dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was mainly supported by the  European Union   BEST 2. 0  project " Biopelagos " (Grant Number  1070-2015  to SPC), the  Province Nord (Grant Number   15C493 ) and the  DAFE-NC  (Grant Number  2019/DAFE/873 ), while the  "Ecole Doctorale du Pacifique" and "Université de la Nouvelle-Calédonie " provided A. R. with a PhD fellowship. CPER ( Contrat de Projet Etat-Région ) and the  FEDER (Fonds Européen de Développement Régional)  provided additional funding to the  IRMS of LIENSs laboratory  and the  IUF (Institut Universitaire de France)  is acknowledged for its support to  P. Bustamante  as a Senior Member. We wants to warmly thank the many collaborators and students involved in the fieldwork in New Caledonia, especially  M. Thibault ,  J. Allemand ,  E. Bourguet ,  M.
Previous characterizations of the T. forsythia virulence factors were mostly based on the American Type Culture Collection (ATCC) 43037 type strain employing wet-lab experimentation, whereas computational analyses of the virulence-related gene repertoire mostly used the genome sequence of strain FDC 92A2. Although FDC 92A2 was the first fully sequenced T. forsythia strain available  [19] , the resulting genome assembly was incorrectly labelled and deposited as ATCC 43037 in the National Center for Biotechnology Information (NCBI) databases. This discrepancy was not noticed by the research community until many years later. Because of inconsistent results and sequence mismatches, initially interpreted as sequencing errors or as misassemblies in the genomic reference, T. forsythia was sequenced again and a genuine genome assembly for ATCC 43037 was generated  [20].
Meanwhile, the strain attribution error has been corrected in the NCBI databases, but persists in other databases. The T. forsythia ATCC 43037 genome assembly published by Friedrich et al. was a draft genome assembly, consisting of 141 contigs with an N50 contig length of 110 kbp. Even though this has substantially improved the genomics resources available for T. forsythia, a more contiguous and more complete genome assembly is required for many analyses, especially for whole-genome comparative approaches. Furthermore, the genome assembly of strain FDC 92A2 remained in the NCBI databases as reference genome for T. forsythia due to its completeness. However, the cultivation of FDC 92A2 has been reported to be unreliable  [21] , so that ATCC 43037 will certainly continue to be the most widely used strain in research labs.
We compared our ATCC 43037 assembly to a published 15 kbp-long genomic sequence (GenBank accession KP715369) of the same T. forsythia strain  [18]  resulting in a conflicting alignment. About one half of the sequence published by Ksiazek et al. aligned to a non-terminal region in scaffold 1 and the other half aligned to a non-terminal region in scaffold 2 in our assembly. We carefully checked the sequencing reads that supported our connections and also mapped our reads to the 15-kbp sequence. Reduced read coverage was found in all breakpoint regions, but several thousands of connecting mate-pairs supported our version compared to only twenty mate-pairs that would confirm the continuity of the 15-kbp sequence (Fig. 1 ). When comparing the 15-kbp sequence to the published genome assemblies of T.
Comparative assessment of Tannerella virulence factors Currently available T. forsythia genomes contain 2600-2700 protein-coding genes, many of which lack functional annotation. The increasing wealth of knowledge contained in sequence databases may provide functional predictions for these genes in the future. At present, however, we may reveal candidate genes involved in pathogenesis by comparing complete genomes from strains of known pathogenic and non-pathogenic nature, even if their genes are not yet functionally annotated. Such an approach is especially interesting in the case of T. forsythia, as its cultivation requirements make a systematic knock-out approach very challenging. A number of genes have so far been suggested to be associated with the pathogenicity of T. forsythia  [18, [31] [32] [33].
