dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluorescence in situ hybridization (FISH) Metaphase spreads for FISH were prepared from patient lymphocytes as described elsewhere  [44]. BAC clones were selected from the Ensembl database (http://www. ensembl. org) or the UCSC Human Genome Browser (http://genome. ucsc. edu) and ordered from the BACPAC resources centre (Children's Hospital Oakland Institute). For the initial mapping of the clones, DNA was isolated using a rapid alkaline lysis miniprep method (Qiagen mini/midi plasmid kit). Probes were labeled with biotin-16-dUTP or digoxigenin-11-dUTP (Roche) by nick translation. Probe labelling, DNA hybridization and antibody detection were carried out as described previously  [45]. Following hybridization, slides were mounted with a drop of Vectorshield antifadent containing DAPI (Sigma).
Mutation analysis of the FOXC1 locus An apparently identical 131 kb deletion (chr6:1,543,591-1,675,085; hg18) encompassing FOXC1 was identified as a de novo occurrence in two unrelated individuals 1449 and 1246 (Table  1 , Fig  3 ). Each of these deletions had been confirmed in an independent UK laboratory using an alternative method. Furthermore, the two individuals were shown to be distinct based on their aCGH profile of genome-wide copy number variants (data not shown). We then screened FOXC1 in our cohort by direct sequencing. Two individuals were found to carry missense mutations in the FOXC1 fork-head domain ( ). In both individuals, the mutations were absent from the unaffected parents and had most likely occurred de novo (Fig  3 ). The amino acid substitution p. (Pro79Thr) has been reported previously in a family with classical Axenfeld-Rieger syndrome and the mutant protein has impaired nuclear localization and transactivation activity  [46].
Bolidomonas is a genus of picoplanktonic flagellated algae that is closely related to diatoms. Triparma laevis, a species belonging to the Parmales, which are small cells with a siliceous covering, has been shown to form a monophyletic group with Bolidomonas. We isolated several novel strains of Bolidophyceae that have permitted further exploration of the diversity of this group using nuclear, plastidial and mitochondrial genes. The resulting phylogenetic data led us to formally emend the taxonomy of this group to include the Parmales within the Bolidophyceae, to combine Bolidomonas within Triparma and to define a novel species, Triparma eleuthera sp. nov. The global distribution of Bolidophyceae was then assessed using environmental sequences available in public databases, as well as a large 18S rRNA V9 metabarcode data set from the Tara Oceans expedition.
Environmental sequences A reference data set of near-complete sequences of genes coding for nuclear 18S rRNA, plastid 16S rRNA gene, rbcL, nad1 and ITS (ITS1+5. 8S rDNA +ITS2) sequences from Bolidophyceae was compiled. In particular, we retrieved all sequences assigned as 'Bolidophyceae' in the PR2 database for nuclear 18S rRNA  (Guillou et al. , 2013)  and in the PhytoRef database for plastid 16S rRNA  (Decelle et al. , 2015). This data set was used to identify similar environmental sequences in the GenBank (release 206. 0; February 2015) and CAMERA (http://camera. calit2. net/-note that this service no longer exists but the data are available on iMicrobe at http://data. imicrobe. us/) databases using MegaBLAST and BLAST (e-value e -10 ). For 18S rRNA, only Genbank sequences longer than 800 bp were kept for analysis (Supplementary Table  S2 ).
For CAMERA, only data retrieved from the 'all 454 reads' database-matched Bolidophyceae sequences were used. The nuclear 18S and plastid 16S rRNA environmental sequences were manually checked for chimeras. Two subsequences of 300 bp were extracted from the 5′ and 3′ ends, respectively, and assigned to a taxonomic group based on BLAST searches against GenBank or PhytoRef. Sequences that presented conflicting taxonomic assignation between the two parts were examined in detail to confirm their chimerical nature and not considered further (Supplementary Table  S2 ). Nuclear 18S rRNA sequences were clustered at 99% similarity with CD-HIT-EST  (Li and Godzik, 2006)  to obtain OTUs. OTUs with representative sequences on the 18S rRNA phylogenetic tree based on full-length sequences (see below) were annotated following the clades described (Supplementary Table  S2 ).
