dataesr/scientific-paragraphs-categorization huggingface dataset:
With this work, we pave the way to the discovery of yet unknown binding sites, opening up new perspectives for drug design. We provide the structures and the experimentally known DNA-binding sites for our two newly created datasets, HR-PDNA187 and HOLO-APO82 at: http://www. lcqb. upmc. fr/ PDNAbenchmarks/. These datasets represent the largest body of non-redundant known highresolution crystallographic protein-DNA complex structures. They were manually curated to ensure quality and biological relevance. They can be used as benchmark sets for evaluating DNA-binding site predictors and DNA-protein docking methods. The code of JET 2 DNA is available at: http://www. lcqb. upmc. fr/JET2DNA.
Datasets HR-PDNA187. The complete list of 1257 protein-double strand DNA complexes determined by X-ray crystallography with a resolution better than 2. 5Å was downloaded from the Nucleic Acid Database  [38]  (February 2016 release). This list was filtered using PISCES sequence culling server  [39]  to define a set of 222 protein-DNA complexes non-redundant at 25% sequence identity, with an R-factor lower than 0. 3 and with at least one protein chain longer than 40 amino acids. The complexes' 3D structures were downloaded from the PDB  [4]. Subsequently, the dataset was manually curated to ensure its good quality. We removed entries where: (1) the asymmetric unit did not contain at least one biological unit or (2) the DNA molecule was single-stranded or contained less than 5 base pairs or (3) only Cα atoms were present.
Moreover, only chains with more than one contact with the DNA were retained. When the biological unit contained multiple copies of the protein-DNA complex, only one copy was kept. The structure 4hc9 was excluded because it displays different DNA-binding sites in the asymmetric and biological units. The complex 4aik was substituted with the 100% homolog 4aij, where the DNA-binding site is twice bigger. Finally, we removed the only antibody present in the dataset (3vw3), since this class of proteins have very peculiar characteristics and should be treated separately. In total, we retained 187 complexes (Table A in S1 File). HR-PDNA187 covers all major groups of DNA-protein interactions according to Luscombe et al. classification  [1] : helix-turn-helix (HTH), zinc-coordinating, zipper type, other α-helical, β-sheet, β-hairpin/ribbon, other.
It spans a wide range of functional classes: it comprises 100 enzymes, 78 regulatory proteins, 7 structural proteins, 1 protein with other function and 1 unclassified protein (Table A in S1 File). HOLO-APO82. We collected all available X-ray structures of the APO forms of the proteins from HR-PDNA187. We used the blastp program from the BLAST+ package  [40]  from NCBI with a threshold of 95% for seq. id. , 10 -3 for the E-value, a percentage of coverage � 70% and a percentage of gaps � 10% with respect to the query protein chain. Among the structures matching these criteria, only the ones having the same UniProt code  [41]  or belonging to the same organism as the query sequence were retained. If several structures passed all filters, only the closest one to the query or the one with the highest resolution was chosen.
We found unbound forms for 81 complexes. For the complex 2isz, the unbound protein was found as a monomer and also as a dimer in two different PDB structures. Both structures were retained as they were both reported as present in equilibrium  [42]. The resulting list comprises a total of 82 HOLO(bound)-APO(unbound) pairs. Within each pair, the APO form may be in the same oligomeric state as the HOLO form or may have fewer chains (Table A in S1 File). TEST-NABP82. We used an additional recent dataset  [18]  of 82 proteins bound to nucleic acids, with a resolution better than 2. 5Å. Among these, 49 proteins were solved bound to DNA (TEST-DBP49) and 33 to RNA (TEST-RBP33). Among the 49 DNA-binding proteins, 25 share more than 30% sequence identity with proteins from HR-PDNA187. To fairly assess JET 2  DNA performance, they were removed from the dataset and the remaining 24 were used as non-redundant test set of DNA-binding proteins (TEST-DBP24).
