dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and conclusions EMULSION is the first framework that simultaneously provides a Domain-Specific Language dedicated to the comprehensive and accurate description of epidemiological models, from SIR-like models to more complex multi-scale multi-concern models, together with a modular simulation engine using a multi-level agent-based architecture, to encompass existing epidemiological issues and modelling paradigms within a homogeneous interface (Fig  1 ). Elaborating realistic models (such as  [15, 57] ) often requires many trials aiming at the exploration of various assumptions and processes. EMULSION significantly accelerates model development, first because it provides classical computational bricks, but also since changing hypotheses (e. g. adding or deleting a state or a transition) generally consists in modifying the configuration file, instead of rewriting many parts of a large specific source code.
The modularity of model description allows assessing separately hypotheses, which demands much more work when dealing with ad-hoc models and is more prone to programming and hence interpretation errors. Using EMULSION, we re-implemented very quickly the initial within-herd Q fever dairy cattle model and assessed the compatibility of simpler alternative model structures with previous predictions derived from a model whose parameters were estimated using observed on-farm data. Then, moving to the regional scale required little transformation of the within-herd model. Small pieces of code related to trade and weather data and calculation of transmission functions were used as specific add-ons by the simulation engine. The facility to compare a large panel of assumptions regarding the existence of a latent stage in Q fever infectious process, the nature of airborne transmission, and values of the less well-known parameters, allowed us to identify and calibrate the best candidates with respect to observed herd incidence data.
Also, conditions under which trade movements and airborne transmission contribute to new infections were explored, highlighting new findings, especially that infections caused by movements are almost deterministic and impact mostly herds in peripheral areas and low prevalence areas. This works provides a proof of concept, demonstrating the added-value of using such a framework, both in terms of code reduction and model readability. To promote our approach and modelling language, EMULSION will be soon released as an open-source software. The current version of the generic simulation engine being written in Python, efficiency cannot compete with compiled languages such as C++ (as the code generated by KENDRICK  [40] ) or Java (used in the Broadwick framework  [38] ), especially at the between-herd scale.
This was not under the scope of the present study. Nevertheless, the facility to choose the granularity level of simulations and the adaptive gathering of individuals make the approach much more efficient than IBM anyway. To tackle efficiency issue, the next step will be to consider using EMULSION's DSL to build dedicated, optimized code from model descriptions. We showed how modelling paradigms and scales could be wrapped within agents, which are in charge of processing the required calculations according to their specificities. This allows modellers to focus on their research questions instead of implementation issues, while still being able to select and compare relevant modeling paradigms. To go further, new computational issues in epidemiological modelling have to be addressed, especially in coupling contrasted paradigms  [63].
With the rapidly developing high-throughput sequencing technologies known as next generation sequencing or NGS, our approach to gene hunting and diagnosis has drastically changed. In <10 years, these technologies have moved from gene panel to whole genome sequencing and from an exclusively research context to clinical practice. Today, the limit is not the sequencing of one, many or all genes but rather the data analysis. Consequently, the challenge is to rapidly and efficiently identify disease-causing mutations within millions of variants. To do so, we developed the VarAFT software to annotate and pinpoint human disease-causing mutations through access to multiple layers of information. VarAFT was designed both for research and clinical contexts and is accessible to all scientists, regardless of bioinformatics training.
Data from multiple samples may be combined to address all Mendelian inheritance modes, cancers or population genetics. Optimized filtration parameters can be stored and re-applied to large datasets. In addition to classical annotations from dbNSFP, VarAFT contains unique features at the disease (OMIM), phenotypic (HPO), gene (Gene Ontology, pathways) and variation levels (predictions from UMD-Predictor and Human Splicing Finder) that can be combined to optimally select candidate pathogenic mutations.
INTRODUCTION Massively parallel sequencing, also called NGS (next generation sequencing), led to a genetic revolution with the ability to sequence any human genome in a few hours. Nevertheless, despite the thousands of exomes and genomes that have been studied (Genome Aggregation Database (gno-mAD) (1)), we still have only a limited vision of the human genome variability especially in the context of rare human genetic disease. Indeed, most disease-causing mutations are private, and the availability of functional tests is limited. Therefore, distinguishing neutral mutations from diseasecausing ones is challenging. This is even more challenging for rare diseases, defined in Europe as conditions with a frequency below 1: 2000, most of them being very rare. A review from Orphanet  (2) , revealed that the majority of rare diseases are defined by a handful of published reports describing a few individuals with a previously unidentified genetic syndrome.
