructed usinga binary indicator of observed/unobserved instead of readcounts for each fend pair. All distances are measured be-tween fragment or fend midpoints.

HiFive normalization algorithmsHiFive offers three different normalization approaches.These include a combinatorial probability model basedon HiCPipe’s algorithm called ‘Binning’, a modifiedmatrix-balancing approach called ‘Express’, and a multi-plicative probability model called ‘Probability’. In theBinning algorithm, learning is accomplished in an itera-tive fashion by maximizing each set of characteristicbin combinations independently each round using theBroyden–Fletcher–Goldfarb–Shanno algorithm for max-imum likelihood estimation.

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The Express algorithm is a generalized version ofmatrix balancing. While it can use the Knight-Ruiz al-gorithm [26] for extremely fast standard matrix balan-cing (ExpressKR), the Express algorithm also has theability to take into account differing numbers of pos-sible interactions and find corrections weighted bythese numbers of interactions. The set of valid interac-tions is defined as set A, interactions whose fends haveboth passed the above-described filtering process andwhose inter-fend distance falls within user-specifiedlimits. In addition, because counts are log-transformedfor 5C normalization, only non-zero interactions are in-cluded in set A. For each interaction c between fends orfragments i and j for HiC and 5C, respectively, in theset of valid interactions A, correction parameter fi isupdated as in (1) for HiC and (2) for 5C.

f ′i ¼ f i

ffiffiffiffiffiffiffiffiffiffiffiffiffiXj∈Ai

cijEijX

j∈Ai

1

vuuuuut ð1Þ

f i′ ¼ f i þ

Xcij∈Ai

i;j

ln cij� �

−Eij� �

Xcij∈Ai

i;j

2

ð2Þ

The expected value of each HiC interaction is simplyproduct of the exponent of the expected distance-dependent signal D(i,j) and the fend corrections (3).

Eij ¼ eD i;jð Þf i f j ð3Þ

5C interactions have expected values that correspondto the log-transformed count and are the sum of eachsignal component (4).

Eij ¼ D i; jð Þ þ f i þ f j ð4Þ

By scaling the row sums based on number of interac-tions, the weighted matrix balancing allows exclusion ofinteractions based on interaction size criteria withoutskewing correction factors due to non-uniform restric-tion site distribution, position along the chromosome, orfiltered fragments or fends due to read coverage. Becauseit can incorporate the distance-dependent signal, the Ex-press algorithm can operate on counts data unlike mostother matrix balancing approaches, although it also can beperformed on binary data (observed vs. unobserved) orlog-transformed counts for HiC and 5C, respectively.This algorithm allows for adjustment of counts basedon the estimated distance-dependence signal prior tonormalization in both weighted (1 and 2) and un-weighted (Knight-Ruiz) versions.

The multiplicative Probability algorithm models thedata assuming some probability distribution with a priorequal to the estimated distance-dependent signal. HiCdata can be modeled either with a Poisson or binomialdistribution (Additional file 1: Figure S7). In the case ofthe binomial distribution, counts are transformed into abinary indicator of observed/unobserved and the distance-dependence approximation function is based on this samebinary data. 5C data are modeled using a lognormal distri-bution. In both cases only counts in the set of reads A(described above) are modeled.For both the Express and Probability algorithms, a

backtracking-line gradient descent approach is used forlearning correction parameters. This allows the learningrate r to be updated each iteration t to satisfy the Armijocriteria (5) based on the cost C, ensuring that parameterupdates are appropriate.

