dataesr/scientific-paragraphs-categorization huggingface dataset:
| Estimation of the protein surface involved in functional interactions We used both experimental interfaces and predicted patches to estimate the proportion of protein surface involved in functional interactions. On average, experimental ISs from PPI-262 cover 30% of the protein surface (Figure  3A ). Hence, by looking at this dataset, one may infer that the residues involved in functional interactions generally represent less than a third of the protein surface. However, when looking at PPI-262 ext (Figure  3B ), which comprises experimental IRs defined from close homologs, the coverage increases up to 50%. Moreover, a significant number of proteins (32) have their surface completely or almost completely covered by functional interactions (coverage ≥80%). This suggests that most of the proteins from P-262 engage in multiple interactions with different partners.
Datasets 2. 1. 1 | Proteins: P-262 We defined a dataset of 262 protein chains and associated Protein Data Bank (PDB) structures featuring both single and multiple partners interactions. This dataset was extracted from a larger set of 2246 protein chains defined in the scope of the HCMD2 project (see http://www. ihes. fr/ carbone/HCMDproject. htm), for which we performed CC-D. We considered the subset of PDB structures comprising a protein complex previously reported in Reference 45, from which we excluded: (a) only C-α structures, (b) chains for which docking results were missing, (c) chains forming coiled-coils complexes, (d) deprecated PDB codes, (e) chains for which no biologically relevant interface (see subsection Surfaces and interfaces for definition) could be found in the whole PDB (considering 90% sequence identity, see below).
61 This amounted to 23 642 ISs, which were merged into 370 IRs to define our second "extended" dataset, PPI-262 ext. The two examples in Figure 2B illustrate the complexity of the experimental interaction surfaces in our datasets. Binding sites may be disjoint, overlapping or included in others (Figure 2B, on the left), and they may be defined by the interaction with another copy of the same protein, other proteins or peptides (Figure 2B, on the right). The two examples show five ISs (three on the left and two on the right), which were merged into three distinguished IRs (two on the left and one on the right, contoured by thick.
24 and   it increases up to 0. 57 ± 0. 19 on PPI-262 ext (Figure4A). This increase reflects a global shift of the F1-score distribution toward higher values (P-value = 10 -4 with the Mann-Whitney U test). The proportion of proteins with very good predictions (F1-score > 0. 6) increases from 18% to 46%, while that of proteins with very poor predictions (F1-score < 0. 2) drastically reduces from about one quarter to 4%. These results highlight the importance of considering all available experimental information to properly evaluate protein interface predictions. Predicted residues that would be considered as false positives when looking at the restricted dataset, PPI-262, are actually involved in interactions with other partners, as revealed by the extended dataset, PPI-262 ext. We further investigated to what extent the partitioning of protein surfaces into patches predicted by the different SCs matches experimental IRs (Figure 4B,C,D).
to the HPC resources of the Institute for Scientific Computing and Simulation (Equip@Meso project -ANR-10-EQPX-29-01, Excellence Program "Investissement d'Avenir"); the World Community Grid (WCG, www. worldcommunitygrid. org) and WCG volunteers that allowed us to perform cross-docking experiments with MAXDo on the HCMD2 dataset; Alexey Porollo for handling his script to submit large scale projects to SPPIDER's webserver.
2. 1 | 1 Datasets 2. 1. 1 | Proteins: P-262 We defined a dataset of 262 protein chains and associated Protein Data Bank (PDB) structures featuring both single and multiple partners interactions. This dataset was extracted from a larger set of 2246 protein chains defined in the scope of the HCMD2 project (see http://www. ihes. fr/ carbone/HCMDproject. htm), for which we performed CC-D. We considered the subset of PDB structures comprising a protein complex previously reported in Reference 45, from which we excluded: (a) only C-α structures, (b) chains for which docking results were missing, (c) chains forming coiled-coils complexes, (d) deprecated PDB codes, (e) chains for which no biologically relevant interface (see subsection Surfaces and interfaces for definition) could be found in the whole PDB (considering 90% sequence identity, see below).
