dataesr/scientific-paragraphs-categorization huggingface dataset:
This example suggests that clustered motifs do not supersede the motifs from which they were merged, in particular when these motifs were built from hundreds of ChIPseq peaks, such as TF65. However, a systematic analysis reveals that this observation admits exceptions. Indeed, we ex-PAGE 11  OF 13 Nucleic Acids Research, 2017, Vol. 45, No. 13 e119  tended this analysis by comparing the IC of each merged motif with the two original motifs along the trees resulting from the clustering of all the Human TFBMs from the HO-COMOCO database. The distribution of relative IC differences (Figure  6D , see Supplementary Notes for the formulae) shows that in most cases (361/514) the IC of the merged motif has an intermediate value between the respective ICs of the two original motifs. However, we also observed 41 cases of IC gain and 112 cases of IC loss (i.
Notably, this visualization has enabled us to identify the Ocr motif in the Oct4 ChIP-seq peaks (Figure  2 ). This motif was already present in our previous analysis of the same dataset (  15 ), but we had not been able to detect this subtle variation among all other unclustered motifs. We thus expect that this dynamic visualisation of motif clusters will be beneficial to both experts and nonexperts users. Furthermore, matrix-clustering dynamic interface can be used and integrated in the website of motif databases. Our method relies on hierarchical clustering with a bottom-up partitioning. The tree is thus segmented based on the similarity between all the descendant PSSMs of each branch, which strongly differs from the usual cut-off at an arbitrary height of the tree. We evaluated an alternative segmentation method called dynamic tree cut, which relies on tree topology to produce balanced clusters (  66 ), but we kept our approach because it allows to cut the tree based on motif similarity rather than on the sole tree topology.
However, in order to favor specificity and obtain non-redundant collection of motifs (case study 4), stringent thresholds can be used (Ncor > = 0. 65 and cor > = 0. 80). Several databases like JASPAR and HOCOMOCO already provide non-redundant collections, obtained by a time-consuming manual curation, which will become complicated to maintain with the increasing number of motifs. Of note, in motif databases, the term non-redundant denotes the restriction to one PSSM per TF  (7). However, distinct TFs may also bind very similar motifs (e. g. Oct4, Oct9 and Oct11), and in some cases a same TF might bind to alternative motifs (e. g. , TF complexes, or multi-domain TFs). In this study, the term non-redundant refers to a single PSSM summarizing a set of highly similar motifs, independently of the binding TF.
Reducing the size of motif collections is becoming crucial to limit the processing time of tools relying on full motif databases (e. g. , motif enrichment, motif comparisons, identification of regulatory variants). As a proof-of-concept, we have shown that matrix-clustering can be used to compare full collections, but also to drastically reduce inter-database redundancy: in case study 4 we produced non-redundant motifs collections that reduced the insect, plant and vertebrate collections to 19%, 19% and 32% of their original sizes, respectively. We thus expect that meta-databases, such as footprintDB  (10)  or Cis-BP (9) could benefit from matrix-clustering to offer non-redundant motif collections. Non-redundant motif collections would reduce computing time when scanning large sequence sets with large collections of PSSMs.
However, it should be noted that merged motifs resulting from clustering are by definition less specific than the original motifs, more so if they have a poor quality. Still, for motifs built from a few binding sites, a merged motif could be more specific (Figure  6  and Supplementary Notes). We suggest that merged PSSMs could be used to represent a group of similar motifs to reduce computing time for tasks affected by motif redundancy (e. g. comparison of discovered motifs with reference databases). For more precise tasks, such as TFBS prediction, they can be suboptimal. The possibility to cluster several collections simultaneously makes matrix-clustering a versatile tool, as demonstrated by the four case studies considered (identification of motif variants, integration of motifs found by multiple motif discovery tools, comparison of motifs obtained e119 Nucleic Acids  Research, 2017, Vol.
