dataesr/scientific-paragraphs-categorization huggingface dataset:
7 LogMAR": best corrected visual acuity 0. 7 LogMAR (HP:0030560). "centrocecal": centrocecal scotoma (HP:0000576); "RNFL two or more": mean retinal nerve fiber layer thinning in 2 or more quadrants; "MRI": brain MRI performed; "hemeralopia": hemeralopia (HP:0012047); "photophobia": photophobia (HP:0000613). Data as of October 12, 2018 Fig. 2  Distribution of the 516 unique genomic variants in the LOVD OPA1 database (compact view). Eighteen large rearrangements (eleven deletions, six duplications, and one deletion-insertion) are shown as extended bars with rafters, substitutions as black bars, deletions as blue bars, insertions as green bars, and duplications as orange bars. At the top are reported the genomic coordinates on human chromosome 3 (assembly GRCh37/hg19), and OPA1 transcript variants 1 and 8 structure in navy blue with alternative exons in pink, including exon numbering.
The full view detailing the names of each mutation is available in Additional file 2. Adapted from UCSC Genome Browser (http://genome. ucsc. edu) with the LOVD OPA1 database custom track; data as of  October 12, 2018  Database (gnomAD), which is the aggregation of the high-quality exome (protein-coding region) DNA sequence data for tens of thousands of individuals  [36]. However it was decided not to add these variants as a new record, but only to indicate the frequency reported in gnomAD for each variant present in the server, in order not to flood the LSDBs with data not related to a phenotype. This information is particularly useful at the time of the curation, as well as to assess its relevance. In total, 7% of the unique variants  (36)  in our database are listed with a frequency in gnomAD.
Interestingly, we have assigned a "(probably) non-pathogenic" or "variant of unknown significance" status to all variants with a frequency greater than 0. 001% in gnomAD; all the variants we labelled as pathogenic have a very rare frequency in gnomAD, at most nearly 0. 001% (13 out of about 13,000 alleles) for the c. 239A > G variant in exon 2, which induces a p. (Tyr80Cys) missense mutation. This lastmentioned variant has been reported twice independently as pathogenic in our database, which is a strong argument for concluding to the pathogenicity of a missense mutation; conversely, it is listed without clinical significance in the NCBI dbSNP (Build 151, dbSNP# rs151103940)  [37] , highlighting the increased accuracy of the LSDB approach as it applies to our database.
Clinico-ophthalmological relevance To date, the database includes 831 patients (182 males, 131 females, and 518 patients of unspecified gender). Among these, 697 patients had isolated DOA, 47 had DOA+ (including 12 with hearing loss), and 83 were asymptomatic or unclassified. In addition, four of the patients were reported with phenotypes that are not referenced as being associated with OPA1, i. e. ocular albinism type I (OA1; MIM# 300500); polyneuropathy, hearing loss, ataxia, retinitis pigmentosa, and cataract (PHARC; MIM# 612674); spinocerebellar ataxia-5 (SCA5; MIM# 600224); and autosomal recessive spastic paraplegia-18 (SPG18, MIM# 611225). The database includes a new set of full clinical data for 88 patients consulting at our Ophthalmological Center, in addition to the 30 patients already described in 2015  [35] , as well as 60 patients from our Molecular Genetics Laboratory, now representing all the data (178 patients) available from our Center, along with data from 232 patients, retrieved by the curator from publications.
In particular, all the published data from the research teams of our European network on inherited optic neuropathies (France, Germany, Italy, United Kingdom) have been integrated; their unpublished data, and those of any other team that may emerge, will be gradually integrated. Overall, since the last major update in 2015  [35] , the number of patients in our database has more than doubled, increasing from 328 to 831, with a larger proportion of patients for whom full clinical data is now available, increasing from about 10 % to almost half. Interestingly, 30% of the patients in the database are unpublished in the literature, i. e. have been submitted to our database only, 178 (two thirds) submitted by our Center, France, and 74 (a third) from abroad, outside France (six independent submitters from Germany, Netherlands, United Kingdom and the USA).