Armijo ¼ Ct−Ct−1rXi∈A

∇f ið Þ2 ð5Þ

Filtering interactions by interaction sizeChromatin topology is organized around highly reprodu-cible regions of frequent local interactions termed ‘topo-logical domains’ [27]. Within these structures it has beenobserved that specific features can influence the frequencyof interactions in a biased and differential way up- anddownstream of them, such as transcript start sites (TSS)and CTCF-bound sites [14]. In order to account for sys-tematic noise and bias without confounding normalizationefforts with meaningful biological-relevant structures,HiFive allows filtering out of interactions using interactionsize cutoffs. In order to assess the effects of filtering outshorter-sized interactions, we analyzed data both with andwithout a lower interaction distance cutoff. For HiC datawe analyzed two mouse embyronic stem cells (ESC) data-sets with no lower limit and with a lower distance limit of500 Kb using each of the described normalization algo-rithms. This size was chosen to eliminate all but the weak-est interaction effects observed for TSSs and CTCF-bound sites [28]. HiC normalization performance wasassessed using the inter-dataset correlations. For 5C data,there is a much smaller range of interactions. In order tohandle this, we set a lower interaction size cutoff of 50 Kb.5C normalization performance was assessed as the correl-ation between 5C data and HiC data of the same cell type[27] and binned based on probed 5C fragments to createidentically partitioned sets of interactions and normalizedusing HiFive’s Probability algorithm.The differences in HiC algorithm performances with

and without the lower interaction size cutoff were var-ied, although the largest effects were seen when datawere binned in 10 and 50 Kb bins for intra-chromosomal

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interactions and for overall inter-chromosomal interac-tions (Additional file 1: Figure S8). Overall, excludingshort-range interactions made little difference for the Ex-press algorithm but did improve the performance of theProbability and Binning algorithms. The 5C algorithmsshowed an opposite result, with almost universal de-crease in performance when short-range interactionsare excluded (Additional file 1: Figure S9). As a result,learning HiC normalization parameters using HiFive al-gorithms was performed excluding interactions shorterthan 500 Kb and 5C analyses were performed using allinteraction sizes. All analyses subsequent to normalization(for example, dataset correlations) were performed acrossall interactions.

Analyzing 5C dataTo date, limited work has focused on processing of 5Cdata to remove technical biases [22–24, 29]. Of that, nonehas been formalized in published analysis software. Inorder to assess HiFive’s performance in normalizing 5Cdata, we used two different 5C mouse ESC datasets [23,24] and found correlations to HiC data of the same celltype [27] and binned based on probed 5C fragments tocreate identically partitioned sets of interactions (Fig. 2,Additional file 1: Figures S9 and S10). HiC interactionswere normalized using either HiFive’s probability algo-rithm (Fig. 2) or HiCPipe (Additional file 1: Figure S10)and heatmaps were dynamically binned to account for

sparse coverage (see Additional file 1: Methods: 5C-HiCdata correlations). Correlations were found between allnon-zero pairs of bins (fragment level resolution) follow-ing log-transformation. All of HiFive’s 5C algorithmsshowed an improved correlation with HiC data comparedto raw data, regardless of HiC normalization approach.The Binning algorithm showed the least improvement,likely due to the limits on the number of bins into whichfeatures could be partitioned and characteristics missingfrom the model, such as primer melting temperature. Thestandard matrix-balancing approach (ExpressKR) showedgood improvement, although not quite as good as theExpress and Probability algorithms. All of these normali-zations were accomplished in less than one minute pro-ceeding from a BED file and a counts file to heatmaps.

HiC analysis software comparisonSeveral algorithms have been proposed to handle inter-action data normalization (Table 1). These analysis ap-proaches can be divided into two varieties, probabilisticand matrix balancing. The probabilistic approach isfurther divided into combinatorial and multiplicativecorrections. The combinatorial probability model is im-plemented in HiCPipe [14] and remains one of themost popular approaches. This approach uses one ormore restriction fend characteristics partitioned intoranges of values and iteratively learns correction valuesfor each combination of ranges based on a binomial

Fig. 2 5C analysis performance. HiFive normalization of 5C data and their correlation to corresponding HiC data. a Correlation of 5C data(intra-regional only) with the same cell type and bin-coordinates in HiC data, normalized using HiFive’s probability algorithm for two differentdatasets and using each of HiFive’s algorithms. b Heatmaps for a select region from each dataset, un-normalized, normalized using HiFive’sprobability algorithm, and the corresponding HiC data, normalized and dynamically binned