ACKNOWLEDGEMENTS We wish to thank to  Bruno Contreras-Moreira ,  Aïtor Gonzales ,  Carl Herrmann ,  Samuel Collombet ,  Roberto Tirado-Magallanes ,  Lambert Moyon  and  Coby Viner  for suggestions to improve the method and the visualization. We are also thankful to the JASPAR team for checking and validating the clustering of JASPAR motif collections.
FUNDING French  Agence Nationale pour la Recherche iBone  [ ANR-13-EPIG-0001-04 ];  EchiNodal  [ ANR-14-CE11-0006-02 ];  CONACyT-Mexico  grant [Fellowship 391575 to J. A. C. M. ]; PhD grant of J. A. C. M. from the  Ecole Doctorale des Sciences de la Vie et de la Santé ,  Aix-Marseille Université. Funding for open access charge:  French Agence Nationale pour la Recherche ORICHOICE  [ ANR-14-CE10-0019-03 ].
The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www. genoscope. cns. fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform Claudine Medigue, PhD, is a research director at CNRS. She is the head of the Laboratoire d'Analyse Bioinformatiques en Genomique et Metabolisme located at Genoscope.
He works on the development of pan-genome graphs in MicroScope and their application in metagenomics. Adrien Josso is an engineer in bioinformatics at CEA. He works on MicroScope software development for workflow management and metabolic data integration. Aurelie Lajus is an engineer in bioinformatics at CEA. She works mainly on (meta)genome project management and software integration in the MicroScope platform. Jordan Langlois is an engineer in bioinformatics at CEA. He works on software integration and Web developments in the MicroScope platform. Hugo Pereira is an engineer in bioinformatics at CEA. He works on MicroScope software development for workflow management of NGS projects. Remi Planel is an engineer in bioinformatics at CEA. He works on MicroScope software development for pan-genome computation and Web visualization.
David Roche is an engineer in bioinformatics at CEA. He works on NGS project management, software integration and Web developments in the MicroScope platform. He is also involved in the training of MicroScope users. Johan Rollin is an engineer in bioinformatics at CEA. He works on software integration and Web developments in the MicroScope platform. Zoe Rouy is an engineer in bioinformatics at CEA. She works mainly on (meta-)genome project management, software integration and Web developments in the MicroScope platform. David Vallenet is a senior researcher at CEA. He is managing all the technological developments of the MicroScope platform and has specific research activities in the development of methods for enzyme function prediction and metabolic network analysis.
Introduction Large-scale genome sequencing and the increasingly massive use of high-throughput approaches produce a vast amount of new information that completely transforms our understanding of thousands of species. However, despite the development of powerful bioinformatics approaches, full interpretation of the content of these genomes remains a difficult task. To address this challenge, several integrated environments that combine and standardize information from a variety of sources and apply uniform (re-)annotation techniques have been developed (i. e. EnsemblGenomes  [1] , IMG  [2] , PATRIC  [3] ). In the context of the French National Sequencing Center (CEA/DRF/Genoscope), we have developed the MicroScope platform, which is a software environment for management, annotation, comparative analysis and visualization of microbial genomes.
Interface for user data integration Integration and analysis of genomic data into MicroScope are open and free of charge for the worldwide community of microbiologists. To standardize and make user submission fully automated, we have developed a dedicated Web interface (https://www. genoscope. cns. fr/agc/microscope/about/services. php). The service is mainly used for the annotation of microbial genomes: both newly sequenced genomes (which will remain private till the genome publication and/or their submission to public databanks) and, for comparative analysis purpose, public prokaryotic genomes (Figure  2 ). Moreover, three other types of services are provided for the integration of (i) genome assemblies (bins) from metagenomic samples (ii) RNA-seq data for quantitative transcriptomics and (iii) DNA sequencing (DNAseq) data to identify genomic variations in evolved strains (Figure  3 ).