BU063 genome included here. As it was the best bidirectional hit, it is likely that it represents a true mirolase ortholog. The corresponding gene (NCBI protein database accession WP_069175679. 1) is almost identical (97% identity at 99% coverage) with the gene reported by Ksiazek et al. which was described to share a high degree of similarity in the catalytic domain with KLIKK proteases, but lacks a signal peptide, lacks an Nterminal pro-fragment, and lacks the variable region that is characteristic of the C-terminal extension in KLIKK proteases. In contrast to previous reports  [28] , we found a best bidirectional hit for karilysin in the Tannerella sp. BU063 assembly, in this case with 53% identity over the entire length of the gene. Within the work of Beall et al. , only homology to the C-terminal part containing the secretion signal domain was reported.
Interestingly, a large fraction of the dissimilarity between T. forsythia karilysin, as described by Ksiazek et al. [18] , and the putative ortholog (NCBI protein database accession WP_083206853) identified in the gap-free Tannerella sp. BU063 genome assembly was found in regions other than the catalytic domain of the protein (Additional file 13: File S2). Further work will be required to determine whether this gene is a bona fide functional karilysin ortholog. In any case, the previous conclusion that KLIKK proteases are completely absent from the Tannerella sp. BU063 genome has to be revised. As potential targets for therapeutic strategies we would consider only those genes that are highly conserved in all T. forsythia strains, but absent or weakly conserved in the putative health-associated Tannerella sp.
Assembly scaffolding SOAPdenovo 2. 04  [49]  was used for scaffolding. The helper program finalFusion, also maintained by SOAPdenovo developers, was used to prepare the input contigs for applying the scaffolding steps (map-scaff) of the main program. The k-mer size parameter was varied in repetitions of this procedure (K =  33, 43, 45, 47, 49) , where K = 47 resulted in an assembly with the highest values for N50 scaffold length, and for the size of the largest scaffold, respectively. Gap-filling, as carried out by SOAPdenovo, was enabled by using the -F parameter. Results were assessed using QUAST v3. 2  [50] , additionally, critical links were validated by manual inspection of the mate-pairs supporting these links in IGV 2. 3. 68  [51, 52]  and by analysing the amount and mapping positions of mate-pairs supporting these links.
Whole-genome alignments Whole-genome alignments of more than two genomes were generated and visualized with Mauve (version snapshot 2015-02-13 build 0)  [55] , using the progressiveMauve algorithm with default parameters --seed-weight = 15 --gapopen = 400 --gap-extend = 30 --scoring-scheme = sp. The contigs of fragmented assemblies were ordered and oriented using the contiguous genome assembly of T. forsythia 92A2 as reference with Mauve's "reorder contigs" module prior to alignment. Additional alignments employing blastn  [56, 57]  were used for calculating the fraction of alignable regions to strain 92A2 per genome. Whole-genome alignments between ATCC 43037 and BU063 were carried out using the MUMmer 3. 23 software package  [58] , employing nucmer with default parameters --mumreference -b 200 --nobanded -c 65 --delta -D 5 -d 0.
12 --extend -g 90 -l 20 --optimize --simplify for aligning and mummerplot with the option --filter for creating Gnuplot scripts. Six-frame amino acid translation alignments between ATCC 43037 and BU063 were generated with promer with the parameters -mumreference -b 60 -c 20 -g 30 -l 6 -m 8 -× 2. Gunplot scripts were adapted manually with respect to aesthetics and readability and plotted with Gnuplot 4. 4 (http://www. gnuplot. info/).
Analysis of core-and accessory genomes The T. forsythia core and accessory genomes were compiled using components of the GET_HOMOLOGUES pipeline  [59]. All genome assemblies included in the analysis were downloaded as RefSeq gbff files from the NCBI ftp server (ftp://ftp. ncbi. nlm. nih. gov/genomes/) as input for get_homologues. pl. As starting point for further analysis, an all-vs-all blastp  [56, 57]  of all coding sequences (CDS) contained in the input files was performed in a batchwise manner, using the parameters -dbsize 100,000, 000 -seg yes -soft_masking true -evalue 0. 01 -outfmt 6 qseqid sseqid pident length qlen slen qstart qend sstart send evalue bitscore -max_target_seqs N (where N is the total number of sequences in the database used in that run). Based on the blastp results, clusters of putative orthologs were clustered with get_homologues.