(2015) , respectively Metabarcode sequences for each data set were clustered into OTUs using the SWARM software  (Mahé et al. , 2014). Each OTU corresponds to a set of sequences differing by one base at most. For the present analysis, we only considered OTUs that contained at least 10 reads and that had a BLAST similarity higher than 80% to an 18S rRNA sequence from GenBank. OTUs were assigned to a taxonomic group based on ggsearch against an annotated eukaryotic V9 database  (de Vargas et al. , 2015)  derived from the PR 2 database  (Guillou et al. , 2013) , which provides an eight-level taxonomic hierarchy (kingdom, superdivision, division, class, order, family, genus, species). Taxa were regrouped at the class level and we extracted the 36 OTUs that were classified as Bolidophyceae-and-relatives (Supplementary Table  S4 ).
We compared the contribution of Bolidophyceae metabarcodes to the total number of 'photosynthetic' metabarcodes. Groups considered as photosynthetic included Chlorophyta, Rhodophyta, Cryptophyta, Haptophyta, Ochrophyta (i. e. , photosynthetic heterokonts such as diatoms), but not dinoflagellates for which the differentiation of photosynthetic vs heterotrophic species is highly complex. Tara Oceans data have been deposited to the International Nucleotide Sequence Database Collaboration and are accessible at http://doi. pangaea. de/10. 1594/PANGAEA. 843017 and 843022. Analyses, statistics and graphics of the Tara Oceans data were performed with the R software using the following libraries: ggplot2, mapproj, maps, mapdata, reshape2, scales and gridExtra  (R Core Team, 2015).
pacifica clade, which included two environmental sequences from the Atlantic and Pacific Oceans. Finally, T. mediterranea formed the most basal group together with one environmental sequence from the Gulf Stream. A number of environmental 18S rRNA gene sequences retrieved from PR 2 /GenBank according to their affiliation with Bolidophyceae did not group with Triparma sequences and formed two distinct clades (env. I and II) basal to the Bolidophyceae. These sequences originated from a range of environments including freshwater and marine sediments (Figure  2 ). The monophyly of these clades with the Bolidophyceae was supported by neighbor-joining bootstrap, ML and Bayesian analyses. In contrast to the situation for the 18S rRNA gene, all other molecular markers (plastid 16S rRNA (Figure  3 ), plastid rbcL (Supplementary Figure  S4 ), ITS rRNA (Supplementary Figure  S5 ), mitochondrial nad1 (Supplementary Figure  S6 )) revealed the presence of two distinct subclades within the silicified Triparma species.
Oceanic distribution from sequences available in public databases To determine the oceanic distribution of Bolidophyceae, we first searched for similar sequences in GenBank and in PR 2  (Guillou et al. , 2013)  as well as PhytoRef  (Decelle et al. , 2015) , which contain annotated eukaryotic nuclear 18S and plastid 16S rRNA sequences, respectively, as well as CAMERA, which contains large-scale environmental metagenomic data sets (Supplementary Table  S2 ). Only environmental sequences from nuclear 18S and plastid 16S rRNA genes were retrieved from these databases. The absence of rbcL, ITS or nad1 sequences in GenBank is explained by the fact that these genes are typically not used as molecular markers in environmental diversity studies. The data retrieved from CAMERA consisted of 135 reads that were reduced to 32 reads closely related to Bolidophyceae after careful checking by alignment with the full sequences used for the phylogenetic analysis and exclusion of reads without information on sample location.
The last abundant OTU (no. 6 or 'B. uncult 6') was present at a few stations in the Indian Ocean near South Africa and in the Atlantic Ocean off Brazil where it dominated (Figures  4b  and 5a ). It was much more prevalent at the DCM and was dominant at the same stations where it dominated in surface waters (Figures  4c  and 5b ). These two OTUs appeared to have a narrower temperature ranges than the four dominant OTUs (Supplementary Figure  S7 ). Finally, OTU no. 11 matching an Antarctica GenBank sequence, represented in Figure  2  by the    S4 ) have been regrouped in the category 'B. others'. 0 0 1 2 0 1 9 0 1 1 1 1 2 2 1 3 2 1 4 2 1 5 2 1 0 1 2 3 4 7 9 1 1 6 1 8 1 0 2 2 2 environmental sequence clone SGYI402 (KJ758075), was most abundant at Stn TARA_085, which was the southern-most station sampled by the Tara Oceans expedition.
, 2014; Luria et al. , 2014; Taylor and Cunliffe, 2014). The Tara Oceans metabarcoding data set also allows assessment of the distribution of the four phylogenetic clades that can be distinguished based on very clear 18S rRNA signatures. Two OTUs are clearly ubiquitous, T. eleuthera and T. pacifica. The former has been isolated several times from a wide range of oceanic locations (Pacific and Atlantic Oceans, Mediterranean Sea; Table  1 ), while in contrast T. pacifica has been isolated from a single cruise in the Pacific Ocean (OLIPAC in 1994; Table  1 ). T. mediterranea is particularly well named as this species dominates in surface waters of the Mediterranean Sea (Figures  4  and 5 ), the only region from which it has been isolated into culture, during a single cruise and at a single station.