MATERIALS AND METHODS VarAFT is a freely available application written in Java and can therefore be used on most computers. Various binaries are available to download for Mac, Windows and Linux operating systems. VarAFT does not require any specific hardware configuration, however performances are dependent on the number of CPU cores and the amount of available memory. For example, the annotation of one sample containing 58,783 variations takes 19 min with 1 CPU core and drops to 9 min with 4 CPU cores. The breadth and depth of coverage analysis of the same sample takes 21 min with 1 CPU core and 7 min with 4. This time could be reduced to, respectively, 27 and 6 s if the user limits the breadth and depth of coverage analysis to the ACMG actionable genes list  (22). Specific versions have been created to allow the installation of VarAFT on Windows machines without administrative rights.
The graphical user interface was created using the Java Swing library. The 'coverage module' use BEDTools  (23)  to compute breadth and depth of coverage data for any sequencing experiment using BAM files. Tables and charts are respectively generated with the Swing JTable library from Oracle (http: //oracle. com) and the JFreeChart library (http://jfree. org). The breadth and depth of coverage analysis is performed at the genomic level unless a BED file is provided to limit the analysis to regions of interest. The 'annotation module' can collect variant data from various file formats including VCF/gVCF (single or multisamples) or tabulated files. It combines all information from the dbNSFP  (24)  and ANNOVAR with unique features including OMIM  (25) , HPO  (26) , Gene Ontology  (27) , pathways (Reactome  (28) , KEGG  (29) , PID  (30) ) and predictions from UMD-Predictor  (31)  and HSF (Human Splicing Finder)  (32).
Note that ANNOVAR, KEGG, UMD-Predictor and HSF require a user registration to comply with their license. Once annotated, data from any sample can be combined through the 'filtration module'. It allows users to combine data from multiple sources and layers. The display mode can be parametered to display a subset of available columns. Interactive filtration features allow the progressive reduction of the list of candidate mutations by combining the various annotations. An in-house mutation database can be generated by VarAFT or provided by users, to exclude frequent mutations reported in a specific population and/or platform-dependent artefacts. Once filtration steps have been defined and validated, they can be saved, reapplied and shared for subsequent analysis to ensure filtration standardization in a clinical diagnosis context or for large research networks.
Experiment quality control compatible with clinical use The 'coverage analysis module' was designed to evaluate experiment quality. It provides the breadth and depth of coverage for any transcript or exon at the nucleotide level, either through dynamic histograms or tables (Figure  2 ). A report can be generated to rank genes and exons according to their breadth of coverage at a depth of 1, 5, 10, 20 or 30× and evaluate their quality in accordance with international guidelines (EuroGentest: www. eurogentest. org). In a clinical diagnosis context, BED files can be provided or generated through VarAFT to restrict the exome analysis to some transcripts or genes. Indeed, as indicated in the Eurogentest guidelines, to limit incidental findings it is recommended to focus on genes of interest for which a relationship between genotype and phenotype has been published and confirmed  (34).
identified from trio, cohorts and individual cases from autosomal dominant and recessive diseases, such as dystonia, neuromuscular disorders, mental retardation and premature aging. For example, VarAFT was recently used to analyze 306 genes in a cohort of distal myopathy patients  (35). The software was also evaluated as a prioritization system to highlight mutations involved in cancers (  14 ) and other situations  (14) (15) (16) (17) (18) (19) (20) (21). To demonstrate VarAFT usefulness and efficiency, we chose the following use cases: Use case #1. The first dataset was extracted from Kamphans et al. and contains VCF files from four individuals (464, 465, 466 and 467) (36) from a family with an autoso-W550 Nucleic Acids Research, 2018, Vol. 46, Web Server issue mal recessive disease. Each sample contained respectively 20 708, 20 560, 20 552 and 20 547 variants.
DISCUSSION VarAFT is a multiplatform freely available software that allows the simultaneous annotation, filtration, and breadth and depth of coverage analysis of WES, WGS and targeted sequencing experiments from any sequencing platform. Its graphical user interface, various modules and unique features, such as pathogenicity predictions from UMD-Predictor and HSF, allow untrained users to rapidly highlight disease-causing mutations in multiple genetic scenarios. In addition, VarAFT allows visualization of data quality (breadth and depth of coverage) through direct access to BAM files. The nucleotides and genotypes can also be easily accessed through IGV. As reported by Salgado et al. (4) , on one hand, automatic prioritization systems are now available to ensure a homogeneous treatment of samples.