The intersection of the cluster sets generated by the two different algorithms was extracted using com-pare_clusters. pl and used for further analysis. Orthology clusters containing genes from all T. forsythia genomes were extracted using the script parse_pangenome_ matrix. pl; the genes contained in these clusters constitute the core genome of T. forsythia. In an additional run, orthologs were required to be present in at least 80% of the T. forsythia genomes to become part of a relaxed form of the core genome, sometimes referred to as "soft core genome". Allowing an ortholog to be absent in one of the assemblies reduces the risk of incorrectly excluding bona fide core genes from the core genome due to annotation, assembly or sequencing errors, or the incomplete nature of some of the included assemblies.
Tannerella forsythia core and pan genome sizes were estimated based on random sampling by using only the T. forsythia genomes as input for get_homologues. pl with the additional parameter -c. Plots illustrating these estimations were generated with plot_pancore_matrix. pl, using the parameter -f core_Tettelin for the core genome plot and -f pan for the pangenome plot. Comparisons on the presence or absence of single genes were carried out using the script check_ BDBHs. pl. All these Perl scripts are part of the GET_HO-MOLOGUES pipeline.
Detection of putative pathogenicity islands Based on the results of the pan-genome analysis, putative pathogenicity islands were detected as follows: Genes that were found to be present in at least eight of the ten T. forsythia strains but absent from Tannerella sp. BU063 were considered to be T. forsythia-specific and assessed for their co-localization within the genome of strain 92A2 using the Perl script GeneClusterFinder. pl developed in this work. The script takes a file containing all annotated genes of the genome in tabular form (as can be downloaded from https://www. ncbi. nlm. nih. gov/ genome/proteins/11045?genome_assembly_id=231734) as reference input file, assigning sequential numbers to the genes sorted by position. This step is necessary, as the locus tags used by NCBI are not necessarily sequential.
Searching for glycosylation loci Putative glycosylation loci were defined as genomic regions where at least three predicted Gtfs or other putative glycosylation-related genes occur within a stretch of 15 consecutive genes. The current Reference Sequence (RefSeq) gene set for strain 92A2 was used as reference. The complete gene set was annotated using the carbohydrate-active enzyme specific annotation web server dbCAN  [62]. Additionally, conserved domains were annotated for all genes, using NCBI's CD-Search  [63] [64] [65] [66]  (parameters: database: CDD -53,069 PSSMs, expect value threshold 0. 01, compositioncorrected scoring on, low-complexity filter off, maximum number of hits 500, include retired sequences on). All genes that either were predicted as Gtfs by dbCAN or to contain a Gtf-associated conserved domain by CD-Search were combined and used for the subsequent steps.
Analysing the co-localization of these genes employed a custom Perl script implementing a sliding-window approach to find stretches of 15 consecutive genes encompassing at least three putative Gtfs. Redundant windows were removed and overlapping ones combined; the split region of the circular genome in the assembly was checked manually. Additional analysis on the presence or absence of the detected putative glycosylation loci in other members of the Bacteroidales order was performed using Gecko 3. 1  [67]. Organisms included in this analysis were chosen based on previous work  [11] , the RefSeq assembly versions of the corresponding genomes were downloaded from the NCBI ftp server as GenBank flat files (Additional file 4: Table  S4 ) and used as input for the script gecko3_gb_ to_transclust_to_cog.
py from the Gecko suite. First, this script was used to prepare a Blastp search (version 2. 2. 30+) by using the parameter -prepareSingleBlast. This sets up a database and a query FASTA file containing all CDS from all included genomes and executes the corresponding Blastp command with an E-value cut-off of 0. 01. The file containing the Blastp results as well as the query file described above were loaded into Transclust 1. 0  [68]. Clustering was performed using Best Hit (BeH) as cost model and a density parameter of 40. The results produced by Transclust were converted into the format required as input for Gecko, using the script gecko3_gb_ to_transclust_to_cog. py with the parameter -transclustTo-Cog. Whether a putative glycosylation locus or parts of it can be found in one of the included genomes apart from T.