Therefore, sequences found in tropical waters could correspond to Triparma species for which we have no isolates such as T. laevis subsp. mexicana or T. retinervis  (Silver et al. , 1980; Kosman et al. , 1993; Bravo-Sierra and Hernández-Becerril, 2003). The genetic diversity observed for the metabarcodes with no matching GenBank sequences could correspond to uncultured taxa, either among the silicified forms for which many morphotypes, in particular corresponding to the genera Tetraparma and Pentalamina, have not yet been cultured. These unknown metabarcodes could also correspond to flagellated forms previously classified as Bolidomonas, which are also quite difficult to bring into culture as demonstrated by the fact that T. mediterranea has only be isolated once, almost 20 years ago. Some of these metabarcodes could be more abundant in areas under-represented in this data set such as polar regions, as suggested by OTU no.
Acknowledgements We thank  Masanobu Kawachi  for help with the SEM pictures and  Yukiko Taniuchi  for help in sequencing the nad1 gene. Two anonymous referees provided very useful comments. Ian Probert provided invaluable help and advice for the taxonomic treatment. Financial support for this work was provided by  Grants-in-Aid for Scientific Research   22657027 ,  23370046 ,  26291085  and  15K14784  from the  Japan Society for the Promotion of Science (JSPS) , the  Canon Foundation and Core Research for Evolutional Science and Technology from Japan Science and Technology  as well as by the  European Union  programs MicroB3 (UE-contract-287589) and  MaCuMBA  ( FP7-KBBE-2012-6-311975 ), and the  French 'Investissements d'Avenir'  programs  OCEANOMICS  ( ANR-11-BTBR-0008 ) and  EMBRC-France.
0 was deemed to be of sufficient purity to be used for subsequent analyses. The total DNA of the colon contents was then diluted to 50 ng/μ l and used in the preparation of amplicons for high-throughput sequencing of microbial 16S rRNA. Conventional PCR was performed to amplify the V4 regions of the 16S rRNA gene using universal primers 515F (5′ -GTG CCA GCM GCC GCG GTA A-3′ ) and 806R (5′ -GGA CTA CHV GGG TWT CTA AT-3′ ). The reverse primer contained a 6-bp error-correcting barcode unique to each sample. DNA was amplified using the protocol described previously  62. DNA samples were sent to a commercial service provider (Novogene, Beijing, China) for amplicon pyrosequencing on an Illumina MiSeq platform according to the manufacturer's instruction  63. Microbial community analysis. Raw data were screened and assembled by QIIME  64  and FLASH  63  software packages.
The reads flagged as chimeras were removed to form the "effective sequences" collection for each sample. QIIME software package (http://qiime. org/) and UPARSE pipeline (http://drive5. com/uparse) were used for analysis of the sequences and determination of the Operational Taxonomic Units (OTUs) with an identity threshold of 97%. Meanwhile, we picked a representative sequence for each OTU and used the RDP classifier  65  to assign taxonomic data to each representative sequence. Taxonomy classifications were assigned against RDP Classifier (Version 2. 2, http://sourceforge. net/projects/rdpclassifier/)  65 and the Greengenes database (http:// greengenes. lbl. gov/cgi-bin/nph-index. cgi)  66. Alpha diversity values for colon bacterial communities of pregnant Huanjiang mini-pigs were obtained using abundance-based coverage estimator (ACE), bias-corrected Chao richness estimator, Shannon index, Simpson index, and Good's coverage, at a sequence depth of 31,460 for all samples.
Statistical analysis. The alpha diversity indices of bacterial communities, the apparent relative abundances at phylum and genus levels, as well as colon metabolites (SCFA, NH 3 -N, indole, skatole, and bioamines) were analysed in a completely randomised design with the GLM procedure of SAS (SAS Institute, Inc. , Cary, NC). The PCoA of overall diversity of microbial communities based on an un-weighted Unifrac metric was performed using Bray-Curtis distance to compare all samples. A study of the relationship among parameters was also carried out using Pearson's linear correlation coefficient. Phylum and genus at < 0. 5% relative abundance for both diets were excluded from all analyses. Differences with P values < 0. 05 were considered as statistically significant while a tendency was considered to exist at 0.