Definition of interface residues For each bound form from HR-PDNA187, we calculated the residues relative accessible surface areas in presence (rasa DNA ) and absence (rasa free ) of DNA, using NACCESS 2. 1. 1  [43]  with a probe size of 1. 4Å. Interface residues were defined as those being more buried in the presence of DNA than in the free form (Δrasa > 0). They were classified in three structural components  [34] : support residues are buried both in presence (rasa DNA <25%) and absence of DNA (rasa free <25%); core residues are exposed in absence of DNA (rasa free �25%) and become buried upon binding (rasa DNA <25%); rim residues are exposed in presence (rasa DNA �25%) and in absence (rasa free �25%) of DNA (Fig  1a ). The interfaces for TEST-NABP82 were directly taken from  [18]. They were defined from the PDB entries comprised in the dataset and also PDB structures of identical or similar complexes (seq.
id. �80% and TM-score�0. 5) [44, 45]. This allows to account for interface variability coming from molecular flexibility. Nevertheless, we should stress that these interfaces were defined using a very stringent distance criterion of 3. 5Å, and hence they are substantially smaller than those we defined from HR-PDNA187. We used them to fairly assess JET 2 DNA performance on an independent dataset, not created by us, and to fairly compare JET 2 DNA with DRNApred  [18] , which was evaluated on them.
Support-Core-Rim vs Core-Support-Rim model for protein-DNA interactions To identify characteristic features of protein-DNA interfaces, we analysed the non-redundant set of protein structures bound to DNA available in the PDB. This dataset, HR-PDNA187, comprises 187 high-resolution crystallographic structures, covers all major types of DNAinteractions and spans a wide range of protein functions (see Materials and methods). Interface residues were classified based on the support-core-rim model (Fig  1a , on top) proposed for protein-protein interfaces  [34]. The support (in yellow) comprises residues buried both in presence and absence of DNA, the core (in brown) comprises residues exposed in absence of DNA and becoming buried upon binding, and the rim (in green) comprises residues exposed both in presence and absence of DNA (see Materials and methods).
Discovery of alternative DNA-binding sites The complete and/or automated mode(s) of JET 2  DNA produce patches that do not match the experimental interfaces present in our datasets (Table J in S1 File). Due to the relatively small number of protein-DNA complexes available in the PDB, it is difficult to systematically assess the pertinence of these additional predictions. Nevertheless, we could identify several cases where the protein binds to DNA via two completely distinct binding sites. We report four such cases in the following (  These examples illustrate the different usages of JET 2 DNA multiple scoring schemes and how one can learn about each DNA-binding site properties from JET 2 DNA predictions. Specifically, different D-SC may target different binding sites (recognition or catalytic), or different regions of the same binding site (conserved core or protruding rim), or even yield overlapping predictions.
Comparison with other tools We compared JET 2 DNA with three other state-of-the-art machine learning based predictors, namely DISPLAR  [7] , multiVORFFIP  [15]  and DRNApred  [18]  (see Materials and methods). iJET 2  DNA achieves �20% higher sensitivity and similar accuracy compared to DISPLAR and multiVORFFIP on all the datasets (Table  F   Fig 4). We should stress that DRNApred was designed to specifically discriminate DNA-from RNAbinding residues, which is not the purpose of JET 2 DNA. In particular, JET 2 DNA performs much better than the three other tools on the RNA-binding proteins of TEST-RBP33 (Table  I in.
Comparison with other benchmarks We compared HR-PDNA187 and HOLO-APO82 with the most popular benchmarks, namely PDNA62  [6, 64] , DBP374  [13] , MetaDBsite316  [55] , Displar264  [7] , PDDB1. 2  [65] , DBP206  [66] , PDNA224  [10]  and DNABINDPROT54  [24]. Contrary to our datasets, most of them comprise only single chains, even if the functional biological unit of the protein in complex with DNA is annotated as a multimer. Moreover, when applying to them the PISCES criteria used to construct HR-PDNA187, the number of resulting complexes was systematically smaller than 187 (Table L in S1 File). Finally, very few of them provide the APO forms of the proteins.