ACKNOWLEDGEMENTS We are grateful to all users for their continuous support and feedback. We thank the members of the  AP-HM Medical Genetics department , which were the first adopters of the system in a clinical diagnosis context and all members of the  Marseille Medical Genetics research unit  for useful input. We acknowledge our RD-Connect and NeurOmics partners for their adoption of the VarAFT system. Additionally, we thank the  French ELIXIR Node , the  Institut Francais de Bioinformatique (IFB) , for hosting a cloud appliance of the VarAFT system used for trainings. At last, we would like to thank  Michael J. Mitchell  for manuscript editing.
[3] [4] [5] ). Not only is the image of the specimen visible but the associated data of each specimen (location, collection date, habitat, etc. ) is also painstakingly captured and entered into databases. These efforts are even more important in regions of high biodiversity concentration such as the tropics, and rain forests in particular, where our understanding of biodiversity remains limited. Open access online databases can and will accelerate our basic understanding of species taxonomy and distribution patterns, a prerequisite for any biodiversity study. This type of large scale endeavor, integrated with data from other fields such as earth and social sciences, opens the way to large scale biodiversity syntheses providing fundamental data towards understanding the current state, past evolution, and future of biodiversity.
Looking back, is there a project that your lab pursued that stands out for you as particularly inspiring, tough, or simply memorable? In 2013, we synthesized for the first time our knowledge about tropical African plant biodiversity. Leaning on the massive digitization effort of plant specimens for numerous participating herbaria and a consortium of several African botany experts, we generated a high quality open access mega database called RAINBIO. RAINBIO contains to date over half a million African plant specimens representing distribution information for over 25,000 plant species  [6]. This biodiversity synthesis exercise allowed us to address numerous questions about the current state and conservation of African tropical plant biodiversity  [7] [8] [9] , and especially rain forests. We are now using this database and modeling approaches to understand how tropical vegetation will respond to climate change across tropical Africa by the end of this century.
The project was tough because we were dealing initially with thousands of records that needed to be sorted, quality checked, and validated. We generated automated scripts to deal with this mass of information, which was then checked by the experts to ensure the database is as accurate as possible. This was inspiring because it highlighted the vital role of taxonomists and experts. The availability of masses of online biodiversity data gives the impression that all is known and perfect, but the reality is that biodiversity data need to be constantly updated, curated, and validated, which is part of the taxonomists' work. Now more than ever we need to continue efforts to discover and describe biodiversity around us, especially in high biodiversity regions such as the tropics. Is there a paper or a scientist that inspired you, or was seminal for your research? Origin and evolution of tropical rain forests published in 2000 by Robert J.
The current wealth of genomic variation data identified at the nucleotide level has provided us with the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that amino acid variation of a molecule's sequence has on its molecular interactions is a key step towards connecting a full mechanistic characterization of nonsynonymous variation to cellular phenotype. Here we present an open access resource created by IMEx database curators over 14 years, featuring 28,000 annotations fully describing the effect of individual point sequence changes on physical protein interactions.
It has been the work of the IMEx Consortium  11  to capture such information into a single data set and thus make it available for researchers to re-use and reanalyse. IMEx Consortium annotators follow a detailed curation model, capturing not only full details of the experiment (including interaction detection method, participant identification method and the host organism) but also a description of the constructs used. This may include the co-ordinates of deletion mutants used to derive a minimum binding domain and also the effect of point mutations. Databases in the Consortium perform detailed, archival curation of published literature and also receive pre-publication data through direct submissions. This close collaboration with data producers often entails access to unpublished details in the data, such as experiments reporting mutations that have no effect on interactions, which enables the capture of added value for the scientific community.
The IMEx mutations data set: data curation and quality control The IMEx Consortium databases have been collecting point mutation data for over 14 years, which has resulted in a sizeable data set of almost 28,000 fully annotated events (www. ebi. ac. uk/intact/resources/datasets#mutationDs). The IMEx resources curate interaction data into structured database fields, and from there into community standard interchange formats, and each observation is described using controlled vocabulary terms. Mutations are mapped to the underlying protein sequence in UniProtKB and updated in line with changes to that sequence, to ensure that they stay mapped to the correct amino acid residue with every proteome release. In order to make the mutant data set more accessible to the biomedical scientist, the Consortium has released the mutation data set in a tab-delimited format (Box 1), which includes details of the position and the amino acid change of the mutation, the molecules in the interaction and the effect of the mutation on the interaction, as well as additional fields containing contextual information.