To ease data integration and comparative studies, standardization of contextual data about genome sequences is essential. For metagenomes, we have added a dedicated form that follows the MIMS specifications (minimum information about a metagenome sequence  [16] ). When submitting assembled metagenomic data in Microscope, the users are invited to select the type of environment (e. g. soil; air; water; human-associated; plant-associated) and to complete the associated fields (e. g. collection date, environment biome, geographic location, etc. ). These fields are dynamically loaded and displayed on metagenome type selection. Indeed, the MicroScope database model is flexible enough to store predefined descriptors, like MIMS, or the ones defined by users. At present time, an average of eight genomes a day are requested for integration in the platform (this includes bins from metagenomic samples).
Running the annotation pipelines About 25 analyses workflows include most of the currently used annotation software, plus some in-house tools and/or annotation strategies (Table  1 ). The newly sequenced (meta)genomes, generally submitted in several contigs and organized (or not) on the final chromosome(s), are first analyzed by the syntactic annotation pipeline to identify protein genes, transfer RNA (tRNA), ribosomal RNA (rRNA), noncoding RNA (ncRNA) and repeats (Figure  2 , Table  1 ). For a more accurate prediction of small genes and/or atypical gene composition, we have developed a strategy to first construct appropriate gene models that takes into account the codon usage of the studied organism. These models are then used in the core of the AMIGene program  [17]. Starting with the set of genomic objects identified during the syntactic annotation process, the next step is to infer biological functions of the predicted genes.
Our functional annotation pipeline includes sequence similarity searches tools using generalist (i. e. UniProtKB/Swiss-Prot) or specialized (i. e. Interpro, FIGFAM, etc. ) databases (Table  1 ). Results obtained with high-quality manually curated protein sequence data sets (i. e. Swiss-Prot, E. coli K-12, B. subtilis 168 MicroScope-curated genes) are first considered in the final functional automatic annotation procedure. This procedure also takes into account the results obtained from the computation of synteny groups with complete reference prokaryotic genomes and the one available in MicroScope. Indeed, for assigning function to novel proteins, gene context approaches often complement the classical homology-based gene annotation in prokaryotes. The method we have developed offers the possibility of retaining more than one homologous gene (i.
e. not only the bidirectional best hit), to allow for multiple correspondences between genes; that way, paralogy relations and/or gene fusions are easily detected  [4]. Information from the syntactic and functional annotation pipelines can be placed into a biological context to understand how the predicted objects interact in functional modules such as metabolic pathways. Each genome integrated into MicroScope is processed by an in-house workflow based on the MetaCyc reference database  [18]  and on the Pathway Tools software  [19]. This software creates a Pathway Genome DataBase (PGDB) containing the predicted pathways and reactions of an organism. It uses a matching procedure for which we directly use as input the official MetaCyc reaction frame identifiers when available in the genome annotation; this allows to avoid  Four types of services are provided for the integration of (i) newly sequenced or publicly available genomes (Genome), (ii) genome assemblies/bins from metagenomic samples (Metagenome), (iii) RNA-seq data for quantitative transcriptomics (RNA-Seq), (iv) DNAseq data to identify genomic variations in evolved strains (Evolution).
Following the three main steps of the procedure, the user is invited to complete the requested metadata to describe sequencing, genomes and experimental properties, to upload FASTA (genome assemblies) or FASTQ (RNA-seq or DNA-seq reads) files and, finally, to approve the terms of services. Users are then informed by an e-mail about the progress of their integration request. When a public prokaryotic genome is integrated into MicroScope, the original annotations are stored in the database, and the syntactic re-annotation process, which uses the MICheck procedure, often allows to identify missing genes or wrongly annotated one  [21]. This step is useful to annotate more completely the pseudogenes found in a genome ('real' or because of sequencing errors), an important piece of information when comparing closely related species.