France; the access to the HPC resources of the Institute for Scientific Computing and Simulation (Equip@Meso project -ANR-10-EQPX-29-01, Excellence Program "Investissement d'Avenir"); the World Community Grid (WCG, www. worldcommunitygrid. org) and WCG volunteers that allowed us to perform cross-docking experiments with MAXDo on the HCMD2 dataset; Alexey Porollo for handling his script to submit large scale projects to SPPIDER's webserver.
Brief descriptions of the DL1-Data-Handler, GammaLearn, and CTLearn packages follow below. DL1-Data-Handler The datasets used for training models in this work are stored in a custom HDF5-based file format defined by the DL1-Data-Handler (DL1DH) package  [17]. This format, built on the PyTables library, is database-like in structure, consisting of tables of structured data which include information about telescope properties, the IACT array configuration, and the complete list of simulated events and their properties, as well as the images from the detected showers. A system of index-based mapping allows easy lookup of the variable number of telescope images associated with each shower event (and vice versa), while maintaining the efficient storage properties of the table-based PyTables HDF5 format.
Large datasets of IACT data simulated with CORSIKA and sim_telarray  [18]  can be processed efficiently into this HDF5 format using the DL1DataWriter module from DL1DH. DL1DataWriter is built upon ctapipe  [19]  data containers 2 and is designed to flexibly support a variety of other input file formats besides the ones described above, allowing the possibility of processing IACT data from sources other than sim_telarray. DL1 Data Writer implements several PyTables HDF5 storage optimizations, including indexing for faster lookup and chunking/compression to minimize file sizes. DL1DH offers the DL1DataReader module to read the HDF5 files it produces. Internally, DL1DataReader can either map the camera images from the 1D arrays contained in the HDF5 files to 2D NumPy arrays, implementing all the mapping methods described in Sec.
2. 1, or calculate the pixel neighbor matrix needed for indexed convolution, described in Sec. 2. 2. Additionally, DL1DataReader implements efficient selection cuts on data and supports arbitrary event and 1 Area under the receiver-operating-characteristic curve. 2 The calibration of the raw sim_telarray data is performed using the available ctapipe calibration tools. image filtering and user-defined transformations. Data loading and pre-processing for both Gam-maLearn and CTLearn are performed using DL1DataReader. GammaLearn GammaLearn is a high-level Python package providing a framework to apply deep learning methods to IACT data using PyTorch. In particular, it solves some of the main challenges scientists are facing when applying DCNs to the reconstruction of IACT data: the application of convolutions on hexagonal images, the combination of stereoscopic images, and the reproducibility of the experiments via configuration files.
IndexedConv is fully integrated in the GammaLearn framework. A more detailed description of this package can be found in  [20, 21]. CTLearn CTLearn is a high-level Python package providing a backend for training deep learning models for IACT event reconstruction using TensorFlow. CTLearn allows its user to focus on developing and applying new models while making use of functionality specifically designed for IACT event reconstruction. CTLearn offers reproducible training and prediction, ensuring that settings used to train a model are explicitly and automatically recorded. Further details on CTLearn can be found in  [22, 23]. DCN model We decided to work with simple models of proven particle classification capabilities, taking as a reference the single-tel model in CTLearn for the image mapping strategy and a slightly modified version of this model for the indexed convolution strategy.
Dataset The dataset that was utilized in this work is made of Monte Carlo simulated events for the Cherenkov Telescope Array (CTA)  3. CTA is the next generation ground-based observatory for gamma-ray astronomy at very high energies, aiming to improve on the sensitivity of currentgeneration experiments by an order of magnitude and provide energy coverage from 20 GeV to more than 300 TeV. CTA will access the full sky thanks to an installation in the Northern Hemisphere (La Palma, Spain) and another installation in the Southern Hemisphere (Cerro Paranal, Chile), featuring more than 100 IACTs in total. Events from the third large-scale Monte Carlo production  [24]  were reduced, from raw to calibrated images, on the EGI  4   Writer in DL1DH. We selected simulated data for the Southern installation, with a Zenith angle of 20 • and an Azimuth angle of 0 • (North pointing), and the "S8" layout (according to the notation in  [24] ).