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distribution of observed versus unobserved fend inter-actions. A multiplicative modeling approach is used inthe analysis software HiCNorm [16]. HiCNorm uses aPoisson regression model using binned counts insteadof binary output and assuming that biases from fendcharacteristics are multiplicative between bin combina-tions. A different multiplicative approach is matrixbalancing, which finds a value for each row/column ofa symmetric matrix (or in this case, heatmap) such thatafter multiplication of each matrix value by its associ-ated row and column values, the sum of each row andcolumn is one. This has been described with at leastfour different implementations in the literature [15, 17,20, 30] although only two software packages making useof it have been published (HiCLib [17], now included inthe R package HiTC [21] and Hi-Corrector [20]). For thispaper, we chose to use our own implementation of thealgorithm described by Knight and Ruiz [26] for compari-son due to speed and ease of use considerations.

Method performancesTo assess HiC analysis method performances we used twodifferent pairs of HiC datasets [15, 27, 31], finding inter-action correlations across different restriction digests ofthe same cell type genomes. The Dixon et al. data [27]were produced using mouse ESCs digested with eitherHindIII or NcoI, yielding approximately 4 Kb fragments.The Selvaraj et al. data [31] were produced from humanGM12878 cells using HindIII, while the Rao et al. data[15] were produced from human GM12878 cells using the4 bp restriction enzyme MboI, producing approximately

250 bp fragments. This allowed assessment of methodperformance and data handling across a range of experi-mental resolutions. Correlations were calculated for 10mutually exclusive intra-chromosomal (cis) interactionranges and across all cis interactions simultaneously forfour binning resolutions. Correlations were also calculatedfor inter-chromosomal interactions for two resolutions.HiC analysis methods showed varied performances

across interaction size ranges, resolutions, and datasetsfor intra-chromosomal interactions (Fig. 3a and b). Forsmall interaction sizes, HiFive’s Probability and Expressalgorithms performed consistently well regardless ofresolution. At longer interaction distances the Expressalgorithm typically outperformed the Probability algo-rithm. HiCNorm showed a nearly opposite performancewith poorer inter-dataset correlations for shorter-rangeinteractions but higher correlations at longer ranges,relative to other methods. HiCPipe’s performance ap-peared to depend on binning resolution. At higher resolu-tions (≤50 Kb), HiCPipe performed worse than themajority of methods. However at lower resolutions ittended to outperform other methods, regardless of inter-action size range. HiFive’s Binning algorithm had a moreconsistent performance around the middle of all of themethods across all binning resolutions, with the exceptionof the 1 Mb resolution for the human data where it per-formed the worst. Standard matrix balancing consistentlyperformed at or near the bottom of the group regardlessof the interaction size range or resolution.Correlations across all intra-chromosomal interactions

showed much more consistency between analysis method-ologies (Fig. 3c and d). This is primarily due to the factthat the main driver of inter-dataset correlation, the inter-action distance-counts relationship, was present in all ofthe analyzed data. HiFive’s Probability and Express algo-rithms were again top performers across almost everyintra-chromosomal comparison, although the Probabilityalgorithm showed a decreasing advantage with decreasingbinning resolution. HiCNorm, HiCPipe, matrix balancing,and HiFive’s Binning algorithm were highly consistent interms of performance for the mouse datasets. For the hu-man inter-dataset correlations HiCPipe and matrix balan-cing showed a slightly better performance than averagewhile HiCNorm faired worse. HiFive’s Express algorithmwas still the top performer.Inter-chromosomal datasets showed a wider range of

performances and were strongly dependent on whichdatasets were being analyzed (Fig. 3c and d). For mouseinter-chromosomal interactions, HiFive’s Probability andExpress algorithms performed much better than othermethods at the 250 Kb binning resolution, but consistentwith other methods at the 1 Mb resolution. HiCNormshowed worse performance at both bin sizes for themouse datasets. HiCPipe showed the best performance at