Acknowledgements The present work was jointly supported by grants from the  National Nature Science Foundation of China  ( 31270044 ,  31572421 ), " Western Light" key program for Talent Cultivation  from  Chinese Academy of Sciences , and  Chinese Academy of Sciences   Visiting Professorship for Senior International Scientists  ( F. Blachier ,  2013T2S0014 ). Key Programs of frontier scientific research of the  Chinese Academy of Sciences  ( QYZDY-SSW-SMC008 ),  National Key R&D Program  ( 2016YFD0501201 ), Project Supported by  Changsha City Science and Technology Program of China  ( k1508008-21 ). We would like to thank Editage [http://online. editage. cn/]for English language editing.
Subsequently, WES was performed in the trio (proband and parents): exons of DNA samples were captured and investigated as shown before with in-solution enrichment methodology (SureSelect Clinical Research Exome, Agilent, Massy, France) and NGS (Illumina HISEQ, Illumina, San Diego, CA, USA)  [16]. For all subjects, overall WES coverage of the captured regions was 96% and 90. 67% for 10x and 25x depth of coverage respectively resulting in a mean sequencing depth of 82x per base (S1-S3 Tables). Image analysis and base calling were performed with Real Time Analysis software (Illumina)  [16]. Genetic variation annotations were realized by an in-house pipeline (Integra-Gen), and results were provided per sample or family in tabulated text files. Stringent filtering criteria were used to select most likely pathogenic variant(s): only nonsense, missense, splice site variants or small deletions or insertions (InDels) with a minor allelic frequency 0.
005 in Exome Variants Server (EVS, http://evs. gs. washington. edu/EVS/), HapMap (http://hapmap. ncbi. nlm. nih. gov/), 1000Genomes (http://www. 1000genomes. org/) and Exome Aggregation blindness. org/), Prix de la Fondation de l'OEil (IA), and doctoral funding from the Ministère de l'Enseignement Superieur et de la Recherche (MESR) (http://www. enseignementsup-recherche. gouv. fr/) (CM). IntegraGen SA (Genopole, Campus, Evry, Paris, France) provided support in the form of salaries for authors SB, ML and JPS, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. Competing Interests: The commercial affiliation (IntegraGen SA, Genopole, Campus, Evry, Paris, France) does not alter our adherence to PLOS ONE policies on sharing data and materials.
2, Forward: 5'-GAGCCCA GAGAGCAGGTG-3' and Reverse: 5'-GGAGCTGGACGGGGCTG-3') (Sigma, Saint Quentin Fallavier, France) at a concentration of 10 μM each with 1. 5 mM MgCl 2 (Solis BioDyne, Tartu, Estonie), 3x S solution (Solis BioDyne), 1x B2 buffer (Solis BioDyne), 0. 004 U of a polymerase (Hot Fire Polymerase, Solis BioDyne) and 0. 2 mM dNTPs (Solis BioDyne) with an annealing temperature of 60˚C and as previously described  [11]. The novel GNAT1 nonsense variant identified in this study has been deposited in dbSNP database (https://www. ncbi. nlm. nih. gov/ snp) prior to publication. A cohort of additional 384 probands with RCD was studied by targeted NGS, including GNAT1 as previously described with an updated panel covering 195 different genes implicated in RCDs  [11, 15].
Tridimensional structure of GNAT1 To identify GNAT1 proteins previously crystallized, BLAST searches (protein Basic Local Alignment Search Tool, BLAST, http://blast. ncbi. nlm. nih. gov/Blast. cgi) were performed against the Protein Data Bank (PDB, http://www. rcsb. org/pdb/home/home. do) using the GNAT1 protein sequence (human GNAT1 reference sequence NP_653082. 1) as a query. The sequences (Bovine NP_851365. 1 GNAT1, human, wild-type and the two RCD mutant forms from NP_653082. 1, and the crystallized bovine GNAT1 (1TND, PDB ID)) were then aligned using Clustal Omega (http://www. ebi. ac. uk/Tools/msa/clustalo/). The three GNAT1 3D-structures, the full-length and the two truncated mutants of NP_653082. 1, were predicted (Protein Homology/analogY Recognition Engine V2. 0, Phyre 2 , http://www.
sbg. bio. ic. ac. uk/phyre2/ html/page. cgi?id=index,  [17] ; Iterative Threading ASSEmbly Refinement, I-TASSER, http:// zhanglab. ccmb. med. umich. edu/I-TASSER/, [18] ; TM-Align, http://zhanglab. ccmb. med. umich. edu/TM-align/, [19] ). The PyMOL Molecular Graphics System, Version 1. 7. x Schro ¨dinger, LLC, was used to model GNAT1 interactions with GDP (PDB code 1GOT), GTP (PDB code 1TND), and with RHO (PDB code 4A4M).
