dataesr/scientific-paragraphs-categorization huggingface dataset:
Predictive model for principal loop enrichment We built a random forest machine learning classifier  [55]  to identify biological markers predictive of regions enriched in the principal loop anchors of many-body complexes. We used publicly available biological datasets (Additional file 3: Table  S2 ), primarily from ENCODE reference epigenome for GM12878 (ENCSR447YYN)  [53, 54] , as our input features (Fig. 8a ). At each of the 39 modeled loci, genomic regions corresponding to nonoverlapping 5-KB bins were sorted based on principal loop participation; a subset of those occurring above the "elbow" inflection point (Fig. 8b ) were labeled as enriched; those occurring below the inflection point were labeled as not enriched. To avoid ambiguous labels and to provide a more robust decision boundary among enriched versus not enriched regions, we retained the top 20% of the above-elbow fraction at each locus and discarded the remainder, while still retaining all samples below the elbow.
Our final data set consisted of 231 regions enriched (i. e. , positive) in many-body interactions and 5800 regions not-enriched (i. e. , negative). To control for potential class imbalance issues during training, we used the randomForest R package  [56]  with stratified resampling to present equal number of positive and negative samples to each decision tree (n = 500) in the random forest. Classifier performance results, mean ROC AUC of 0. 805 and out-of-bag error of 21. 5% (Fig. 8c ), were obtained on a held out test set (∼ 20% of labeled samples) over 5-fold cross-validation using the caret R package  [71].
Acknowledgements We thank  Daniel M. Czajkowsky  and  Gamze Gürsoy  for helpful discussions,  Lin Du  for the help with A/B compartment calling, and  Kimberly Beiting  for her editing. This work is supported by grants  NIH R21AI126308 ,  R21AI137865 ,  R01CA204962 , and  R35GM127084  (to JL) and  CAS-18H100000104  and  NSFC-81627801  (to ZFS).
While border detection can be achieved quite efficiently using different methods (segmentation, break-point detection, etc; ref. 21 ), the calling of loops, as well as other more peculiar features such as "hairpinlike" signals, remains challenging. Most tools aiming at detecting DNA loops in contact maps rely on statistical approaches and search for pixel regions enriched in contact counts, such as Cloops  22  , HiCCUPS  23  , HiCExplorer  24  , diffHic  25  , FitHiC2 26 , HOMER  27. These programs can be computationally intensive and take several hours of computation for standard human Hi-C datasets (reviewed in ref. 22  ), or require specialised hardware such as GPU (HiCCUPS). In addition, most if not all of them were developed from, and for, human data. As a consequence, they suffer from a lack of sensitivity and fail to detect biologically relevant structures not only in non-model organisms but also in popular species with compact genomes such as budding yeast (Saccharomyces cerevisiae) or bacteria where the scales of the structures are considerably smaller than in mammalian genomes.
Results Presentation and benchmark of Chromosight. Chromosight takes a single, whole-genome contact map in sparse and compressed format as an input. It applies a balancing normalization procedure  31  to attenuate experimental biases. A detrending procedure, to remove distance-dependent contact decay due to polymeric behaviour, is then applied, which consists in dividing each pixel by its expected value under the polymer behaviour (Fig. 1b ). A template (kernel) representing a 3D structure of interest (e. g. a loop, a boundary,. ) is fed to the program and sought for in the image of the contact map through two steps (Fig. 1b ). First, the map is subdivided into sub-images correlated to the template; then, the sub-images with the highest correlation values are labelled as template representations (i.
e. proportion of relevant patterns detected) is more than threefold higher (~70%) compared to the second-best program Hicexplorer (~20%) (Fig. 1c ). As a result, Chromosight's F1 score, a metric that considers both precision and sensitivity, is also threefold higher, reflecting the effectiveness of the program at detecting more significant loops in this synthetic case study (Supplementary Fig. 2a ). To further benchmark the program's performance, we ran the three best CPU-based programs (Cooltools, Hicexplorer, Chromosight) on high resolution (10 kb), human genome-wide experimental contact maps. Chromosight outperforms existing methods regarding computing time (Fig. 1d ), without straining RAM (Fig. 1e ). For instance, on a single CPU core, it detects loops at maximum distance of 5 Mb within ~5 min compared to ~17 and 30 min for Cooltools and Hicexplorer, respectively.