Discussion We have collected and carefully curated 187 high resolution protein-DNA complexes representative of all known types of protein-DNA interactions. This new dataset, supplemented by the 82 available protein unbound conformations, could serve as a reference benchmark for the community. Based on the analysis of the evolutionary and structural properties of the DNAbinding sites from this dataset, we have proposed an original method to predict them. Importantly, we do not estimate interacting probabilities for individual residues, but we predict ensembles of residues proximal in 3D space, in other words "patches". Predictions based on patches are justified by the fact that residues being conserved or displaying specific physicochemical properties at protein-DNA interfaces tend to cluster together  [58, 67, 68].
Moreover, we propose a "discretized model" of interfaces describing the role of each predicted residue in the interface. This model is inspired by the support-core-rim model defined for protein-protein interfaces. We have shown that it is also useful for protein-DNA interfaces, where the support and the core may switch their positions. We have defined three archetypal protein-DNA interfaces, namely conserved generic, conserved enveloping and not conserved, and have devised three scoring schemes to detect them. We have thoroughly assessed our method's performance on bound and unbound protein forms and on an independent dataset. JET 2 DNA compares favorably with machine learning based state-of-the-art methods. Specifically, the predictions are more sensitive and remarkably robust to extensive conformational changes and to stochiometry changes.
and Table I in S1 File andFig 4). The predictions are slightly less precise (by �5-10%) than those of multiVORFFIP on HR-PDNA187 �� and APO82, but equally or more precise than DISPLAR and multiVORFFIP on the other datasets (TableFand Table I in S1 File and Fig 4). This results in �5-10% higher F1 values and most of the times �5-10% higher MCC values compared to both tools (Table F and Table I in S1 File and Fig 4). Compared to DRNApred, iJET 2 DNA 's detection is at least twice more sensitive and at least �10% more precise on all the datasets (Table F and Table I in S1 File and.
This work was funded by  LabEx CALSIMLAB  (public grant  ANR-11-LABX-0037-01  constituting a part of the  "Investissements d'Avenir" program  -reference:  ANR-11-IDEX-0004-02 ) (FC) and the  Institut Universitaire de France (AC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Contributions: We propose a novel approach for efficiently detecting interactions between complementary datasets in a high-dimensional setting with a reduced computational cost. The method, named SICOMORE, reduces the dimension of the search space by selecting a subset of supervariables in the two complementary datasets. These supervariables are given by a weighted group structure defined on sets of variables at different scales. A Lasso selection is then applied on each type of supervariable to obtain a subset of potential interactions that will be explored via linear model testing.
Contributions and organization of the paper In this work, we propose a method for detecting interactions between genomic and metagenomic data. The method comprises four steps. Given a dataset: (1) Identify a group structure within the variables using a hierarchical clustering; (2) Create compressed features, or supervariables, according to this group structure; (3) Select a subset of supervariables using a Lasso procedure with a penalty factor weighted by the length of the gap between two successive levels of a hierarchical clustering; (4) Combine the two compressed datasets in a linear model with interactions in order to perform multiple hypothesis testing. This scheme allows interactions to be detected efficiently in a high-dimensional setting with a reduced computational cost. The paper is organized as follows.
"Learning interactions with complementary datasets" section looks at the role of linear models of interactions and proposes a framework for learning using complementary datasets. "Method" section describes our method, which seeks to uncover relevant interactions using, first, compressions of data based on hierarchical structures, second, a Lasso selection procedure and, third, model testing. Finally, "Numerical simulations" section provides an illustration of our approach using numerical simulations, and "Application on the rhizosphere bacterial communities of Medicago truncatula" section describes an application for examining interactions between the genomic markers of the species Medicago truncatula and metagenomic markers of its rhizosphere microbial community.