Such layout consists of four large-size telescopes (LSTs), 25 medium-size telescopes (MSTs), and 70 small-size telescopes (SSTs). Three models of MST and three models of SST were originally simulated, but we restricted ourselves to the single-mirror MST featuring FlashCam as its camera (MST-F) and the single-mirror SST-1M respectively. The dataset contains both diffuse gamma-ray and proton-initiated showers with balanced statistics, accounting for nearly 400 thousand events (1. 4 million images, since most events trigger more than one telescope). The selected events were randomly drawn from the source dataset and then split following a 8/2 ratio into a train dataset and a test dataset.
Results We trained the models in Sec. 3 with batches of 64 individual images (samples) randomly drawn from the training dataset described in Sec. 4, and stopped after 50,000 batches were seen by the model. We trained on each type of telescope independently. The training was monitored by periodically validating the evolution of the loss function, and the accuracy and AUC metrics. The samples used for the validation were randomly drawn from the training dataset (amounting to 10% of it) and never used during actual training. Image mapping methods were explored with CTLearn, while the experiments with indexed convolution were conducted with GammaLearn. Fig. 3  (left panel) exemplifies the evolution of the AUC during training for the three types of telescopes and all the tested methods. We performed independent training runs with different random seeds for each explored method (10 runs for CTLearn, 5 runs for GammaLearn) to assess if the differences in performance between methods were due to stochastic fluctuations alone.
Conclusion and Outlook The standard deviation of the distribution of learning metrics obtained from all the training ranges from 0. 2% to 0. 5% in the validation set, almost identical in the test set, without noticeable distinction between methods or telescope types. The values for accuracy and AUC from the validation and test datasets are compatible within errors for all methods and telescope types. Bilinear interpolation is consistently superior, although not significantly better than most of the other methods, being consistent with indexed convolution, oversampling, rebinning and bicubic interpolation within errors. Nearest interpolation consistently underperforms and there are strong hints that this method could be significantly worse that the rest. Future studies should test whether these results generalize to further aspects of IACT event reconstruction, like the estimation of the energy and arrival direction of the simulated events.
Acknowledgments This work was conducted in the context of the CTA Analysis and Simulations Working Group. We thank  Johan Bregeon  for his support reducing our dataset on the EGI. DN and TM acknowledge support from the  former Spanish Ministry of Economy, Industry, and Competitiveness / European Regional Development Fund  grant  FPA2015-73913-JIN. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements  653477  and  824064. This work has been done thanks to the facilities offered by the  Univ. Savoie.
Transcription factors (TFs) are sequence-specific DNA binding proteins, fine-tuning spatiotemporal gene expression. Since genomic occupancy of a TF is highly dynamic, it is crucial to study TF binding sites (TFBSs) in a cell-specific context. To date, thousands of ChIP-seq datasets have portrayed the genomic binding landscapes of numerous TFs in different cell types. Although these datasets can be browsed via several platforms, tools that can operate on that data flow are still lacking. Here, we introduce TFregulomeR (https://github. com/benoukraflab/TFregulomeR), an R-library linked to an up-to-date compendium of cistrome and methylome datasets, implemented with functionalities that facilitate integrative analyses. In particular, TFregulomeR enables the characterization of TF binding partners and cell-specific TFBSs, along with the study of TF's functions in the context of different partnerships and DNA methylation levels.
We demonstrated that TFs' target gene ontologies can differ notably depending on their partners and, by re-analyzing well characterized TFs, we brought to light that numerous leucine zipper TFBSs derived from ChIP-seq experiments documented in current databases were inadequately characterized, due to the fact that their position weight matrices were assembled using a mixture of homodimer and heterodimer binding sites. Altogether, analyses of context-specific transcription regulation with TFregulomeR foster our understanding of regulatory network-dependent TF functions.
Integrated with DNA methylation profiles, our recently launched cell-specific TF-binding profile database, Meth-Motif, has clearly showed that cell context influences TFBS at both sequence and epigenetic levels  (19). DNA methylation information is therefore an additional layer to take into consideration for a comprehensive TF binding analysis, in order to delineate precise DNA sequence affinity and uncover TF function in a specific cell context. Nevertheless, although numerous cistrome databases and tools facilitating the prediction of CRMs and cofactors of a TF of interest are available  (20) (21) (22) , an environment connecting a large compendium of TF binding sites and respective DNA methylation profiles is still lacking. To fill this gap, we have established a large collection of standard ChIP-seq and Whole Genome Bisulfite Sequencing (WGBS) datasets derived from the GTRD repository  (23)  and MethMotif database  (19).