To get a sense of the differences between the softwares when applied to experimental human contact maps, we compared them with default parameters on Hi-C data generated from GM12878 cell lines  33. Compared to Chromosight, we first noticed that other programs missed multiple loops which were clearly visible on the maps (e. g. Supplementary Fig. 3a ). For instance, Chromosight found 85% of the loops detected by Cooltools, the software with the highest precision in our benchmark, while overall identifying a much larger number of loops (37,955 vs. 6264, respectively) (Supplementary Fig. 3c ). We then measured the proportion of loops with both anchors overlapping CTCF peaks identified from ChIP-seq  34. Almost all (~95%) loops detected by Hiccups and Cooltools, the most conservative programs, co-localize with CTCF enriched sites, compared to ~64% for the loops detected by Chromosight and Hicexplorer (Supplementary Fig.
pombe yeast, which has longer chromosomes. On the other hand, loop calling on contact maps generated from cells in G1, where cohesin does not stably binds to chromosomes, yielded only 115 loops (Fig. 2d  and Supplementary Fig. 4a ). Interestingly, this pool of loops appears different from the group of loops detected during mitosis suggesting that cohesin independent processes act on chromosomal loop formation in yeast (Fig. 2d  and Supplementary Fig. 4a ). Notably, loop anchors were enriched in highly expressed genes (HEG) (Supplementary Fig. 4a ). To validate the biological relevancy of the loops detected by Chromosight during mitosis, we further analysed their dependency and association to cohesin using the quantification mode implemented in the program (Methods and Supplementary Fig. 5a ).
4b ). Exploration of various genomes and patterns. To further test the versatility of Chromosight, we called all three kernels described in Fig. 1a , i. e. loops, borders and hairpins (Supplementary Fig. 6 ) in Hi-C contact maps of human lymphoblastoids (GM12878)  36  (Fig. 3a ). With default parameters, Chromosight identified 18,839 loops (compared to ≃10,000 detected in ref. 6  ) whose anchors fall mostly (~58%, P < 10 -16 ) into loci enriched in cohesin subunit Rad21 (Fig. 3b ). Decreasing the detection threshold (Pearson coefficient parameter) allows to detect lower intensity but relevant patterns (Supplementary Fig. 7a ). The program also identified 9638 borders, ~75% of which coincide with CTCF binding sites, compared to ~14% expected (P < 10 -16 ). In human, TADs are known to be delimited by CTCF-enriched sites, suggesting that Chromosight does indeed correctly identify boundaries involved in TADs delimitation.
Automatic detection now unambiguously supports a specific viral chromosome structure that could impact the transcriptional regulation and metabolism of the virus  40. Application to different proximity ligation protocols. Besides Hi-C, Chromosight can be applied on contact data generated with alternative protocols developed to explore various aspect of chromosomal organisation (Fig. 4a ). We retrieved publicly available datasets from asynchronous human cells spanning a range of techniques (i. e. ChIA-PET, DNA SPRITE, HiChIP and Micro-C) from the 4D Nucleome Data Portal  41  , and applied loops detection in the resulting contact maps. In situ ChIA-PET  42  quantifies the contact network mediated by a specific protein of interest thanks to the addition of an immunoprecipitation step. Chromosight required adjustment of a single parameter to produce visually satisfying loop calling in in situ ChIA-PET data.
We then performed loop detection on DNA Split-Pool Recognition of Interactions by Tag Extension (SPRITE) data  43. This approach requires cross-linking and fragmentation of chromatin but does not use ligation. Instead, it splits the content into 96-well plates with barcode molecules in each well. The barcode signature allows clustering of complexes that were originally part of a higher-order chromatin structure in the nucleus. Chromosight was able to detect patterns that visually correspond to loops, although the noise present in this original proof-of-principle dataset made detection challenging. We then analysed HiChIP data  44  , a protocol similar to ChIA-PET but with a better signalto-noise ratio, and that requires a lower amount of input DNA. The results of loop calling on HiChIP matrices were very close to those from Hi-C (Fig.
