dataesr/scientific-paragraphs-categorization huggingface dataset:
Benchmarking against GOTHiC and CHi-CAGO consistently showed that ChiCMaxima was a more stringent method for interaction calling, but more robust to handling undersampling when comparing biological replicates. Further, ChiCMaxima gave a higher enrichment for interactions containing hallmarks of regulatory chromatin, such as histone modifications indicative of enhancers or CTCF binding sites, suggesting that its false positive detection rate for functional chromatin loops may be lower than for the other methods. Analysis of the chromatin contact network resulting from ChiCMaxima-called interactions in mES cells identified potential key roles of Polycomb proteins and elongating RNA polymerase II, in line with previous findings  [22] , further demonstrating the utility of ChiC-Maxima. In addition to the pipeline for calling CHi-C interactions, we also present ChiCBrowser, a user-friendly and flexible browser for inputting whole CHi-C datasets and then normalizing and visualizing bait-specific interaction profiles.
Benchmarking of ChiCMaxima We performed ChiCMaxima on a published mES promoter CHi-C dataset  [11]  and compared our results with published ones from GOTHiC and CHiCAGO applied to the same dataset  [11, 20]  (Table  1 ; Additional file 4: Table  S1 ). On visual inspection, ChiCMaxima successfully identified clear promoter interactions, some of which we also validated by 4C, and seemed to call fewer spurious ones than the other two methods (Fig. 2 ). Indeed, ChiCMaxima identified fewer promoter-centered interactions  (23, 583)  than  CHiCAGO (94, 148)  or GOTHiC (548,551). Pairwise comparisons revealed a striking dissimilarity of called interactions across all three methods-with the exception of ChiCMaxima interactions within the GOTHiC set, the majority of called interactions from one method is not shared with those of another (Fig.
We reasoned that an interaction calling method that found the greatest proportion of putative enhancers and/or CTCF sites within a promoter CHi-C dataset was most likely to have the best true positive detection rate. Based on this, ChiCMaxima compares favorably to the other two methods. It has a higher enrichment for interacting regions containing CTCF, H3K27ac, and H3K4me1 (Fig. 3c ), with a ~2-fold improvement over CHiCAGO and ~5-fold improvement over GOTHiC. The enrichment in these functional hallmarks is decreased when the d parameter of ChiCMaxima is reduced to zero, but is still slightly better than CHiCAGO. Chromatin interaction networks mediated by Polycomb group proteins have also been well described in embryonic stem cells  [17, 22, 30, 31]. Reflecting this, promoter-interacting regions called by ChiCMaxima and CHiCAGO are also comparably and highly enriched in binding for core components of the two major Polycomb repressive complexes, Ring1B and Suz12 (Fig.
ChiCMaxima performance in other CHi-C datasets To test whether the tuned ChiCMaxima parameters are more globally applicable, and to more comprehensively benchmark the method, we applied ChiCMaxima with the standard parameters (w = 20, s = 0. 05, b = 30 kb, c = 1. 5 Mb, geometric mean filter) to other published CHi-C datasets. Notably, whereas ChiCMaxima and CHiCAGO were readily applied to these data, we were unable to implement GOTHiC due to the very high memory requirement of the method. First, we called interactions from a complementary mES promoter CHi-C dataset (two biological replicates), which used a different probe design and a more frequently cutting restriction enzyme, MboI, in the HiCap strategy  [10]. These present an analytical challenge, since they have been relatively less deeply sequenced, and are derived from a much more complex mixture of Hi-C ligation products (~200-fold more possible pairwise restriction fragment combinations).
Despite this greater complexity, the same proportion of HiCap interactions were reproduced across biological replicates within d = 20 kb as for CHi-C (~40%). Perhaps due to its reliance on calling interactions at the level of single restriction fragments within a more complex pool of products, CHiCAGO called ~5-fold more interactions from the HiCap data than from CHi-C; ChiCMaxima actually called 1. 5-fold fewer interactions (Additional file 5: Table  S4 ). However, visual inspection of the different calls on HiCap profiles, assessment of CTCF, enhancer and Polycomb mark enrichments, and the proportions of candidate enhancers contained within the interaction sets strongly indicate that ChiCMaxima is the more stringent, robust interaction calling method (Fig. 4 ). We then compared ChiCMaxima and CHiCAGO interaction calling within deeply sequenced CHi-C datasets derived from nine different primary human hematopoietic cell types (erythroblasts; M0, M1, and M2 macrophages; megakaryocytes; monocytes; naïve CD4 and CD8 T cells; neutrophils)  [13].
