dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural and functional genome annotation. Continuous refinement and routine updates of anno- tation are prerequisites for correctly interpreting the functional elements of a genome. An integrated approach was used in this work to update the v3. 5. 1 melon protein-coding genes  9, 10. The structural annotation workflow relied on the MAKER v2 software  18  , which uses a combination of tools to integrate evidence from transcriptome assembly and ab initio gene discovery. The previous annotation release (v3. 5. 1) and an automated-produced annotation provided by NCBI (v102, https://www. ncbi. nlm. nih. gov/) were transferred to the new genome assembly v3. 6. 1 through a liftover process. The transferred annotations were used also as EST tracks to train the definition of HMM models for the ab initio gene discovery step.
The integrated annotation workflow (Supplementary Figure  S2 ) uses RNA-Seq data to add additional features to existing gene models and to identify new gene models where none existed previously. Next-generation sequencing data, especially RNA sequencing data, have already been reported to hold great potential for an independent confirmation and improvement of genome annotation offering an unprecedented improvement for genome annotation analysis  [22] [23] [24] [25] [26] [27]. Among the several genomic resources available for melon a large RNA-Seq collection exists that includes public  [12] [13] [14] [15] [16]  and private experiments. A subgroup of 57 RNA-Seq datasets (Supplementary Table  S3 ) coming from 20 different tissues and developmental stages derived from four melon genotypes was chosen to obtain a reliable genome-guided transcriptome assembly.
About 2 billion reads were collected and after the trimming and filtering processes, about 70% of them were retained, representing 540-fold coverage of the v3. 6. 1 melon genome. Benchmarking analysis were conducted in order to test if a different number of reads might be associated with an increasing benefit in terms of gene annotation. Although the use of only 10% of the total reads was sufficient to produce more than 50% of the maximum number of transcripts detected, the use of larger datasets constantly improved the transcriptome definition. A similar trend was also reported in the maize genome annotation  8. Therefore, the complete set accounting for 1. 2 billion reads (about 220 billion bp) was used for the final melon genome-guided transcriptome assembly. 105,000 assembled transcripts were predicted by TRINITY with an average length of the coding sequence (CDS) of about 1 Kbp and all of them were used to support the gene annotation process.
AHRD annotation uses a combination of approaches and databases to assign a coherent description and a reliability score to each annotation overcoming problems caused by wrong annotations, lack of similar sequences and partial alignments. These advantages can produce relevant insight when exploring a gene annotation. Other plant and non-plant genomes have been annotated using a similar approach, including the 6a maize annotation 8 and tomato ITAG3. 2 (https://www. solgenomics. net/). Among the 29,980 genes, the AHDR procedure allowed to assign a function description to 24,247 of them. About 72% of the AHRD annotated genes have a three star AHRD score and 22% a two star score. Only 6% showed a zero star score. A gene ontology annotation and a KEGG enzymatic pathways association with the annotated genes is reported in Supplementary Table  S5.
Finally, about 6,000 v3. 5. 1 genes were not present in the new annotation v4. 0. All of them, although successfully predicted in the annotation workflow and present in the raw dataset of 43,564 genes, were removed since there was overlapping with repeat elements. We also observed that these removed genes were frequently short (<100 bp), monoexonic or without a known functional description. By contrast, only a small part (<10%) showed a description or an expression signal in at least one tissue, evincing a possible role as functional genes. The main differences between the v3. 5. 1 and v4. 0 annotation releases, concerning different feature parameters, such as gene length and number of exons, are reported in Supplementary Table  S6.
4 ). The tracks can be browsed via the hierarchical track selector. The browser also offers bulk download of interval-specific track data in common file formats (FASTA, GFF3, BED) as well as a search tool (elasticsearch) that allows performing searches by key words directly from the browser. The versatility of the Jbrowse also permits to integrate user-provided datasets, either local or hosted on third party servers. While searching or scrolling, the page URL is dynamically updated with sufficient information to fully reproduce the display. This feature enables collaboration through URL sharing between remote users. The integration of all the different information permits easy highlighting of the behaviour of a specific feature or of an entire chromosome. Besides the Jbrowse, the Melonomics webpage also hosts a Search section, where users can check a gene or transcript based on keywords, ID, position and pathway, among others.