4c ). The resulting spectra were quite similar, with loop scores peaking around 120 kb for each protocol. Surprisingly, a secondary peak was also clearly visible at 250 kb, corresponding to about twice the fundamental frequency. This peak was clearest with the Micro-C data. These peaks were absent from dataset generated directly on mitotic condensed chromosomes (T = 0 from ref. 46  ), but using the same ChIP-seq dataset (Supplementary Fig. 8c ). The median distance between cohesin peaks called from ChIP-seq was 468 kb, suggesting that this parameter didn't introduce a bias accounting in the 120 kb. This double peak in the distribution of cohesin contacts as a function of their genomic distance in interphase cells remains to be validated independently, and its signification characterised. Point and click mode.
49  ). Using the kernel generated de novo from the S. cerevisiae contact map, Chromosight automatically detected 26 out of the 28 inter-centromeric patterns of C. albicans, along with one false positive (most likely a genome misassembly, located at the edge of the map) (Fig. 5 ). These positions are nevertheless sufficient to point at centromere positions, and can for instance then be used to characterise their genomic coordinates  47. Note that, although subtelomeric regions in yeast tend to cluster in yeast nuclei and therefore display discrete contacts reminiscent of those of peri-centromeric contacts, Chromosight was able to discriminate between those two patterns, detecting specifically inter-centromeric interactions. The program was therefore able to correctly assess the subtle geometrical differences between these two patterns.
Discussion In this work, we present Chromosight, a computer vision program to detect 3D structures in chromosome contact maps. We show that Chromosight outmatches other programs designed to detect chromosome loops, and that it can be used to extract other biologically relevant patterns generated through different chromosome capture derivatives. Chromosight is versatile and we expect that additional pattern configurations will be added by the community, such as stripes, bow-shaped patterns, patterns associated to misassemblies or structural variations (e. g. inversions, translocations. ) or any pattern of interest that the user can propose. The approach could therefore be used to investigate structural rearrangements in cancer cells, for instance, although the sensitivity of the program to detect rearrangements taking place in only a fraction of a population of cells remains to be tested.
The first by placing several occurrences of the border pileup on the diagonal, where the distance between borders follows a normal distribution fitted on the experimental coordinates. The second probability map is generated by adding the loop kernel 2-100 pixels away from the diagonal with the constraint that it must be aligned vertically and horizontally with border coordinates. For each generated matrix, the product of the P(s), borders and loops probability maps is then computed and used as a probability law to sample contact positions while keeping the same number of reads as the experimental map. This simulation method is implemented in the script chromo_simul. py, which can be found on the github repository: https://github. com/koszullab/chromosight_analyses_scripts. Benchmarking. To benchmark precision, sensitivity and F1 score, the simulated Hi-C data set with known loop coordinates were used.
Each algorithm was run with a range of 60-180 parameter combinations (Supplementary Fig. 2 ) on 2000 simulated matrices and F1 score was calculated on the ensemble of results for each parameter combination separately (Supplementary Table  1 ). For each software, scores used in the final benchmark (Fig. 1 ) are those from the parameter combination that yielded the highest F1 score. For the performance benchmark, HiCCUPS and HOMER were excluded. The former because it runs on GPU, and the latter because it uses genomic alignments as input and is much slower. The dataset used is a published high coverage Hi-C library  36  from human lymphoblastoid cell lines (GM12878). To compare RAM usage across programs, this dataset was subsampled at 10%, 20%, 30%, 40% and 50% contacts and the maximum scanning distance was set to 2 Mbp.