Exploration of the genomic data: simple and advanced queries The 'Search/Export' menu (Figure  4 ) allows the user to perform Blast and pattern searches in the MicroScope database, and to download, in standard file formats (Genbank, EMBL, GFF, etc. ), sequences, annotation data and the metabolic networks. The 'Search by keywords' functionality allows the user to identify genes and functions of interest using a variety of selection filters. The 'single mode' is used to query only one chromosome and the 'multiple mode' to query several replicons (of one organism) and/or several genomes. A basic keyword search enables the user to quickly retrieve genes having a particular function (i. e. 'kinase', 'transporter'). Each kind of precomputed results (i. e. Blast results on various primary data, InterPro and FigFAM results, etc.
) can be queried. Figure  4  shows an example of a query on the similarity searches in the CARD database  [22]  ('Resistome' data set). Keyword searches are useful to compare current annotation of the gene functions with the results, in terms of biological function, given by a specific analysis method. Indeed, the result of a query can be refined with a further query. For example, one can search for gene annotated as 'protein of unknown function' (first query) and then, search for the one having significant Blast results with proteins annotated with specific functions (second query). Whatever the query, the result output is a list of candidate genes, the genomic contexts of which can be easily visualized: next to the gene label, a magnify icon can be clicked to come back to the MaGe graphical representation with automatic displacement of the genome browser centered on the gene of interest.
MaGe (Magnifying Genome): a genome browser in the light of synteny results The MaGe graphical interface is one of the functionality that had a strong positive resonance among users: this genome browser offers gene context exploration of the studied genome compared against other microbial genomes. The graphical representation of the synteny groups allows the user to quickly see if part of the genome being annotated shares similarities and locally conserved organization with the selected sequences. As shown in Figure  5 , there is a clear synteny break in the visualized part of the E. coli CFT073 strain: the genes located between 5116000 and 5131000 share homologs only with the E. coli pathogenic strain ABU and, partially, with the E. coli commensal strain BLASTP similarity results obtained with well-known resistance genes stored in the CARD database are queried using an amino acid identity threshold of at least 80% and using the keywords 'kanamycine tetracycline'.
The method combines (i) the results of algorithms that detect signals in the query sequence indicative of horizontal transfer origin (tRNA hotspots; mobility genes; compositional bias  [24] ) and (ii) the identification of synteny breaks in the query genome in comparison with closely selected microbial genomes. Results are reported in a tabular form and on a circular representation of the genome (Figure  6 ). Finally, the 'Pan/ Core Genome' functionality computes dynamically the pangenome and its components (core-genome; variable-genome) of a set of selected organisms (up to 200). The method uses the MicroScope gene families (MICFAM) computed with the SiLiX software  [25]. The set of common (¼ core-genome), variable and strain-specific genes of each compared genomes can be exported in a tabular file format or in a 'Gene Cart'.
Indeed, at any level of the MicroScope Web interface, the gene list that results from the corresponding search/analysis can be selected for inclusion into a 'Gene Cart'. The user can manage several 'Gene Carts' at the same time resulting from different queries. A specific interface has been developed to perform various operations such as the intersection or the difference between two gene carts, to extract sequences or to run multiple alignments via the plugged Jalview software  [26]  (Functionality 'Gene Carts' of the 'User Panel' menu). Two functionalities of the 'Comparative Genomics' menu are most specifically related to pathogen analysis (Figure  6 ): 'Resistome', which uses the Comprehensive Antibiotic Resistance Database  [22]  a manually curated resource containing high-quality reference data on the molecular basis of antimicrobial resistance, and the Resistance Gene Identifier (RGI) tool to predict the resistome of a genome.
MicroScope annotation platform | 1079 Starting from the set of predicted and/or validated Enzyme Commission numbers (EC numbers), metabolic maps are dynamically drawn via a request to the KEGG Web server ('KEGG' functionality). A color-based code enables to see the number of enzymatic activities (i. e. EC number) of the annotated genome found in specific metabolic pathways (Figure  7 ). The interconnected metabolic pathways represented in KEGG are supplemented by the MicroCyc PGDBs built with the Pathway Tools software using MetaCyc as reference metabolic database (see 'Running the annotation pipelines' section). The 'MicroCyc' functionality allows the user to browse and query the metabolic network of a target genome using the Pathway Tools Web interface  [18]. These two sets of predicted pathways can be used in the 'Metabolic profiles' functionality.