Three additional markers on the Y chromosome were genetically divergent between the father and affected son (S6 Table ). Together these results may suggest non-paternity. Further haplotyping was not performed in absence of consent for such a test. Due to numerous probably disease-causing homozygous variants found in the affected boy, homozygosity mapping was performed using the WES data from the affected boy and the unaffected mother to identify large homozygous regions of more than 30 Mb (S2 Methods) and reported in S7 Table. Some homozygous variants identified in the affected boy after filtering were in large homozygous region (S4 Table ): interestingly, the GNAT1 nonsense variant is localized in a large homozygous region (43,6 Mb; S4 and S7 Tables), supporting its involvement in the disease.
Acknowledgments The authors are thankful to the patient and family members participating in the study, clinical staff from the  Centre d'Investigation Clinique CIC1438  and  Katia Marazova  for her help with language editing. DNA samples included in this study originate from NeuroSensCol DNA bank, part of the  BioCollections network  for research in neuroscience (PI:  JA Sahel , coPI  I Audo , partner with  Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts , INSERM and CNRS).
Assembly of transcripts As no reference transcriptome is currently available for T. officinale, a de novo reference was assembled using all 40 libraries sequenced. Raw reads were first trimmed for adaptors and low quality reads (Phred score < 33) using the fastq-mcf tool from ea-utils  [39]. Additionally, the first 10 nucleotides of all reads (both reverse and forward) were trimmed using seqtk (https://github. com/lh3/seqtk). This is known to improve assembly of full length de novo transcripts  [40]. Assembly was performed using Trinity version r20140413  [41] , using default parameters with a minimum kmer coverage of two to get rid of possible sequencing errors.
Annotations and gene ontology Transcripts were annotated using BLAST, KEGG and SwissProt protein (http://web. expasy. org/) databases (BLASTx, E < 1e-05), as well as the Arabidopsis thaliana Protein Coding Sequence database (BLASTn, E < 1e-05). Output files were parsed to retain the best blast hit (best E-value) for each transcript with more than one hit. The Blast2Go suite was then used to generate gene ontology terms based on the BLAST outputs  [42]. Strict parameters were used allowing a BLAST high-scoring segment pair length of 33 and a minimum coverage of the query of 33. Gene ontology enrichment analyses (Fisher's test on the most specific ontologies, adjusted p-value < 1e-05, following recommendations from  [43] ) were also conducted using the Blast2Go suite for the set of differentially expressed genes (DEGs) against a background of all de novo assembled genes.
Mapping and identification of differentially expressed genes Reads from each library were mapped against the new reference transcriptome using Bowtie  [44]  with default parameters. The software RSEM  [45]  was also used to count the number of reads matching each putative gene. To perform the differential expression analysis, the Bioconductor DESeq package was used on raw count numbers  [46]. Both experiments were analyzed separately giving more confidence into overlapping DEGs. Filtering was performed over all counts, removing the lowest 40th percentile of transcripts based on mapped read counts  [47]. Using DESeq, samples were normalized and differential expression between accessions was tested for each transcript separately using a statistical model based on the negative binomial distribution (see  [46] ).
More precisely, two models are specified: the full model regresses gene expression on accessions while the reduced model tests for the null hypothesis. For each gene, the generalized linear model is fitted according to the two models which are then compare to infer whether accessions improves the fit and hence has significant effect. Transcripts showing a significant accession effect after correcting for multiple testing at a FDR threshold of 0. 05  [48]  were considered as differentially expressed. Principal Coordinate Analyses (PCoA) were performed using GenAlEx  [49] , based on pairwise Euclidean distances as calculated by DESeq from normalized read counts. Additionally, broad-sense heritability (H 2 ) was estimated for all genes as the variance component due to the accession effect divided by the total variance (accessions plus residual variance components).
Gene network analysis To identify and visualize networks and highly connected genes (which interact indirectly through RNA and protein expression products with many other genes) among the list of DEGs, Virtual Plant 1. 3 was used (www. virtualplant. org). We were able to detect and quantify known molecular interactions in the different sets of DEGs previously found from public databases using A. thaliana annotations (see  [50]  for details on databases). More specifically, the tool "network statistics" was used to generate tables of the most highly connected nodes in the network and to pinpoint ecologically relevant genes and functions. Functional annotations of all highly connected genes were then retrieved from the TAIR website (www. arabidopsis. com).