Central database relevance The majority of databases, which are central, encompass all the genes of an organism, as in sequence databases  [38, 39]  or in databases oriented towards non-pathogenic variations  [36, 37]. In contrast, databases reporting pathogenic variations, i. e. the so-called locus-, gene-or disease-specific databases (LSDB, GSDB or DSDB), have proved to be the most complete  [40]  since they benefit from the participation of a curator who is a referent specialist for the gene or disease Fig. 3  Distribution of the different effects on the protein of the OPA1 variants considered pathogenic. Other consequences (5%) include: synonymous  (11) , no protein is produced (5), duplication (3), and extension  (1). Data as of  October 12, 2018  considered. Unfortunately, these databases are often based on isolated initiatives, using various interfaces hosted on different servers, rendering their interoperability and intuitive use rather difficult.
Therefore, the Human Variome Project currently favors the centralization of LSDBs at https://databases. lovd. nl/shared [41, 42]. This centralization is the major asset of the work reported here, along with the exhaustive inclusion of patients from our ophthalmological center and data collected from the literature as described above. The implementation of phenotypic descriptions of all patients from the database using the Human Phenotype Ontology (HPO)  [43]  offers a standard vocabulary for referencing phenotypic abnormalities. Figure  4  shows an example of the hierarchy of terms used for visual acuity. Genomic medicine calls for the precise definition of phenotypic variations  [45] [46] [47]  and descriptions of human disease using HPO annotations are key elements in several algorithms designed for molecular diagnosis and genetic research.
Ongoing developments Some OPA1 patients, already referenced, carry a second mutation in another gene, which it is now technically possible to include in the databank. This would allow the inclusion of other genes involved in neurological diseases affecting mitochondrial dynamics and bioenergetics. For instance, peripheral neuropathy has been linked to OPA1 mutations, and optic neuropathy to MFN2 (MIM# 608507) mutations, thus revealing the close proximity of the diseases  [48]. Thus, we will integrate MFN2, responsible for Charcot-Marie-Tooth neuropathy type 2A (MIM# 609260, 617,087)  [49] , as well as genes Fig. 4  Tree view of the Human Phenotype Ontology term "Abnormal best corrected visual acuity test" (HP:0030532). In the Ontology Lookup Service  [44]. The term is highlighted, superclasses indicated above, subclasses indicated below.
Conclusion The integration of the OPA1 database into the central LOVD database means that OPA1 shares a common platform with 22,981 other human genes as referenced to date in databases. lovd. nl/shared. This major step constitutes a computational bridge between genome biology and clinical medicine with a common vocabulary, making it possible to interface phenotypic profiles of OPA1 patients with those involving mutations in other genes or clinical presentations. It also contributes to a better understanding of polygenic diseases by connecting a patient to a large number of genes screened, as highthroughput sequencing now routinely allows, with each gene being validated by a specialized curator. Finally, the database is directly queried by software suites dedicated to the annotation, filtering, and exploration of genomic variations, such as Alamut® (Interactive Biosoftware, France/SOPHiA GENETICS, Saint Sulpice, Switzerland).
Nomenclature All names, symbols, and OMIM numbers were checked for correspondence with currently official names indicated by the Human Genome Organization (HUGO) Gene Nomenclature Committee  [54]  and the Online Mendelian Inheritance in Man database -OMIM®  [55, 56]. The phenotype descriptions are based on HPO  [43] , indicating the HPO term identifier. OPA1 variants are described according to the OPA1 transcript variant 8 (RefSeq: NM_130837. 2), representing the longest transcript. Compared to transcript variant 1 (RefSeq: NM_015560. 2), the original transcript identified, transcript variant 8, based on an alternate splice pattern characterized by Delettre et al. [11] , contains two additional exons, 4b and 5b. However, it maintains the same reading frame encoding an isoform (8) of 1015 amino acids (aa).
Implementation of the database Our database has migrated to the "Global Variome shared Leiden Open-source Variation Database (LOVD)" currently running under LOVD v. 3. 0 Build 21  [58] , following the guidelines for locus-specific databases (LSDBs)  [59]  and hosted under the responsibility of the Global Variome/Human Variome Project  [42, 60]. The database for OPA1 mutations includes a total of 21 items characterizing the DNA variants, 10 items characterizing the transcript variants (cDNA) (Fig. 1a ), 7 items characterizing the molecular screenings (Fig. 1b ), 14 items characterizing the individuals (Fig. 1c ), and lastly, 24 items characterizing the phenotypes (Fig. 1d ). A standardized description of the clinical and molecular items is set up using drop-down lists or list boxes with predefined variables.