Table 1 A comparison of HiC Analysis software algorithms andfeatures

Method Normalizationalgorithm

Can accountfor distance

Fend-levelresolution

Parallelizable

HiFive Matrixbalancing

X X X

Binning X X X

Probability X X X

HiCPipe Binning X X

HiCNorm Poissonregression

HiCLib Matrixbalancing

X

HiTCa Poissonregression

Matrixbalancing

X

HOMER Readcoverage

X X

Hi-Corrector Matrixbalancing

X X

aThis method is an R-based implementation of HiCLib’s and HiCNorm’snormalization approaches

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the 1 Mb resolution, slightly above other methods, but thesecond worst performance at the 250 Kb resolution. Re-sults for the human datasets were more consistent acrossresolutions. HiCNorm, HiFive’s Express algorithm, andmatrix balancing performed best in both cases with Ex-press doing slightly better at the 250 Kb resolution andHiCNorm at the 1 Mb resolution. The remaining methodsshowed similar performance to each other, although HiF-ive’s Probability algorithm performed slightly worse thanHiFive’s Binning algorithm and HiCPipe.The inconsistency between results for cis and trans

interactions suggests that no approach is ideal for bothtypes of interactions. To further explore this we lookedat the effects of pseudo-counts in the Binning/HiCPipenormalization scheme and the effects of distance-dependence on normalization. Pseudo-counts are valuesadded to both expected and observed reads to mitigate

the impact of stochastic effects. HiCPipe showed astronger performance compared to HiFive’s Binning al-gorithm at longer ranges and at larger bin sizes. We de-termined that the primary difference was the inclusion ofpseudo-counts in all feature bins prior to normalization.By progressively adding counts, we found that cis inter-action correlations decreased at shorter interactionranges and overall, although the correlations increasedat longer ranges and for trans interactions (Additionalfile 1: Figure S11).We also performed parallel analyses using our weighted

matrix balancing algorithm, Express, with and without theestimated distance-dependence signal removed prior tonormalization (Additional file 1: Figure S12). This showeda similar effect to the addition of pseudo-counts, such thatleaving the distance-signal relationship intact resulted instronger long-range interaction correlations in larger bin

Fig. 3 HiC method comparison. Interaction correlations between datasets created with different restriction enzymes for multiple normalizationschemes across different binning resolutions. Two datasets are shown, mouse and human. Each mouse dataset was produced using a six-baserestriction enzyme. The human datasets were mixed, one produced with a six-base cutter and the other with a four-base cutter. a Data werenormalized using several approaches and compared for correlation between two mouse HiC datasets. Interactions were broken down into 10groups of non-overlapping cis interaction ranges for four resolution levels. b Correlations for 10 different non-overlapping cis interaction rangesat each resolution for each analysis approach. c Overall mouse dataset correlations for each resolution for intra-chromosomal (cis) and inter-chromosomal(trans) interactions. d Overall human dataset correlations for each resolution for intra-chromosomal (cis) and inter-chromosomal (trans) interactions

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sizes, stronger 1 Mb binned trans correlations, and pooreroverall cis interaction correlations across all bin sizes.

Computational requirementsIn order to determine the computational requirements ofeach analysis method, we ran each analysis on an abbrevi-ated dataset consisting of a single chromosome of cis in-teractions from the mouse NcoI dataset starting fromloading data through producing a 10 Kb heatmap. Allnormalizations were run using a single processor and pub-licly available scripts/programs. The exception to this wasbinning the counts and fragment feature data for HiC-Norm. No script was provided for this step so one waswritten in R to supplement HiCNorm’s functionality.Runtimes varied greatly between normalization methods,

ranging from less than 7 min to approximately 12.5 h(Fig. 4). With the exception of HiFive’s Probability algo-rithm, HiFive performed better in terms of runtimethan all other algorithms. HiCPipe and HiCNorm bothshowed long runtimes at least an order of magnitudeabove other methods. The slowest approach, though,was HiFive’s Probability algorithm. This was due to itsmodeling of every interaction combination across thechromosome. HiFive’s implementation of the Knight-Ruiz matrix balancing algorithm, ExpressKR, showed adramatically faster runtime than any other approach.This was the result of HiFive’s fast data loading and ef-ficient heatmapping without the need for distance-dependence parameter calculations.