Overall evaluation showed that NcoI was the most appropriate enzyme for optical-based genome assembly (Supplementary Table  S7 ). Individual molecules of HMW DNA were elongated and fixed onto Optical Chips (MapCards)  35  , digested with NcoI and stained with fluorescent dyes. Restriction fragments were imaged by laser-illuminated fluorescent microscopy using the Argus Mapper. Bulk data collection of MapCards were obtained and super-scaffolding was performed using Genome-Builder TM software. Superscaffolds were used to map them manually onto the v3. 5. 1 assembly, in order to determine the level of scaffold anchoring concordance between the two assemblies. Mapchart  36  and MUAVE (http://darlinglab. org/mauve/mauve. html) were used for genome graphical representations and for synteny analysis between v3.
Repeats annotation. Total transposable element (TE) annotation was performed with the REPET package  17  , with default parameters and thresholds. Annotations shorter than 200 bp were discarded. TEs can contain complete or partial genes, which includes coding domains, and this can lead to leave unannotated coding genes if TE representatives contain those domains. For this reason, confusing TE representatives due to sequence quality or TE representatives that contained specific non-TE protein domains were removed from the masking for the gene annotation process. In order to do so, we built a dedicated subset of the REPET annotation. TE representatives obtained in the TEdenovo step of REPET pipeline were checked with hmmscan (HMMER 3. 1b1, hmmer. org) against the PFAM database 37 for coding domains.
TE representatives with known domains of non-TE proteins (kinases, NB-ARC, LRR); with Ns content higher than 30% and/or TE representatives defined as "noCat" were discarded. For the remaining TE representatives, TE copies search was performed with repeat-Masker (http://repeatmasker. org) with a cutoff of 250. Again, TE annotations shorter than 200 bp were discarded. Final nucleotide coverage of this partial annotation was of 34%. Structural and functional annotation. Transcriptome assembly. We assessed 57 RNA-Seq datasets for the Transcriptome assembly. In an effort to minimize false gene models discovery, only paired-end, strand-specific Illumina HiSeq reads were used. This ensured a considerable reduction of miss-mapped reads and increased the accuracy of the transcriptome assembly. About 100 additional RNA-Seq melon datasets were also available as January 2017 in NCBI SRA archive but were not considered since most of them (>90) derived from a RIL collection and/or from single reads layout experiments.
The complete list of datasets used with a detailed sample description is reported in Supplementary Table  S3. All RNA-Seq reads were processed through a quality check and trimming pipeline using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc) and Trimmomatic  38  respectively, to remove residual adapters and low-quality sequences. Sequences from each library were aligned to the v3. 6. 1 genome using STAR  39. The parameteres used are reported in Supplementary Table  S8. Then, samtools  40  was used to filter only proper-paired reads and optical duplicates were removed using picard-tools (v1. 110). Libraries were checked to have a proportion of mapped reads > 70%. We used raw counts generated by fea-tureCounts (v1. 4. 6)  41  to evaluate gene expression level across the datasets.
The transcriptome was then assembled using TRINITY (version 2. 3. 2)  42  using the genome-guided mode (see Supplementary Table  S8  for the parameters used) and all the mapped reads. The redundancy of the assembly was then reduced by using CD-HIT-EST (version 4. 6. 6) (see Supplementary Table  S8  for the parameters used). The quality of the final assembly was evaluated with BUSCO (version 3) 31 using the conserved plant genes as database (OrthoDB v9). The general statistics of the assembly were produced with TransRate (version 1. 0. 3)  43. Structural annotation with Maker v2. For a complete structural annotation of the melon genes, the Maker v2  18  pipeline was used. All the following steps were performed using the Maker v2 configuration file, only the parameters different from the default values are indicated.