An additional test of the analytical methods came from the presence of greater numbers of biological replicates  (3) (4). For all pairwise combinations of biological replicates, at least 50% of interactions were maintained within d = 20 kb. We found that extending the ChiCMaxima method for handling two replicates (keeping only interactions that are present in both replicates, within a threshold distance, d (usually 20 kb), of each other) to three or four performed well (see Additional file 2 for operational details). Strictly requiring that interactions are present within a fixed window of d for all of the replicates called similar numbers of interactions as for the two replicates of mES CHi-C; furthermore, applying this method to the datasets with four biological replicates (megakaryocytes and naïve CD4 T cells) did not drastically reduce the numbers of called interactions as compared to those    S13 ).
The CHiCAGO threshold scores were not identical in the different cell types, but were much higher than the standard threshold of 5. Importantly, ChiCMaxima gave consistently higher enrichments for H3K27ac and, to a lesser extent, H3K4me1, suggesting that the method does more than simply modulate the threshold of existing interaction calling tools. Overall, these results suggest that ChiCMaxima provides a good compromise of stringency and coverage when assigning target genes to putative cis-regulatory elements, with a robust set of parameters that can be globally applied to different CHi-C datasets.
ChiCBrowser To enable visualization of promoter (and other sparse bait) CHi-C results, alongside linear epigenomic profiles and the interactions called by ChiCMaxima or other methods, we also developed ChiCBrowser, an R-based GUI browser. Unlike the WashU browser  [38] , which displays all interactions simultaneously and can be difficult to interpret visually, ChiCBrowser displays virtual 4C profiles, with the bait and display window defined by the user via a graphical window (Fig. 7 ). Its major functionalities are described below; a full user guide is given in Additional file 2. All CHi-C datasets which may be plotted together or compared are made into one input file (see Additional file 2 for input format details), which only needs to be loaded once into the memory for all subsequent plots to be made.
of chromatin assortativity of different chromatin features within the interaction networks called by ChiCMaxima or CHiCAGO (restricted to promoter-other end interactions). The class of the different chromatin features is color coded To allow fairer comparisons between datasets, all CHi-C-derived virtual 4C profiles are quantile normalized  [39]  before the running mean values are plotted. Since CHi-C experimental designs usually include biological replicates and different conditions to be compared, ChiC-Browser provides flexibility (via the Conditions menu) to define the plot levels of each single CHi-C dataset. For example (shown in Additional file 1: Figure  S8 ), biological replicates can be allocated to different levels and plotted side by side to compare experimental reproducibility, or given the same plot level, so that the mean profile can be plotted for comparison with other experimental conditions.
7b ). However, the input format of these interactions is essentially the chromosomal coordinates of genomic regions associated with a specific bait (see Additional file 2 for details), so this plotting functionality can be adapted to highlight any subset of the CHi-C dataset that the user designs (e. g. , interactions unique to one condition or tissue type and not another). This flexibility in particular makes ChiCBrowser very useful to explore different hypotheses when browsing interactomes. As for other ChiCBrowser functions, the user can alter the name and color of these annotations, as well as select or de-select subsets of them.
Discussion We present two tools for processing and interpretation of Chi-C datasets: ChiCMaxima for interaction calling and ChiCBrowser for bait-specific visualization of interaction profiles. Both were developed to overcome the currently identified unique challenges presented by these data. Despite a clear improvement over conventional Hi-C with limited sequencing throughput, the main issue with CHi-C outputs is that they are greatly under-sampled, creating problems of reproducibility across biological replicates at the highest resolutions (Additional file 1: Figure  S1  and S3 ). The subsequent paucity of bait-specific data confounds the generation of powerful statistical models, so previous methods either appear to have high false positive rates (e. g. , GOTHiC; see Fig. 2 ), and/or rely on combining data from multiple baits (e.