The clinical features are based on a large panel of symptoms encountered in ophthalmological, mitochondrial, and neurological diseases. The OPA1 database reviews clinical and molecular data from patients carrying OPA1 variants published in peer-reviewed literature, as well as unpublished contributions that are directly submitted. While most variants can be described in terms of the latest update of the standard nomenclature, some inaccuracies may persist because gene anomalies discovered earlier might have been named according to a convention now out of use. Eventually, the "DNA published" field of the page dedicated to each variant (Fig. 1b ) indicates whether the published name of the mutation has been modified by the curator. The OPA1 LSDB website requires full compliance with the rules set out above for the description of sequence variants in order to provide uniform and comparable data.
Data collection The nomenclature of all causative variants in the OPA1 database, published in 2015  [33] , was reexamined. New causative variants were also searched for and collected from the literature published to date (October 12, 2018), using the NCBI PubMed search tool  [61]. The positions of variants in the reference transcripts were determined and updated according to the HGVS nomenclature version 2. 0  [57]. Correct naming at the nucleotide and amino acid levels were verified, and reestablished when necessary, using the Mutalyzer 2. 0. 28 Syntax Checker  [62]. Exon numbering was updated with respect to the longest reference sequence (transcript variant 8) together with the originally identified reference sequence (transcript variant 1). Information on the number of patients carrying each causative variant, as well as their geographical origins and the homo-or heterozygosity, was determined from the original or review papers, as well as from data collected during our local ophthalmology consultations.
Data access and submission The OPA1 database is an open database allowing any researcher or clinician to consult the contents freely without prior registration, or to contribute new data after due registration to ensure traceability. The database can be accessed on the World Wide Web at: https://www. lovd. nl/OPA1 (through the Global Variome shared LOVD server; or through the MITOchondrial DYNamics variation portal at: http://opa1. mitodyn. org). The data can also be retrieved via an application programming interface (API), i. e. a web service allowing simple queries and retrieval of basic gene and variant information (documentation available on the web page of the database); as well as serving as a public beacon in The Global Alliance for Genomics and Health Beacon Project  [63]. General information is available at the database home page.
Additional files Additional file 1: Variants listed in the OPA1 database that are unpublished in the literature (count: 149). (PDF 133 kb) Additional file 2: Distribution of the 516 unique genomic variants in the LOVD OPA1 database (full view). Eighteen large rearrangements (eleven deletions, six duplications, and one deletion-insertion) are shown as extended bars with rafters, substitutions as black bars, deletions as blue bars, insertions as green bars, and duplications as orange bars. At the top are reported the genomic coordinates on human chromosome 3 (assembly GRCh37/hg19), and OPA1 transcript variants 1 and 8 structure in navy blue with alternative exons in pink, including exon numbering. Adapted from UCSC Genome Browser (http://genome. ucsc. edu) with the LOVD OPA1 database custom track; data as of  October 12, 2018.
Gammarids are amphipods found worldwide distributed in fresh and marine waters. They play an important role in aquatic ecosystems and are well established sentinel species in ecotoxicology. In this study, we sequenced the transcriptomes of a male individual and a female individual for seven different taxonomic groups belonging to the two genera Gammarus and Echinogammarus: Gammarus fossarum A, G. fossarum B, G. fossarum C, Gammarus wautieri, Gammarus pulex, Echinogammarus berilloni, and Echinogammarus marinus. These taxa were chosen to explore the molecular diversity of transcribed genes of genotyped individuals from these groups. Transcriptomes were de novo assembled and annotated. High-quality assembly was confirmed by BUSCO comparison against the Arthropod dataset. The 14 RNA-Seq-derived protein sequence databases proposed here will be a significant resource for proteogenomics studies of these ecotoxicologically relevant non-model organisms.