Initial selection of variables As described in "Learning interactions with complementary datasets" section, we make the assumption that interactions have strong dependencies, which means that an interaction can be effective only if the two simple effects associated with the variables in interaction are included in the model. For this reason it may be advantageous to make an initial selection in order to eliminate inoperative single effects on G and M respec- tively. Different approaches for carrying out this selection may be considered. For example, screening rules can eliminate variables that will not contribute to the optimal solution of a sparse problem, sweeping all the variables upstream to the optimization. In cases where this kind of screening is appropriate, the work of Lee et al. [39]  is a useful resource.
Selection of supervariables Compression is a key ingredient in reducing significantly the dimension of Problem (3). We take this a step further with an additional feature selection process applied to the compressed variables, as described at the beginning of this section, in order to preprocess the data using screening rules and/or applying a Lasso optimization on each view G and M: and with penalty factors defined by ρ g = 1/ √ s g and ρ m = 1/ √ s m , as explained in "Structuring the data" section. This step for selecting the supervariables in the two complementary datasets can be subject to instability when setting the amount of selection. The method can be improved further in terms of model consistency by using resampling techniques  [5, 33, 47]. This has been implemented in SICOMORE with the R package stabs  [6].
Generation of metagenomic and genomic data matrices Genomic data. To obtain a matrix X G resembling real genomic data we used HAPGEN2 software  [59, 60] , which can simulate an entire chromosome conditionally on a reference set of population haplotypes (from HapMap3) and an estimate of the fine-scale recombination rate across the region, so that the simulated data share similar patterns with the reference data. We generated chromosome 1 using the haplotype structure of CEU population (Utah residents with Northern and Western European ancestry from the CEPH 3 collection) as the reference set, and we selected D G = 200 variables from this matrix to obtain the simulated dataset. An example of the linkage disequilibrium structure among the simulated SNPs is shown in Fig. 2a. Metagenomic data. The data matrix X M , with D M = 100 variables, was generated using a multivariate Poisson log-normal distribution  [1]  with group structure dependencies.
Comparison of methods In accordance with the outline given in the preamble of Section 4, we were seeking to assess the ability of SIC-OMORE, in comparison with MLGL and glinternet, to uncover true causal interactions. For this purpose, we needed to reshape the datasets provided to the two methods as we now describe below. (5) It is worth mentioning that SICOMORE is an approach that draws on the work of Park et al. [50]  and MLGL  [25] , with an explicit design for detecting interactions. We explore two settings : ρ-SICOMORE and SICOMORE, which correspond respectively to the method described in section 3 using ρ h = 1/ √ s h and ρ h = 1 , ∀h. y i = g∈S G xg i β g + m∈S M xm i β m + g∈S G m∈S M xg i • xm i φ gm i θ gm + ǫ i ,.
Since this method can only work at the level of variables, we needed to include a group structure into the analysis, and so we decided to fit the glinternet model on the compressed variables and to constrain the model to only fit the interaction terms between the supervariables of the two matrices XG and XM. We explicitly removed all interaction terms between supervariables belonging to the same data matrix. To ensure that our comparison of SICOMORE was fair, we considered two options, namely GLtree and GLgap. The GLtree option works on the unweighted compressed representations of the two hierarchies (Fig. 1c ) and thus takes into account all the possible interactions between the supervariables of the two datasets. In contrast, the GLgap option considers only the interactions between the compressed variables constructed at a specific level in the hierarchies, chosen by the gap statistic.
g -  of belowground biomass per day. In addition to the phenotypic measurement, the rhizosphere of each accession was also analyzed to determine the bacterial diversity and composition (see Additional file 1). The metabarcoding raw data is available in the European Nucleotide Archive (ENA) EMBL-EBI database system under project accession PRJEB25849. A total of 15617 different bacterial OTUs were found in the rhizosphere of the plants. The different OTUs were pooled according to their taxonomic affiliation at the genus level, and a total of 329 genera were thus analyzed. The 155 sequenced accessions, extracted from http://www. medic agoha pmap. org, were genotyped with a DNA microarray chip, giving a total of 6 372 968 SNPs after 3% MAF, multiallele SNP exclusion and minimum count (100) filtering.