To compare CPU time, all programs were run on the full dataset, at different maximum scanning distances, with a minimum scanning distance of 0 and all other parameters left to default. All programs were run on a single thread, on a Intel(R) Core(TM) i7-8700K CPU at 3. 70 GHz with 32 GB of available RAM. Software versions used in the benchmark are Chromosight v0. 9. 0, hicexplorer v3. 3. 1, cooltools v0. 2. 0, homer 4. 10 and hiccups 1. 6. 2. Input data, scripts and results of benchmarks are available on Zenodo (https://doi. org/10. 5281/ zenodo. 3742095) Preprocessing of Hi-C matrices. Chromosight accepts input Hi-C data in cool format  53. Prior to detection, Chromosight balances the whole-genome matrix using the ICE algorithm  31  to account for Hi-C associated biases. For each intrachromosomal matrix, the observed/expected contact ratios are then computed by dividing each pixel by the mean of its diagonal.
The remaining candidates in IMG CORR are scanned by decreasing order of magnitude: every time a candidate is appended to the list of selected local maxima, all its neighbouring candidates are discarded. The proportion of empty pixels allowed and the minimum separation between two patterns are also user defined parameters. Biological analyses. Pairs of reads were aligned independently using Bowtie2 (v2. 3. 4. 1) with --very-sensitive-local against the S. cerevisiae SC288 reference genome (GCF000146045. 2). Uncuts, loops and religation events were filtered as described in ref. 55. Contact data were binned at 2 kb and normalised using the ICE balancing method  31. Hi-C matrices were generated from fastq files using hicstuff v2. 3. 0  56. Detection for biological analyses of yeast and human data was performed with default parameters using a 7 × 7 loop kernel available in Chromosight using --pattern loops_small unless mentioned otherwise.
For enrichment analysis, cohesin peaks were defined using ChIP-seq data from  57. Raw reads were aligned with bowtie2 and only mapped positions with Mapping Quality superior to 30 were kept and signals were also binned at 2 kb to synchronise with Hi-C data. Peaks of cohesins were considered with ChIP/input > 1. 5 and peaks closer than 10 kb to centromeres or rDNA were removed. Annotation of highly expressed genes was done using RNA-seq data from  8. Alignment was done as above. The distribution of the number of reads for each 2 kb bin was computed and the top 20% of the distribution were considered bins with high transcription. For border annotation, a set of plus or minus 1 bin on the detected positions is used. For human data, hg19 genome assembly was used with same strategy for alignment, construction and normalisation of contact data.
ChIPseq peaks were retrieved from UCSC database (Supplementary Table  2 ). B. subtilis data were aligned with the PY79 genome version and the SMC signal was extracted using ChIP-chip data from  58  and processed as described previously  10, 59. Peaks were annotated with the find_peaks function from scipy (v1. 4. 1), with parameters threshold = 0. 1, width = 50. ChIA-PET data were processed as Hi-C data except that the contact maps were binned at a 500bp resolution. Epstein-Barr virus (EBV) genome, strain B95-8 (V01555. 2) sequence was used to align the reads from EBV. For the detection in the different proximity ligation protocols, we retrieved publicly available data sets from the 4D Nucleome Data Portal  41  , and applied loops detection in the resulting contact maps of the mcool files at 10 kb resolution with the default settings by possibly changing one option that is indicated in (Fig.
Acknowledgements This work was initiated during a  Hackathon between Institut Pasteur scientists  and ENGIE engineers. We would like to thank all the people that allow the organisation of this event especially  Anne-Gaelle Coutris ,  Romain Tchertchian  and  Olivier Gascuel. Julien Mozziconacci ,  Frédéric Beckouët  and all the members of Spatial Regulation of Genomes unit are thanked for stimulating discussions and feedback. This work used the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris. C. M. -D. was supported by the  Pasteur-Paris University (PPU)   International PhD Program. A. B. works within the framework of a "Mécénat Compétence" contract of the company ENGIE. V. S. is the recipient of a  Roux-Cantarini Pasteur fellowship.
Data Collection and Analysis Table  3  summarizes the data collection methods for gathering participants' feedback for each tested PEGASO F4F system component. A mixed method co-design approach was chosen to collect as much feedback in breadth and depth as possible to inform the development of the PEGASO F4F system. In addition, 2 experienced researchers were present during all the workshop and focus group sessions. One researcher was moderating the session and another was allocated to make notes, keep track of the time, and make observations. Although the system components were only available in English, all workshop activities and questionnaires were conducted in the local language.