g. , CHiCAGO) to avoid overfitting model parameters. ChiCMaxima uses a limited number of model parameters to be estimated by naively just searching for local maxima in the virtual 4C profiles (Fig. 1 ), a logic for calling chromatin loops that was used in some of the first 3C studies  [23, 40]. Application of an additional filter was necessary to remove spurious local maxima in distal regions of low signal, and we found satisfactory results from a simple threshold based on geometric means of reads stratified by interaction distance (Additional file 1: Figure  S2 ; Additional file 3). For single datasets, only four parameters need to be defined in ChiCMaxima: the window for local maximum computation, the loess smoothing span, the total genomic span over which maxima are computed, and the bin width for stratifying the geometric mean filter.
The optimal distance may be expected to vary between experiments, particularly with sequencing depth and complexity of the assessed genome. For this reason, we provide tools to allow the user to explore the distributions of closest distances between interactions called in pairs of replicates and thus determine the optimal setting. However, for all 11 CHi-C datasets we tested, these distributions were very similar, with ~40% of interactions within 20 kb of each other in replicate experiments. We also note that such an approach is robust to inclusion of more than two biological replicates and that ChiCMaxima performance was still better than other methods at the most restrictive condition of allowing no distance between reproduced interactions within replicates (e. g. , Figs. 3  and 5 ), albeit with reduced sensitivity.
Further studies will be required to determine the functional significance, if any, of such intragenic chromatin looping events, but ChiC-Maxima seems to be a very useful tool for studying them via CHi-C studies. The ChiCBrowser tool is a flexible, user-friendly GUI to generate virtual 4C profiles, necessary for visual inspection of most CHi-C datasets. It has a built-in flexibility to allow biological replicates or different combinations of biological conditions to be assessed in parallel, and a similar flexibility is also built into the management of gene annotations and epigenomic profiles that are plotted alongside the CHi-C data (Fig. 7 ). Called interactions, whether by ChiCMaxima or other methods, can be easily highlighted on the display, based on a simple input format that can be adapted to highlight any subset of the CHi-C subset that may be of interest to the user.
Conclusions Capture Hi-C, particularly strategies with sparse baits such as promoters, is a rapidly growing technique hampered by the limited tools available to meet the unique challenges of analyzing the datasets produced. ChiCMaxima adopts a simplistic approach, with minimal prior assumptions on the data, and successfully calls CHi-C interactions, performing favorably with existing methods in various benchmarks. Most notably, ChiC-Maxima provides the flexibility to deal with problems of reproducibility across biological replicates at high resolutions, a persistent but often overlooked challenge of CHi-C. Combined with the user-friendly, flexible ChiC-Browser, we provide a suite of tools for CHi-C analysis and visualization which will be of use to many in the nuclear organization community.
Datasets used in this study Mouse ES CHi-C  [11]  and HiCap  [10]  data were downloaded from ArrayExpress (E-MTAB-2414)  [49]  and GEO (GSE60495)  [50] , respectively; human hematopoietic cell CHi-C data  [13]  were downloaded as CHiCAGO data objects from the Open Science Framework (https://osf. io/ u8tzp/)  [51]. Interactions previously called by CHiCAGO and GOTHiC from the mES CHi-C data were downloaded from GEO (GSE81503)  [52]  and ArrayExpress (E-MTAB-2414)  [49]  respectively. Mouse ES ChIP-seq data  [53] [54] [55]  were all downloaded from GEO: CTCF and H3K27ac (GSE29218)  [56] ; H3K4me1 (GSE47082)  [57] ; Ring1B and Suz12 (GSE42466)  [58]. All human hematopoietic cell ChIP-seq data were obtained from the BLUEPRINT consortium (ftp://ftp. ebi. ac. uk/pub/databases/blueprint/data/homo_sapiens/GRCh37/)  [59].