This approach was successful after developing a protein sequence database from an RNA-Seq transcriptome translated in all the possible reading frames. This proteogenomics concept was used to establish an extensive catalogue of protein sequences comprising 1873 mass-spectrometry-certified proteins, thus representing a significant amphipod proteomic resource  29. Despite this progress, molecular resources relating to gammarids remain scarce  31. No gammarid whole genome sequence was available until very recently, when a first-draft genome of Gammarus lacustris was released comprising 443,304 scaffolds  32. The genomes of two related amphipods, Parhyale hawaiensis  33  and Hyalella azteca  34  , have also been sequenced. RNA-Seq datasets are now available for P. hawaiensis  [35] [36] [37]  , Echinogammarus marinus  38  , Eogammarus possjeticus  39  , Gammarus fossarum  29  , Gammarus chevreuxi  40  , Gammarus pulex  41  , and Gammarus minus  42.
However, these datasets are not of equal quality in terms of mRNA sequence coverage, which is a crucial parameter for proteogenomics interpretation  43. They are assembled from mRNAs extracted from a pool of several animals or from specific tissues, and in some cases are no longer accessible as it is the case for E. marinus because the repository used no longer exists  44. The data presented in this article consist of assembled transcriptome sequences for 14 different gammarids, seven males and seven females, namely Gammarus fossarum A (Müller type A), G. fossarum B (Müller type B), G. fossarum C (Müller type C), Gammarus wautieri, Gammarus pulex, Echinogammarus berilloni, and Echinogammarus marinus. These transcriptomes were assembled and translated using the same pipelines (full length whole-organism mRNAs), and thus are of equivalent sequencing depth and quality across the different taxa studied.
Starting material was extracted from single animals to avoid sequence heterogeneity. The transcriptomes have been annotated to serve as reference protein sequence databases for proteogenomics studies involving these sentinel animals that will be soon conducted to gain more basic knowledge and thus improve how aquatic environmental risks are assessed. For these future studies, an interesting strategy could be to interpret MS/MS shotgun data first on the most appropriate specific single-organism database, and then perform a follow-up search on a multi-organism database. The transcriptomes presented here will also serve in comparative analyses to better define the molecular diversity amongst gammarids and will be a valuable sequence resource for future ecotoxicological studies.
PCR products were purified by ultrafiltration using the Nucleofast kit (Macherey-Nagel). Purified amplicons were prepared for sequencing using the BigDye Terminator v3. 1 kit (ThermoFisher), and then sequenced on a DNA analyser ABI 3730XL (ThermoFisher). Sequencing data were analysed using the Sequencher 5. 4. 6 program (Genecodes). COI sequences (freely available from figshare, YC02_COI sequences and phylogenetic tree  51  ) were aligned to build a phylogenetic tree including reference sequences from Weiss et al. 47  and Lagrue et al. 52. Using this phylogenetic tree (freely available from figshare, YC02_COI sequences and phylogenetic tree  51  ) it is possible to position the COI sequences of the Gammarus organisms selected for RNA sequencing in relation to the published reference sequences (SeaView software  53  ; BioNJ method based on J-C distance).
fossarum B and C specimens is about 17% in the Pollon River, it is very unlikely that this female does not belong to the G. fossarum C species. Dataset generation. Gammarids were placed in RNAlater (Sigma) and stored at 4 °C overnight. The RNAlater was then removed, and the organisms were snap frozen in liquid nitrogen and stored at -80 °C until RNA was extracted. Organisms were first homogenized in lysis buffer using a bead homogenizer and then RNAs were extracted using the Qiagen fibrous tissue kit (Qiagen). RNA quantity, quality and integrity were assessed by Nanodrop (Thermo Fisher) and Bioanalyzer (Agilent) analysis. RNA-Seq libraries were generated using the TruSeq stranded mRNA Sample Prep kit (Illumina). mRNA was purified using poly-(T) beads from 2 µg of each total RNA sample, then cleaved in segments of 155 bp on average (120-210 bp range).
Assessing assembly quality. Transcriptome quality was assessed using Transrate v1. 0. 1  55  , which generates standard metrics and remapping statistics. No reference protein sequences were used for the assessment with Transrate. The main metrics are shown in Table  2. To validate the quality of all the assemblies, BUSCO v2. 0 56 was used. The database used for BUSCO analyses was Arthropoda_odb9 which contains 1066 orthologous genes at the nearest taxon level (i. e. , Arthropods) available for Gammarus.
Annotation. For each sample, the transcripts were annotated using the Trinotate v3. 1. 1 annotation pipeline  54. The Swissprot database was used as the main database, and amphipod proteins referenced on Uniref were used as a custom database. Similarity searches were performed with Blastx and Blastp, with an e-value cutoff set at 1e-2. Results from these searches were then used to generate the annotation report with the same e-value cutoff.