First, a repeat masking step was performed using the Eukaryotic database with Repeat Masker. A lift-over of the melon transcripts v3. 5. 1 and the melon NCBI transcripts (ASM31304v1) were then performed (see Supplementary Table  S8  for the parameters used). The obtained GFF3 were filtered to retain only the transcripts with an AED < = 0. 3 and then they were used to create an ab initio HMM matrix with SNAP. The final annotation was produced by using: 1) the GFF3s obtained after the lift over of the v3. 5. 1 and the NCBI transcripts, 2) the genome guided assembled FASTA of the transcripts and 3) the SNAP HMM model (see Supplementary Table  S8  for the parameters used). Functional annotation. The functional annotation of the genes was performed with the AHRD pipeline (v3. 3. 3) (https://github.
com/groupschoof/AHRD). The predicted protein sequences obtained after the structural annotation step were BLASTed against the TrEMBL database of melon protein sequences (downloaded on April 2017, DB1), the UniRef database of Viridiplantae (downloaded on April 2017, DB2) and the TrEMBL database of Viridiplantae (downloaded on April 2017, DB3). The parameters of the blastp were the following: -qcov_hsp_perc 15 -evalue 0. 001. The obtained tables were used as input for the AHRD pipeline assigning a weight of 854, 904 and 653 to the DB1, DB2 and DB3, respectively. AHRD assigns a quality-code that consists of a three-character string ("*" if the respective criteria is met or "-" if not) to each annotation based on three parameters: i) the overlap of the blast results among the databases used, ii) the overcoming of threshold for e-value (<1E-10) and bit score (>50) and iii) the evaluation of the top token score of assigned Human-Readable-Description (HRD).
The Nucleospin RNA kit (Macherey-Nagel) was used for the extraction according to the manufacturer's instructions. Libraries were synthetized from 2 µg of total RNA using NEBNext Ultra Directional RNA library Preparation Kit (NEB) according to the manufacturer's instructions. Two biological replicates were analysed for each tissue. Libraries were sequenced by Illumina technology. Melonomics. Genomic platform development. Data relative to GO terms, KEGG pathways, gene and transcript names, its coordinates and descriptions was imported into a MySQL (version 5. 5) based relational database stored in a CentOS server (version 7. 4). This database was then integrated into a graphical interface by using AngularJS and SocketIO technologies in the front-end and NodeJS (version 6. 12) together with the Express framework (version 4.
Acknowledgements We thank  Carlos Paniagua  for helping with the DNA extractions for the optical mapping. This work was supported by the  Spanish Ministry of Economy and Competitiveness  grant  AGL2015-64625-C2-1-R ,  Severo Ochoa Programme for Centres of Excellence in R&D 2016-2010  ( SEV-2015-0533 ) and the  CERCA Programme/ Generalitat de Catalunya  to JGM. VR was supported by the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Skłodowska-Curie  grant agreement No  6655919.
Experimental setup The SEGOKPLS computation is performed using the Python toolbox "Scikitlearn v. 014"  (Pedregosa et al 2011)  on an Intel(R) Core(TM) i7-4500U CPU @ 1. 80 Hz 2. 40 GHz. The results of alternative optimization methods are transcribed from Regis (2014) and they are obtained using an Intel(R) Core(TM) i7 CPU 860 2. 8GHZ. To increase the reliability of the comparison between the results of the SEGOKPLS algorithm and the alternative optimization methods, the conditions in which alternative optimization methods are performed are closely approximated. To achieve these conditions, a new latin hypercube design of size d + 1 is used (since the initial design of experiments used by  Regis (2014)  are not available but the same size is used) in which all points are infeasible. For each experiment, the SEGOKPLS algorithm is run 30 times and the computation budget is fixed at 100 objective and constraint function evaluations.
Experimental setup The SEGOKPLS+K computations are performed with the Python toolbox "Scikit-learn v. 014"  (Pedregosa et al 2011)  using an Intel(R) Core(TM) i7-4500U CPU @ 1. 80 Hz 2. 40 GHz desktop machine. The alternative optimization methods are performed in Matlab 7. 11. 0 using an Intel(R) Core(TM) i7 CPU 860 2. 8 Ghz desktop machine. This is one of the reasons that the computational cost was not investigated, in addition to the non-practical environment for getting such a consistent study. Bouhlel et al (2016a)  provide a comparison of CPU time between KPLS and KPLS+K using different number of sampling points and input variables, that gives an idea about the total cost of the optimization design. The optimization of the Equation (  17 ) requires between 10 and 20 minutes per iteration.