Sample pre-processing For all mES (CHi-C and HiCap) datasets, raw sequencing reads were processed by custom perl and R scripts, originally derived from the Hi-C analysis pipeline developed in  [60] , which entails mapping the paired reads with Bowtie  [61] , pairing, removing common Hi-C artifacts (PCR duplicates, circularized fragments, non-digested fragments), and then converting from genomic coordinates to restriction fragment space. Operationally, this generates only tiny differences from outputs of HiCUP  [62]. Custom perl scripts, explained in Additional file 2 and available on Github  [63] , were used to convert paired bed files to the input format for ChiCMaxima. These scripts can also be applied to outputs of other Hi-C analysis tools, such as HiCUP  [62] , HiC-Pro  [64] , or Juicer pre-inputs  [43].
For the human hematopoietic cell CHi-C datasets, total CHi-CAGO output files were downloaded as R objects  [51]. These comprise the downstream results of HiCUP processing of the data and CHiCAGO analysis (on merged biological replicates), resulting in a table containing all the fields required for ChiCMaxima analysis (see Additional file 2 for details), for all bait-linked interactions covered by sequencing reads. These tables were manipulated in R to make separate tables for each replicate in a format compatible with the ChiCMaxima scripts and to remove interchromosomal and bait-to-bait interactions.
ChiCMaxima The suite of scripts, made for R version ≥ 3. 2, and its full documentation (including package dependencies, found on Bioconductor or CRAN), is available on Github (https://github. com/yousra291987/ChiCMaxima) [63]. A full description of its usage, and how it is run on supplied test data, is also provided in Additional file 2. In brief, ChiCMaxima_Caller identifies interactions as local maxima of loess smoothed bait-specific interaction profiles within single CHi-C datasets. ChiCMaxima_RepAnalysis determines the distributions of the closest distance between interactions called in pairs of datasets, allowing the user to select an optimal threshold for filtering "maintained" interactions within biological replicates. ChiCMax-ima_MergeRep2 or ChiCMaxima_MergeRepMany then applies this set distance threshold to identify interactions that are conserved in two or more biological replicates, respectively.
Finally, ChiCMaxima_Collate is a utility script that generates one large table from multiple CHi-C datasets, convenient for input into ChiCBrowser. Except where stated specifically in the text, ChiCMaxima_Caller was run on each single CHi-C replicate with the parameters window_size = 20, loess_span = 0. 05, cis_window = 1,500,000, and binwidth = 30,000. ChiCMaxima_Mer-geRep2 or ChiCMaxima_MergeRepMany was run on their outputs with the parameter repdist = 20,000.
ChiCBrowser The browser is run from an R environment (version ≥ 3. 2), and its full documentation (including package dependencies, found on Bioconductor or CRAN) is also available on Github (https://github. com/yousra291987/ ChiCMaxima)  [63]. A full user guide is also presented in Additional file 2, along with examples of its use on supplied test data. This browser or small variants in the code (e. g. , to show raw data instead of after smoothing by running means in Additional file 1: Figure  S1b ) were used to generate all the screenshot images presented in the article.
CHiCAGO and GOTHiC interaction lists The previously called lists of interactions from both mES CHi-C replicates using CHiCAGO (GSE81503_-mESC_PCHiC_merge_final_washU_text. txt; CHiCAGO score ≥ 5) or GOTHiC (ESC_promoter_other_significan-t_interactions. txt; log (observed/expected) ≥ 10) were downloaded directly from their repositories  [49, 52]. CHiCAGO-called interactions from human primary hematopoietic cell CHi-C datasets were downloaded directly from their repository  [51] , and then filtered to remove interchromosomal and bait-to-bait interactions. For each cell type, the interactions were called as those with a score ≥ 5. For interaction calling within individual biological replicates by CHiCAGO, interactions with a score ≥ 5 were used after running CHiCAGO with default parameters (maxLBrownEst = 1,500,000; minFra-gLen = 150; maxFragLen = 40,000; minNPerBait = 250; binsize = 20,000; removeAdjacent = TRUE; adjBait2bait = TRUE; tlb.
filterTopPercent = 0. 01; tlb. minProxOE-PerBin = 50,000; tlb. minProxB2BPerBin = 2500; tech-Noise. minBaitsPerBin = 1000; brownianNoise. samples = 5; brownianNoise. subset = 1000; brownianNoise. seed = NA; weightAlpha = 34. 11573; weightBeta = -2. 586881; weightGamma = -17. 13478; weightDelta = -7. 076092). The same parameters were used for CHiCAGO interaction calls on the HiCap data (both replicates treated simultaneously). Intersections of called interactions across biological replicates (Additional file 1: Figure  S1a ) were found by searches for called interactions with identical Bait_name and ID_OE columns.