Additionally, a data-update pipeline has been specifically developed to ensure the accuracy of the annotation of mutation events as interaction participant features (suppl. figure  1 ). The construction of this pipeline has been made possible by the creation of specific fields capturing sequence changes in our recently developed standard format PSI-MI XML3. 0  12. It is run in coordination with the IntAct database monthly protein update procedure, which ensures synchronization with UniProtKB  13  and automatically shifts feature positions if there are changes in referenced protein sequences. The pipeline has been applied to the entire data in the IntAct database (www. ebi. ac. uk/intact), in which all IMEx data, and also legacy data generated by the IntAct, MINT, DIP and UniProt curation groups is housed (see Supplementary Methods for details on re-annotation and data update procedures).
Data set statistics The full IMEx mutations data set contains 27,868 fully annotated events in which a sequence change has been experimentally tested in an interaction experiment. All this information has been manually curated, representing over 33,000 person-hours' worth of biocurators' work, and it is continuously growing with on-going IMEx curation activities. The 4,353 proteins annotated come from 297 different species, with over 60% of the events annotated in human proteins and roughly 90% annotated in seven main model organisms (see table  I ). In total, 13,926 interaction evidences are annotated with differentially reported effects, using the PSI-MI controlled vocabulary. Most of the effects reported are of a 'deleterious' nature, either disrupting (10,976 annotations, 39. 3%) or decreasing the interaction (8,553 annotations, 30.
99% of the publications (4,173) contain less than 100 mutation annotations and represent 80% of the annotations (22,218). Only 8 publications contain over 100 annotations, with one of them describing over 4,000 events, a study in which the authors systematically tested large numbers of variants and their effect on interactions  5. Recording large-scale data sets such as this one has been enabled by the development of the flexible PSI-MI XML 3. 0 format cited above. Currently, the only resources that represent the impact of amino acid substitutions on binding events are the SKEMPI database  14  , UniProtKB and IMEx Consortium member databases through IntAct (see table II for a detailed comparison). Of these resources, IMEx is the biggest and the only one that can provide easily accessible, systematically described, up-to-date annotations.
UniProtKB mutagenesis annotations record whether a change in sequence affects an interaction, but the experimental context is not captured and the effects are described in a semi free-text field that is difficult to parse. SKEMPI offers a detailed overview of sequence change effects on binding derived from in vitro experiments, recording changes in affinity and other kinetic parameters. Only very specific interaction detection methods, using purified proteins, are considered, which limits its scope. The IMEx Consortium is currently formed by 11 groups, each one with their own area of interest, that have agreed to use the same curation standards and data representation download formats. All members of the consortium  [15] [16] [17] [18] [19] [20] [21] [22]  use the curation platform provided by the IntAct team at EMBL-EBI.
Most of the proteins in the data set have a low number of associated mutations, with most proteins having 5 or less sequence changes (suppl. figure  2a ) and less than 15 annotations (suppl. figure  2b ). There is a greater depth of information available for human proteins, since the relative amount of human data vs other species increases with the number of annotations per protein. The IMEx evidence-centric curation model also makes it possible to check whether the same mutation has been tested on identical interacting molecules using different interaction detection methodologies (or by different research groups) and whether the outcome of the mutations has been consistent in all these experiments. In figure  1f  we show that the majority of the mutations have only been annotated once (tested in one experiment only).
In those cases where there have been multiple instances of evidence testing, the results appear to be highly consistent, with only a small number of cases identified for which conflicting results have been reported. For 7,212 cases where the effect of a mutation on an interface was tested 2 or more times, only 131 (1. 8%) show different effects, and only 61 cases (0. 8%) reported antagonistic effects. One reason for these contradictory results may be differences in experimental methodologies used to measure the effect, since IMEx databases recognize a large variety of experimental approaches that provide molecular interaction evidence. The vast majority of the data set refers to amino acid substitutions, with a marginal amount of insertions and deletions reported (only 65 deletion and 83 insertion annotations).
Genomic variation and the IMEx mutations data set In this era of deep-sequencing genomics, there is a wealth of data concerning nonsynonymous genomic variants. As discussed before, the motivation behind the design of these experiments varies, and only a fraction were specifically designed to systematically test known variants vs reference ("wild type") versions of the participant proteins  5, 24. Hence, we decided to explore how much of currently available information for natural or disease related variation can be linked to the data set. Because of the strong predominance of human data both in IMEx mutations and in variation data sets, we decided to focus on human proteins only. We used the EMBL-EBI Proteins API  25  to access variation data both manually annotated by and mapped to UniProtKB from large-scale sequencing studies such as the 1000 Genomes 26 , ExAC  27  and COSMIC  28  projects.