Introduction Autoimmune and autoinflammatory diseases (AIDs) represent a socioeconomic burden as the second cause of chronic illness in Western countries. In this context, the TRANSIMMUNOM clinical protocol is designed to revisit the nosology of AIDs by combining basic, clinical and information sciences. Based on classical and systems biology analyses, it aims to uncover important phenotypes that cut across diagnostic groups so as to discover biomarkers and identify novel therapeutic targets. Methods and analysis TRANSIMMUNOM is an observational clinical protocol that aims to cross-phenotype a set of 19 AIDs, six related control diseases and healthy volunteers. We assembled a multidisciplinary cohort management team tasked with (1) selecting informative biological (routine and omics type) and clinical parameters to be captured, (2) standardising the sample collection and shipment circuit, (3) selecting omics technologies and benchmarking omics data providers, (4) designing and implementing a multidisease electronic case report form and an omics database and (5) implementing supervised and unsupervised data analyses.
► Production of a large collection of multiomics data from 1000 patient blood and faeces samples following standardised procedures that (1) are analysed through a system immunology approach together with clinical and biomedical data and (2) will benefit to the community once publicly available. ► One limitation is that TRANSIMMUNOM patients are currently sampled only once, with the goal of portraying their immune status; however, the resampling of patients at later time points is possible and could be planned according to specific scientific questions that would arise from the study or from the literature. Open access trial registration number NCT02466217; Pre-results.
study aim The main goal of TRANSIMMUNOM is to revisit the nosology of multiple AIDs belonging to the AIDC in order (1) to discover and validate biomarkers and novel therapeutic targets and (2) to evaluate biotherapies based on this knowledge. To this end, we propose to cross-phenotype a set of 19 AIDs through a systems immunology approach. The primary evaluation criterion is the significance of new gene expression levels, pathways and signatures involved when comparing the clinical phenotypes of different AIDs. The secondary evaluation criterion is the identification of new biomarkers and/or therapeutic targets by system biology approach. More details on primary and secondary outcome measures can be found on https:// clinicaltrials. gov/ ct2/ show/ NCT02466217. The implementation of the study can be summarised in three steps (figure  1 ), from design (Step 1) to implementation (Step 2) and data analysis (Step 3).
MEthoDs AnD AnAlysIs study design and set-up The multiple AIDs selected call for multiple medical specialties and together with the systems immunology approach to be implemented; this made the endeavour necessarily multidisciplinary. Therefore, to design the study, we assembled a multidisciplinary cohort management team (CMT) (figure  2 ), including a clinical expert consortium (CEC) from seven specialties, nurses, biologists, clinical trial methodologists, immunologists, computer scientists and bioinformaticians. We (1) selected the list of 19 AIDs representative of the AIDC and six related control diseases and included HV, (2) established the study protocol, (3) set up a uniform and controlled sample collection and shipment circuit and (4) designed and implemented a multidisease electronic case report form (e-CRF) and a multiomics database.
sample and data collection For each participant, an anonymised subject unification (ASU) number was generated based on hospital patient number (NIP) and French healthcare registration number (INSEE). ASU is a simple four-letter code one-way encrypted to guarantee safe anonymisation. We collect 135 mL of blood for routine lab measurements and for multiomics assays. Patients are provided with a standardised kit (Metagenopolis, INRA, France) that allows easy collection of faecal samples and maintenance of their integrity during shipment before microbiota metagenomics studies. 12 n a 1-day visit (lasting about 2. 5 hour), we collect all samples and all clinical and biological data, which are stored in an in-house secure e-CRF (table  1 ). Common clinical data include demographic data, educational level, lifestyle, personal and family medical history, current and previous treatment, ECG and quality of life questionnaires.