ScalabilityBecause of the ever-increasing resolution of experimentsand the corresponding size of interaction datasets, scal-ability is a primary concern for HiC data analysis. Al-though we compared methods on an even playing field,this does not reflect the complete performance picture

when considering finer-scale resolution, processing acomplete genome interaction dataset, and more availablecomputational resources.There are two approaches to determining analysis reso-

lution, prior to or after normalization. Of the methodspresented, only HiCNorm determines the resolution ofanalysis prior to normalization. While it performs well,this means that the processing time and memory require-ments scale exponentially with resolution. We were un-able to perform any analyses at resolutions for bin sizessmaller than 10 Kb using this approach. The remainingmethods all find correction values for individual fends,meaning that corrections are performed prior to binninginteractions.The increase in dataset size, either due to genome size

itself or a finer-scale partitioning of the genome, can beoffset by employing more processing power by means ofparallelization. HiCLib and HiCNorm do not appear tohave any such capability. HiCPipe does have the abilityto parallelize calculation of model bin sizes prior tonormalization and calculations for heatmap production,although a single processor performs all of the actualnormalization calculations. HiFive, on the other hand,has the ability to run in parallel for nearly all phases ofan analysis. The two exceptions are loading the initialdata and filtering reads, although the latter is very fastalready. All normalization algorithms, including theKnight-Ruiz algorithm implemented in HiFive, havebeen parallelized for HiC analysis using MPI. Theparallelization is implemented in such a way that theadditional memory overhead for each new process isminimal.

ConclusionsHiC analysis remains a challenging subject, as demon-strated by the varied performances across all method-ologies discussed here. No single approach appears to

Fig. 4 Running time for HiC analysis methods. For each method, the runtime in minutes is partitioned into the time required for each stage ofthe processes. All times were determined running methods on an abbreviated dataset of chromosome 1 for the mouse HindIII dataset using asingle processor. Note that because of several extremely long runtimes, the graph includes multiple splits

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be ideally suited for all cases, suggesting that the experi-mental goal should drive the choice of analysis software. Itis unclear how best to assess HiC normalization perform-ance as there is no ‘gold standard’ for determining thequality of a HiC dataset or how well systematic noise hasbeen accounted for during an analysis. As seen in the dif-ferences in correlation between mouse and human data-sets (Fig. 3), factors such as restriction fragment sizedistributions, cut site density, sequencing depth, and HiCprotocol can dramatically impact the similarity of resultingdatasets. Further, in order to detect biologically relevantfeatures against the background of the distance-signal re-lationship, the data need to be transformed, typically usinga log-transformation. This skews the resulting comparisonby ignoring interactions for which no reads have been ob-served, an increasing problem as binning size decreases orinteraction size increases. At longer ranges, non-zero binsare sparse and dominated by macro features (such as A-Bcompartments), a situation that can result in increasingcorrelations (Fig. 3a and b). Two observations suggest thisis not an artifact. First, the long-range interaction correl-ation increase is seen in the human but not mice data,reflecting differences in genome organization. Second, thecorrelation increases are seen across all methodologiesand algorithms.Normalization software attempts to account for many of

these confounding factors and allow direct comparisonbetween datasets produced by different labs, protocols,and even across species although what can reasonably beexpected in terms of this normalization process is unclear.This question depends on many factors and we may nothave sufficient understanding of chromatin architecturevariability across a cell population to answer it accurately.The resolution (bin size), similarity of datasets in terms ofsequencing depth, restriction fragment size distributions,and protocol, as well as cell population size and popula-tion similarity from which the HiC libraries were madewill all influence the correlation. At a low resolution, say1 Mb, we should expect nearly a perfect correlation. How-ever, at much higher resolution differences in mappabilityand RE cut-site frequency will strongly influence thecorrelation. Further, we need to consider the distance de-pendence of the signal as this is the strongest driver of thecorrelation and can give a false impression of comparabil-ity between datasets.To address these normalization challenges, we have

created HiFive, an easy-to-use, fast, and efficient frame-work for working with a variety of chromatin conform-ation data types. Because of the modular storagescheme, re-analysis and downstream analysis is madeeasier without additional storage or processing require-ments. We have included several different normalizationapproaches and made nearly all aspects of each algorithmadjustable, allowing users to tune parameters for a wide

range of analysis goals. HiFive is parallelized via MPI,making it highly scalable for nearly every step of HiC dataprocessing.For 5C data, HiFive is the only analysis program avail-

able for normalization and allows easy management of5C data. We have demonstrated that 5C data normaliza-tions performed by HiFive greatly improve consistencybetween 5C data and corresponding HiC data acrossmultiple datasets.We have also shown HiFive’s performance in handling