Data Records Reads. Read sequences for each sample were deposited in the NCBI Sequence Reads Archive under accession Numbers SRR8089720 57 , SRR8089722-SRR8089725  [58] [59] [60] [61]  , and SRR8089727-SRR8089735  [62] [63] [64] [65] [66] [67] [68] [69] [70]  , as indicated in Table  3  alongside the corresponding Bioproject and Biosample codes. The FastQC results for the 14 samples are freely available from figshare (YC02 _QC data)  51. The data records are stored as 14 folders, each of which contain four folders per transcriptome. Transcriptomes. Transcriptome assemblies were deposited in the NCBI Transcriptome Shotgun Assembly Sequence Database. These data have been deposited in GenBank under identifiers GHCN01000000  71  , GHCP01 000000-GHCR01000000  [72] [73] [74]  , GHCT01000000-GHCZ01000000  [75] [76] [77] [78] [79] [80] [81]  , GHDA01000000-GHDC01000000  [82] [83] [84]  , as indicated in Table  3  alongside the corresponding Bioproject and Biosample codes.
Proteogenomics databases. GHCT01000000 SRR8089732 PRJNA497972 SAMN10259946 EGSM GHCU01000000 SRR8089733 PRJNA497972 SAMN10259947 EGUF GHCW01000000 SRR8089734 PRJNA497972 SAMN10259948 EGUM GHCV01000000 SRR8089735 PRJNA497972 SAMN10259949 GFAF GHCX01000000 SRR8089727 PRJNA497972 SAMN10259934 GFAM GHCY01000000 SRR8089728 PRJNA497972 SAMN10259935 GFBF GHCZ01000000 SRR8089729 PRJNA497972 SAMN10259936 GFBM GHDA01000000 SRR8089722 PRJNA497972 SAMN10259937 GFCF GHDC01000000 SRR8089723 PRJNA497972 SAMN10259938 GFCM GHDB01000000 SRR8089724 PRJNA497972 SAMN10259939 GPCF GHCP01000000 SRR8089725 PRJNA497972 SAMN10259940 GPCM GHCQ01000000 SRR8089720 PRJNA497972 SAMN10259941 GWF GHCR01000000 SRR8089730 PRJNA497972 SAMN10259944 GWM GHCN01000000 SRR8089731 PRJNA497972 SAMN10259945 Table  3. Accessions for the 14 transcriptomes.
Furthermore, considering the importance of lateralization reported above, we took into consideration the right and left hemisphere contribution to task completion at the word or syllable levels in the present work. Finally, we integrated the connectivity data provided by a resting-state acquisition to propose a comprehensive view of the plurimodal large-scale networks for phonological/word processing and their potential roles. To achieve the identification of plurimodal large-scale networks for word-list processing, a large population of 144 right-handers who completed word-list processing in the three modalities, production, reading and listening, during task-induced fMRI acquisition, was selected from the BIL&GIN database  (Mazoyer et al. 2016). In this sample of healthy right-handers, we (1) identified left brain regions showing both leftward joint activation and leftward joint asymmetry and right brain regions showing both rightward joint activation and rightward joint asymmetry during the three word-list tasks; (2) identified the network organization at play within the areas previously identified based on the hierarchical clustering of the BOLD temporal correlation measured during a resting-state acquisition completed in the same individuals; and finally, (3) conducted a comprehensive investigation of how these areas were modulated according to the task and integrate the present results into the literature to elucidate the identified supramodal word-list network's function/role.
Participants The present study included a sample of 144 right-handers balanced for sex (72 women) and picked from the BIL&GIN database, which is a multimodal imaging/ psychometric/genetic database specifically designed for studying the structural and functional neural correlates of brain lateralization  (Mazoyer et al. 2016). The selected participants had French as a mother tongue and were free from developmental disorders and neurological and psychiatric histories. A local ethics committee (CCPRB Basse-Normandie, France) approved the experimental protocol. The participants gave their informed, written consent and received an allowance for their participation. All subjects were free of brain abnormalities as assessed by an inspection of their structural T1-MRI scans by a trained radiologist.