This information can be browsed, analyzed and compared to other data sources via TFregulomeR, an R package that contains processing and visualization functions. To our knowledge, TFregulomeR is the first platform enabling the query and the analysis of context-specific TF modules according to cell type, tissue origin and disease state. This is also the first computational tool allowing a standard integration of TF module binding sites with their respective DNA methylation profiles (Supplementary Table  S1 ). In essence, TFregulomeR facilitates the analysis of context-specific transcription regulation and fosters our understanding of regulatory network-dependent TF functions.
Cistrome and methylome data in TFregulomeR TF motif position weight matrices (PWM) recorded in the TFregulomeR compendium are compiled from the Meth-Motif database  (19)  and GTRD  (23). MethMotif includes a comprehensive collection of TF motifs complemented with methylation information. Briefly, relying on the Irreproducibility Discovery Rate (IDR)  (24) , ChIP-seq peaks were standardly called using MACS2  (25) , followed by a motif de novo enrichment analysis with MEME-ChIP  (11) , focusing on regions defined by a ±100 bp window around peak summits. Subsequently, DNA methylation profiles were inferred using whole genome bisulfite sequencing (WGBS) datasets in all peak regions and predicted TFBSs. GTRD is a large collection of publicly available ChIPseq experiments which serves as an important complementary source to enlarge the PWM pool in TFregulomeR.
ChIP-seq peaks identified by MACS peak caller from the untreated experiments were downloaded from GTRD database. Following the same pipeline in the MethMotif, centrally enriched TF motifs were localized using MEME-ChIP with the default settings in a 200 bp range surrounding peak centres. In TFregulomeR compendium, highly and centrally enriched motifs were selected and compared with the existing TF-binding profile databases, such as JASPAR  (26)  and HOCOMOCO  (27). In some ChIP-seq, the significantly enriched motifs did not correspond to the motifs of ChIP'ed TFs. Among 1468 PWM records in TFregulomeR, we identified 91 motifs different from those reported in TFbinding profile databases. Furthermore, 136 motif matrices were not recorded for their corresponding TFs in these databases. In order to verify that these 227 motifs do not derive from the use of a specific motif discovery algorithm, we used another motif discovery tool, HOMER  (28) , based on different algorithm hypergeometric enrichment, to perform an additional de novo motif discovery.
The motifs obtained with HOMER were compared with those obtained with MEME-ChIP, and their similarities were measured by normalized Pearson correlation coefficient using the comparematrices function in RSAT  (29)  with the formula: Ncor = cor * w / w smaller, where cor is raw Pearson correlation coefficient, w is the alignment width of two matrices from MEME-ChIP and HOMER (the minimum value of w was set as 5), and w smaller is the width of smaller motifs from MEME-ChIP and HOMER. Each PWM has its own unique ID, which keeps track of the source, species, cell type and TF (e. g. 'MM1 HSA K562 CEBPB' and 'GTRD-EXP010975 HSA Ishikawa CEBPB'). Since TF binding preferences are obviously dependent on the context, all obtained TFBS datasets have been thoroughly annotated in terms of species, organ, sample type, cell or tissue origin, disease state and experiment source.
TFregulomeR functionalities TFregulomeR provides functionalities to ease data access, retrieval, integration and analysis. These functionalities en-able browsing of curated datasets in TFregulomeR data compendium (dataBrowser), plotting motif logo (or Meth-Motif logo if DNA methylation is available, plotLogo), loading peak regions (loadPeaks), exporting motif PWM and DNA methylation matrix (exportMMPFM), obtaining context in/dependent peaks (commonPeaks and exclu-sivePeaks), forming a peak intersection matrix for cofactor and TF interaction studies (intersectPeakMatrix), generating a cofactor report automatically along with DNA methylation and read enrichment scores (cofactorReport), profiling TFBS distribution (motifDistrib), annotating peak locations and functions (genomeAnnotate and greatAnnotate), and converting the PWM in TFregulomeR into an R object compatible with TFBSTools (toTFBSTools)  (30).