HiC data. HiFive is consistently performing at or aboveother available methods as measured by inter-datasetcorrelations for cis interactions. In addition, we havedemonstrated that HiFive is tunable to achieve superiortrans performance if desired, albeit at the expense ofperformance across cis interactions. HiFive has alsoproved capable of handling very high-resolution data,making it useful for the next generation of HiC experi-mental data.In terms of performance considerations, our analysis

suggests that, out of all of the methods considered, thebalance between speed and accuracy is best achieved byHiFive-Express or HiFive-ExpressKR. This appears tobe true regardless of resolution or dataset size. In orderto get this performance, it is crucial to use the distance-dependence adjustment prior to normalizing, necessitat-ing the need to pre-calculate the distance-dependencefunction. Because this requires iterating over every pos-sible interaction, using multiple processors is highly rec-ommended. If not possible, HiFive-ExpressKR withoutdistance correction is a robust fallback method. If compu-tational resources are not a limiting factor, we recommendHiFive-Probability. With approximately 100 CPUs, thehigh-resolution human data were processed in about aday. At fine-scale binning, this approach yields the best re-sults of all methods.While HiFive allows for superior normalization of data

compared to other available software under many condi-tions, it also provides users with alternative options forfast analysis with minimal computational requirementsat only a slight accuracy cost, opening high-resolutionHiC and 5C analysis to a much larger portion of the sci-entific community. HiFive is available at http://taylorla-b.org/software/hifive/. Source code is provided under anMIT license and at https://github.com/bxlab/hifive orinstalled using pip from http://pypi.python.org.

Additional files

Additional file 1: A detailed methods section, a table listing thesources of datasets used, and Supplemental figures. (PDF 2245 kb)

Additional file 2: A tar archive containing the HiFive softwarelibrary. (BZ2 578 kb)

Sauria et al. Genome Biology (2015) 16:237 Page 9 of 10

Additional file 3: A tar archive containing all scripts using togenerate the data, analyses, and figures presented in this paper.(BZ2 20009 kb)

Additional file 4: The software documentation. (PDF 530 kb)

Abbreviations3C: chromosome conformation capture; 5C: 3C carbon copy; bp: base pair;cis: intra-chromosomal; CPU: central processing unit; ESC: Embryonic stemcell; Fend: fragment-end; Kb: kilobase; Mb: megabase; MPI: message passinginterface; RE: restriction enzyme; trans: inter-chromosomal.

Competing interestsThe authors declare that they have no competing interests.

Authors’ contributionsMEGS, JEPC, VGC, and JT conceived the project and developed featurerequirements. JEPC made significant contributions to the design of the 5Ctools. MEGS developed all algorithms, designed and wrote all software, andwrote the manuscript. JT contributed to the manuscript and supported theproject. All authors read and approved the final manuscript.

AcknowledgementsResearch reported in this publication was supported by the NationalInstitutes of Health under awards R01GM035463 to VC and R01DK065806 toJT and by American Recovery and Reinvestment Act (ARRA) funds throughgrant number RC2HG005542 to JT. The content is solely the responsibility ofthe authors and does not necessarily represent the official views of theNational Institutes of Health.

Author details1Departments of Biology and Computer Science, Johns Hopkins University,Baltimore, MD 21218, USA. 2Department of Bioengineering, University ofPennsylvania, Philadelphia, PA 19103, USA. 3Department of Biology, EmoryUniversity, Atlanta, GA 30322, USA.

Received: 8 May 2015 Accepted: 14 October 2015

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