Data integration and storage The CMT selected, organised and standardised 865 clinical data and routine lab data on an e-CRF and integrated them using OpenClinica, a Code of Federal Regulations (CFR) 21-part 11 compliant electronic data capture tool. 22 nce samples are collected and treated by the different laboratories, several million omics data are generated by using selected technologies and facilities and stored in a dedicated secure database, including deep-immunophenotyping data (circulating leucocyte subpopulations identified by flow cytometry, as an extension of our standardised Open access method for regulatory T cell monitoring  23  ), proteomic, transcriptomic, T cell receptor repertoire and microbiome data. All the information is centralised on secured servers hosted by the investigator laboratory.
Data analysis strategy Since TRANSIMMUNOM protocol is an open study, we will start analysing data progressively in order to first validate the methodologies selected (pilot study) and second to ensure sample quality and data robustness. Classical statistical analyses and modelling as well as integrative analyses will be used to identify potential biomarkers of disease as well as new therapeutic targets. As a general design, the analysis approach consist in (1) performing quality control on all the data collected using dedicated tools and solutions publicly available (including clinical data through a thorough eCRF monitoring and validation); (2) implementing supervised and classical statistical methods mastered in the team,  24 25  we will analyse parameter modulation (such as protein or gene expression level for cytokines and transcriptome, mean fluorescence intensity or percentage for immunophenotyping, diversity for microbiome and T cell receptor repertoire); (3) in parallel, we will implement unsupervised modelling methods publicly available or developed in the laboratory  24 26-33  in order to identify more complex or hidden parameter modulations within each type of data sets, such as signature discovery  29 32 34  and finally (4) once we will have complete multiparametric data sets for each subject, we will perform integrated analysis using methods relying on Regularised Generalised Canonical Correlation Analysis and variations,  [35] [36] [37] [38] [39]  dedicated to the analysis of structured data sets with the aim of deciphering their relationships.
We describe here a multidisciplinary approach applied to the design, implementation and analysis of a trans-AIDs observational protocol. Based on translational medicine, a CMT designed an innovative protocol that allowed the inclusion of patients currently followed up in different clinical departments and ensured the completeness and standardisation of data collection and integration. The centralisation of the omics analysis and the standardisation of the technics (flow cytometry panel, molecular biology) provided a uniformity of results. The CMT, including the CEC, recruited researchers from different fields to select, design and integrate stateof-the-art standards in clinical medicine, medical biology measurements and science. All the data collected were finally integrated in an e-CRF (database) so as to share information between all the CMT members.
1] [42] [43] [44]  TRANSIMMUNOM has the potential to provide fundamental knowledge about the pathophysiology of AIDs and to redefine their nosology. Indeed, we and others already showed the power of systems immunology to better understand biological processes associated with autoimmune and AID. 24 26 28 30 45-49 1] [52]  With the advance of high-throughput data modelling and integrative analyses, it has been already shown that blood can be used to follow and even predict biological process undergoing in distant tissue. 34 46 53-55 We have no doubt that with the unprecedented endeavour and deep clinical and immune profiling to be achieved in TRANSIMMUNOM, we will contribute to identify new disease biomarkers and possibly new therapeutic targets.
Acknowledgements All the authors thank the patients for having accepted to enter in this clinical protocol. We thank all the staff members involved in TRANSIMMUNOM protocol:  Florence Levenez  and  Mylène Robinet  of  INRA  for their involvement in microbiota collection and analyses;  Camille Louvrier  and  Fawaz Awad  for DNA and  RNA extraction in the Genetics Laboratory at the Trousseau Hospital ;  Michelle Barbie ,  Cornelia Degbe ,  Nathalie Fery ,  Sylvain Porrot ,  Anne-Laure Raveu  and  Alexandra Roux  for the management of immunological samples in the Biotherapy Department;  Adela Mekouo-Tagne  and  Olivia Tran  for the monitoring and the regulatory aspects of the study;  Anne-Marie Chenier ,  Eric Dasque ,  Edith Guilloux ,  Françoise Jouhannet ,  Celine Kocher ,  Laurence Koehl ,  Martine Lebot  from  Pitié-Salpêtrière CIC  and the nurses of the  Rheumatology Department of the Saint Antoine Hospital  for management of the participants.