Common and exclusive peak analysis TFregulomeR provides functionalities to perform context in/dependent peak analysis. The peak sets can be directly derived from the TFregulomeR compendium or customized by users. The context in/dependent peak regions are returned, together with de novo generated Motif logos (Meth-Motif logos if DNA methylation is available) and DNA methylation states. Particularly, the logo-plotting function in TFregulomeR uses the package ggseqlogo  (31) , to generate high quality (Meth)Motif logos in vector format.
TFregulomeR compendium maintenance MethMotif and GTRD are the main sources for the cistrome and methylome data in TFregulomeR compendium. Since its launch, MethMotif database has been updated actively, while GTRD has been also regularly maintained. In the future, TF motif records along with DNA methylation in TFregulomeR will be updated accordingly to offer up-to-date PWM collections for the research community.
TFregulomeR, a comprehensive toolbox to study TF binding dynamics The study of TF context-specific binding preferences heavily relies on comprehensive knowledge on TF cell-specific binding sites and chromatin status  (1, 19, 33). We thus compiled this information by integrating PWM collections derived from GTRD, a database encompassing publicly available ChIP-seq data  (23) , with the resources from our Meth-Motif database, which couples TFBSs with DNA methylation status (19) (Figure  1A ). To date, our compendium includes 1468 PWMs and, in contrast to current available resources, we manually curated our datasets in order to match cistrome with methylome data in a cell type-specific fashion, and organized them according to cell type, tissue of origin, disease state and experiment source (Table  1  and Supplementary Table  S1 ).
It is important to note that, in some ChIP-seq datasets, the highly enriched motif (labeled as binding motif of the TF of interest in TFregulomeR compendium) does not necessarily resemble the canonical motif of the ChIP'ed TF. This is presumably due to the fact that in the given cell type, the ChIP'ed TF is mostly recruited by other TFs instead of directly binding to DNA, and/or that the high presence of    (34). In TFregulomeR compendium, we identified 91 PWMs different from TFbinding profile databases, such as JASPAR  (26)  and HOCO MOCO  (27). In order to verify that divergent motifs do not derive from the use of a specific motif discovery algorithm (in our case MEME-ChIP based on an expectation maximization approach), we used another algorithm, HOMER (based on hypergeometric enrichment), to perform an additional de novo motif discovery.
Motif consistency details have been compiled into the TFregulomeR compendium, and divergent PWMs were flagged to allow users to interpret their results with caution. TFregulomeR compendium is accessible via an effective R-package application program interface (API), called TFregulomeR. This API offers the required functions for easy data access, analysis and integration in pipelines. TFregulomeR facilitates (i) the recognition of context in/dependent TF binding locations (cistrome); (ii) the identification of context-specific TF-interacting partners (interactome); and (iii) the characterization of functionally active cis-regulatory modules (CRM) (Figure  1B ). As demonstrated in the next sections, TFBS context-comparative analysis enables the identification of novel TFBS features, in particular the characterization of co-factors and their influence on TF functions.
TFregulomeR also includes generic functionalities such as TFBS genomic annotation, which can be used to locate the genomic binding site landscapes of a TF of interest depending on cell type and/or partner combination (Figure  1C  right). Interestingly, algorithms implemented in our toolbox allow the detection and segregation of multiple cofactor-derived motifs present in the PWM directly computed from whole genome context (Figure  1C , middle, cf. detailed hereafter). In contrast to all available tools  (20) (21) (22) , by integrating DNA methylome datasets, TFregulomeR uniquely emphasizes the impact of DNA modifications in TF-module recruitment and reveals how DNA methylation can affect TF functions (Figure  1C , left and Supplementary Table  S1 ). At last, objects generated by the TFregulomeR package are compatible with thirdparty packages such as TFBSTools, which provides means for TFBS matrix handling and motif scanning  (30) , and rGREAT, for ontology analysis  (32).