Assessing distances between potentially conserved interactions across biological replicates This is performed by ChiCMaxima_RepAnalysis. Pairs of interaction files (the output of ChiCMaxima_Caller) are split according to their bait, and one set is defined as the query and the other set as the subject. For each non-bait fragment within the query, the genomic distance to the closest non-bait fragment within the subject set is found by the utilities within the R GenomicRanges package  [65].
Fifty-nanogram aliquots of this DNA were used as template for PCR with bait-specific primers containing Illumina adapter termini (primer sequences and optimal PCR conditions available on request). PCR reactions were pooled, primers removed by washing with 1. 8x AMPure XP beads, then quantified on a Bioanalyzer (Agilent) before sequencing with a HiSeq 4000 (Illumina). Sequence reads were filtered and mapped to DpnII restriction fragments, essentially as previously described  [5, 66]. Raw and processed 4C data are available on GEO (GSE129884)  [67]. For visualization of the 4C profiles, running means of read counts across windows of 25 restriction fragments are plotted against the genomic coordinate of the fragment interacting with the bait (Fig. 2 ).
Chromatin assortativity Interaction network analysis was performed exactly as described in  [22]. Briefly, 78 chromatin features were taken from  [71]  and peak-calling/binarization was performed as described there in 200-bp windows. For each fragment, the overlapping windows of chromatin peaks were identified and their values averaged to give a fraction of presence of any feature in each fragment. The abundance of a feature is defined as the average of that feature value across all fragments in the network considered. ChAs of a specific chromatin feature is defined as the Pearson correlation coefficient of the value of that feature across all pairs of nodes that are connected with each other. They are computed from the "assortativity" function of the R package igraph. We created a network of ChiCMaxima detected interactions (39,584 nodes, 23,583 edges).
Availability of data and materials The ChiCMaxima tools and ChiCBrowser, along with supporting documentation and test data, are available under the terms of the GNU General Public License, version 2, on https://github. com/yousra291987/ ChiCMaxima  [67]  and are permanently available at DOI: https://doi. org/10. 5281/zenodo. 2642037. 4C datasets have been submitted to GEO with the accession number GSE129884  [67]. This work used previously published data (explained in the "Methods" section), available from several public repositories  [49] [50] [51] [52] [56] [57] [58] [59]. Authors' contributions YBZ and TS conceived and developed the ChiCMaxima and ChiCBrowser tools and performed the benchmarking with other methods. AMM and NS performed and analyzed 4C experiments. VP performed assortativity and.
10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02. YBZ is supported by funds from LabEX INRT, la Region Grand Est and the ERC. AMM is supported by funds from IDEX (University of Strasbourg) and the Institut National du Cancer. NS is a fellow of the Fondation de Recherche Medicale. VP is supported by the Fondation Toulouse Cancer Santé and Pierre Fabre Research Institute as part of the Chair of Bio-Informatics in Oncology of the CRCT. TS is supported by INSERM.
Acknowledgements We thank all other members of the  Sexton lab  for advice. Sequencing was performed by the  IGBMC GenomEast platform , a member of the  France Genomique consortium  ( ANR-10-INBS-0009 ). This study makes use of data generated by the  Blueprint Consortium. A full list of the investigators who contributed to the generation of the data is available from www. blueprintepigenome. eu. Funding for the project was provided by the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no  282510 BLUEPRINT.
Introduction The Joint United Nations Programme on HIV/AIDS (UNAIDS) publishes annually updated estimates of the global HIV epidemic. These estimates are developed by countries with the support of UNAIDS and partners. National HIV programs, UNAIDS and other international organizations and donors utilize these as consensus estimates to guide program planning, policy decisionmaking and resource allocation, to monitor epidemic trends, and to track progress towards global goals and targets. In 2019, 170 countries, representing 99% of the global population, used Spectrum modelling software to develop estimates of HIV and related indicators. The methods underpinning these estimates are continually refined and evolve in response to changing epidemic dynamics, new data availability, new research, and in support of the needs of programs and decision makers.