For each predicted BGC, a summary of similarities with the ure shows, for one selected MicroScope genome, the mapping of the annotated EC numbers on a KEGG metabolic map (enzymes encoded by genes localized on the current genome browser region are highlighted in yellow, and the ones encoded by genes localized elsewhere are highlighted in green). Predicted PGDBs using the Pathway Tools software are available using the 'MicroCyc' functionality. Comparison of metabolic pathways between a set of selected genomes is performed using the 'Metabolic profiles' tool: for each metabolic pathway, a completion value is computed, which corresponds to the number of reactions found in the genome Â divided by the total number of reactions in the pathway. This value can take into account pseudogenes or not. It ranges between 0 (absence of the pathway) and 1 (complete pathway).
The figure also shows an example of antiSMASH, which predicts Biosynthetic Gene Clusters in prokaryotic genomes. For the NRPS/PKS cluster types, the predicted peptide monomer composition and its corresponding SMILES formula are specified. Below the graphical representation of the predicted antiSMASH cluster, a summary of MIBiG cluster similarities, BGC gene composition as well as tailoring cluster similarities is given. reference database MIBiG  [31] , BGC gene composition as well as tailoring cluster similarities is given. This last item relies on a knowledge database provided with antiSMASH about tailoring clusters already described in known BGCs and associated with publications.
Analysis of experimental data The functionalities available in the 'Transcriptomics' and 'Variant discovery' menus rely on the results of the pipelines used to analyze data from transcriptomic projects (i. e. RNA-seq experiments) and data from evolution projects (i. e. clones of the same species at different generation times). Exploration of these experimental data has been illustrated in the two last publications of the MicroScope platform  [6, 7]. The 'Transcriptomics' functionality allows exploring the transcript coverage along genome, expression levels of genomic objects (genes, ncRNAs) and differential expression between samples for distinct experimental conditions. All appropriate pairwise comparisons of experimental conditions can be directly queried from the interface. Differentially expressed genes may be projected on reconstructed metabolic networks to highlight metabolic pathways significantly affected by experimental conditions.
Expert curation of genomic and metabolic data From the results of the exploration of data and the analysis tools, MicroScope users can review and curate the automatic functional annotation of genes encoded by its genome of interest. This task is performed using the 'Gene Editor', which has been illustrated in the 2013 MicroScope publication  [6]. Briefly, it is made of three main sections: The 'current annotation' section allows the user to modify, delete and add information. The functional description of gene functions is a free-text field exposed to inconsistencies across genes and genomes. We thus have also integrated enumerated lists of well-defined and nonredundant terms for the product type field (defined in GenProtEC  [32] ), the functional classifications (MultiFun  [33]  and TIGRFAMs  [34] ) and for the class field (inspired from the Pseudomonas Genome database  [35] ), which helps understanding the origin of the functional annotation (e.
g. it comes from the functional description of an homologous gene for which the function has been experimentally demonstrated). The curation of associations between genes coding for enzymatic activities and the biochemical reactions catalyzed by these enzymes is performed using two main enzymatic reactions resources: MetaCyc  [18]  and Rhea  [36]. Finally, to alert users about possible inconsistencies, annotation is checked via an automatic procedure launched when the annotation is saved in the database. The 'automatic annotation' section contains the gene function predicted by our automatic functional annotation procedure ('MicroScope pipeline annotation'), which involves the transfer of the reliable up-to-date reference annotations to 'strong' orthologs, if any  [4]. In case of published bacterial genome integrated in MicroScope, the section contains information on the functional annotation in nucleotide sequence databanks and UniProtKB if available.