The missing values were imputed using the snp. imputation function from the R package snpStats  [16]. Given two sets of SNPs typed in the same subjects, this function computes rules that can be used to impute one set from the other in a subsequent sample. By discarding any SNP that had too many missing values to be completely imputed, we reduced the size of the data to 2 148 505 SNPs. The positions of SNPs inside or in the vicinity of genes (± 2Kb) were extracted from context files downloaded from http://www. medic agoha pmap. org. A Singular Enrichment Analysis was conducted using an exact Fisher test with the R package topGO  [3]  and GO term annotation from http://www. medic agoge nome. org. The algorithm requires several hyper-parameters to be chosen in order to run properly: • Aggregating function: For the genomic and the metagenomic data, we defined the mean value of the group as supervariable.
Synthesis The detection of interaction effects in a high-dimensional setting remains a difficult problem because multiple testing is onerous and because effects are small in terms of their significance. In this work, we proposed SICOMORE, a method that reduces the dimension of the search space by selecting a subset of compressed variables obtained from the biological characteristics of complementary datasets. Our approach has demonstrated its ability to uncover interaction effects with a high statistical power. In our simulations, where sample sizes, noise, and the number of true interactions all varied, SICOMORE always exhibited stronger recall than both MLGL and glinternet. SIC-OMORE combines the strengths of different methods in a powerful single algorithm. SICOMORE is also significantly more efficient than the others in terms of computation time.
In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers data for multiple applications, such as modelling colony dynamics, and beekeepers information on colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically detecting cells in comb images and classifying their contents into seven classes. By distinguishing cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented level of accuracy in cell classification.
, 2013). In this context, semi-automatic or automatic methods offer a better alternative for assessing colony strength as subjectivity is eliminated. Herein we developed a free software, DeepBee©, that can be readily employed by honey bee researchers and apiculturists to assess colony strength through analysis of comb images. DeepBee© is capable of automatically detecting cells in comb images and classifying their contents with an unprecedented discriminating power and level of accuracy. DeepBee© evaluates a set of comb images at a high speed, allows edition of the automatic predictions, if needed, and produces a spreadsheet file for downstream analysis. While developing this tool, we further contributed to a wider community including machine learning, image processing and other software developers (i) by providing a method in the segmentation process to automatically readjust the scale of the images driven by the size of the cells, (ii) by testing different neural network architectures related to performance and quality of results, (iii) by providing datasets that can be employed by others when testing new methods, and (iv) by making available the source codes enabling the reproducibility of our results.
Related works Early works developed semi-automatic tools for assessing colony strength by measuring the comb area occupied by brood  (Emsen, 2006; Yoshiyama et al. , 2011). Emsen (2006)  performed the segmentation using mainly the selection tools of the software Adobe Photoshop ® CS2. Yoshiyama et al. (  2011 ) developed an approach similar to that of  Emsen (2006)   Cornelissen et al. (2009)  further advanced comb image assessment by developing a semi-automatic method that counted the number of capped brood cells, instead of measuring occupied area. This method was on average 23s slower than the Liebefeld method, as it required human intervention during segmentation. However, the estimates of capped cells were more accurate with the semi-automatic method (correlation with the actual number of cells = 0.
Meanwhile, various software packages have been developed for comb assessment. In a presentation of the HoneybeeComplete,  Wang & Brewer (2013)  showed the ability of this commercial software to correctly classify capped brood cells 97. 4% of the time, with the rate increasing up to 99. 5% when the user pre-selects the search area. The developers did not provide methodological details on software development, only reporting the use of an unspecified set of pattern algorithms. The commercial HiveAnalyzer software, developed by  Höferlin et al. (2013) , represented an important step forward in comb assessment by classifying cells other than capped brood. The developers categorised the comb cells into seven classes by using a cascade of classifiers based on linear Support Vector Machines. The accuracy of the classifier was 94%, as measured on a subset of cells classified with high confidence.