Focus Groups Focus groups were conducted at all 4 test sites in the local languages and at school and were facilitated by local postdoctoral researchers with a background in behavioral sciences or human-computer interaction (HCI). Facilitators were both male and female in Scotland, Spain, and Italy and all female in England and were not directly involved in the development of the PEGASO F4F software. The focus group topic guides for all 3 iterations can be found in Multimedia Appendix 2 (English-language version only). Focus groups lasted between 60 and 90 min in each iteration, and they were voice or video recorded, transcribed, and thematically analyzed at each study site. Weight of opinion (in terms of number of participants agreeing) was used to make decisions regarding the design, look, and feel or the apps and serious game.
Questionnaires For the development of the Companion app, eDiary app, and serious game, participants were asked to complete 5 questionnaires over the course of the 3 iterative circles (Table  3 ). Participants were asked to complete the System Usability Scale (SUS  [30] ) to determine participants' subjective rating of the usability of the apps and the serious game. The SUS index was calculated using the standard conversion method  [30, 31] , with scores above 70 indicating acceptable usability, scores between 50 and 70 suggesting marginal usability, and scores below 50 reflecting poor or unacceptable usability  [31]. The questionnaire data were entered in to Microsoft Excel and IBM SPSS software (version 21), descriptive summary statistics calculated. The Brief Use of App Questionnaire (Multimedia Appendix 3) was developed by the research team and included closed and open-ended questions.
Principal Findings This study aimed to describe the process of and findings from co-designing and prototyping components of a mobile phone-based technological system for promoting a healthy diet, physical activity, and reducing sedentary behavior in adolescents. Findings of this study suggested that for the PEGASO F4F technology to be motivating and engaging to adolescents, it should (1) allow personalization of the interface, (2) have age-appropriate and easy-to-understand language (of icons, labels, instructions, and notifications), (3) provide easily accessible tutorials on how to use the app or navigate through a game, (4) present a clear purpose and end goal, (  5 ) have an appealing and self-explanatory reward systems, and (6) offer variation in gamified activities within apps and the serious game.
As part of a multidisciplinary interview team, they would have a good understanding of game dynamics and ask appropriate follow-up questions that could be valuable for improved game design and possibly effectiveness. This would be in line with previous research results indicating that gathering feedback from end users should prioritize game dynamics (eg, level) above game aesthetics (eg, storyline and character) as this seems more strongly related to game effectiveness  [40]. Involving game and app developers in the focus groups across the 3 countries and 4 intervention sites was considered for this study, but it was deemed not feasible because of constraints of time, proficiencies in local languages (ie, Catalan), and financial resources. Another limitation of this study was that all PEGASO F4F prototypes were available in English language only.
Although none of the adolescents approached to participate in this study (entire year groups) was excluded because of insufficient English language proficiency, participants in Spain and Italy stressed the importance of having apps available in local languages. The translation of the apps into Italian, Spanish, and Catalan was anticipated from the onset of the PEGASO F4F project and was done for testing the technology in the subsequent controlled pilot study. The lack of multilanguage prototypes might have impacted on participants' engagement with the technology. However, the design workshops and focus groups were conducted in the local language, allowing participants to express their creativity, ideas, and preferences for the development of the technology. Translation and back-translation of the apps and serious game content was not feasible during the technology development process because of the tight project timeline and constraints in the budget.
Acknowledgments The authors thank all participants for their valuable feedback, creativity, and time. They are grateful for the support received by the participating schools. This project was funded by research grants from the  European Commission  under the  Seventh Framework Programme  (call identifier:  FP7-ICT-2013-10 ; project number:  610727 ). AM was supported by the  UK Medical Research Council  (grant number  MC_UU_12017/14 ) and the  Scottish Government Chief Scientist Office  (grant number  SPHSU14 ). The funders had no involvement in review and approval of the manuscript for publication.