Codon usage analysis Nc values  [69]  and GC3s values were calculated with CodonW using the parameters -all_indices -nomenu (http:// codonw. sourceforge. net//culong. html). Fasta files containing the nucleotide sequences of all CDS of the respective genome were downloaded from NCBI's ftp server and all CDS for which the "pseudo" qualifier was set to "true" were eliminated prior to the analysis. scCAI values and scnRCA values were calculated using the program scnRCA with the parameters -g true -d 2. 0 -p 1. 0 -m -1 and GenBank flat files as input files  [44, 45].
Availability of data and materials The improved genome assembly for T. forsythia ATCC 43037 has been deposited at DDBJ/ENA/GenBank under the accession VFJI00000000. The version described in this paper is version VFJI01000000. Mate-pairs of T. forsythia ATCC 43037 were deposited in the Sequence Read Archive under accession SRR9302598 (BioProject PRJNA548889, BioSample SAMN12058270).
Mutations in the cytoplasmic dynein 1 heavy chain gene (DYNC1H1) have been identified in rare neuromuscular (NMD) and neurodevelopmental (NDD) disorders such as spinal muscular atrophy with lower extremity dominance (SMALED) and autosomal dominant mental retardation syndrome 13 (MRD13). Phenotypes and genotypes of ten pediatric patients with pathogenic DYNC1H1 variants were analyzed in a multi-center study. Data mining of large-scale genomic variant databases was used to investigate domain-specific vulnerability and conservation of DYNC1H1. We identified ten patients with nine novel mutations in the DYNC1H1 gene. These patients exhibit a broad spectrum of clinical findings, suggesting an overlapping disease manifestation with intermixed phenotypes ranging from neuropathy (peripheral nervous system, PNS) to severe intellectual disability (central nervous system, CNS).
Genomic profiling of healthy and patient variant datasets underlines the domain-specific effects of genetic variation in DYNC1H1, specifically on toleration towards missense variants in the linker domain. A retrospective analysis of all published mutations revealed domain-specific genotype-phenotype correlations, i. e. , mutations in the dimerization domain with reductions in lower limb strength in DYNC1H1-NMD and motor domain with cerebral malformations in DYNC1H1-NDD. We highlight that the current classification into distinct disease entities does not sufficiently reflect the clinical disease manifestation that clinicians face in the diagnostic work-up of DYNC1H1-related disorders. We propose a novel clinical classification for DYNC1H1-related disorders encompassing a spectrum from DYNC1H1-NMD with an exclusive PNS phenotype to DYNC1H1-NDD with concomitant CNS involvement.
For P3, we performed a commercial targeted next-generation sequencing panel including coding regions of 61 NMD genes (spinal muscular atrophy and limb-girdle muscular dystrophies panel, MGZ München). P4 received trio-WES after enrichment for the IDT xGene exome research panel followed by 2 × 150 bp sequencing with a mean target coverage of 100-fold on Illumina NextSeq500 Sequencer. Alignment (mapping to GRCh37/hg19), variant identification (SNPs and indels), variant annotation, and filtering were performed using the CLC Biomedical Genomics Workbench (Qiagen, Hilden, Germany). Variants were filtered with a focus on proteinaltering variants (missense, frame-shift, splice-site, and premature stop-codons) rare or absent (de novo variants) from public databases (gnomAD and 1000 Genomes project) as previously described  [13].
In P5, we performed trio-WES after enrichment with Agilent SureSelect V6 kit (Agilent, USA) on an Illumina HiSeq 4000 Sequencer (Illumina, USA) with 2 × 75 bp sequencing protocol according to the manufacturer's bestpractice protocol, a mean coverage of 85 for the patient and father and a mean coverage of 80 for the mother was achieved  [14, 15]. The sequencing data were analyzed using a version of the Cologne Center for Genomics exome pipeline, version 2. 20  [16]. The annotated variant lists were uploaded to the Cologne Center for Genomics Varbank (https://varbank. ccg. uni-koeln. de) database for variant filtering. Since a trio-WES was available, we additionally performed calling and filtering for de novo variants using deNovoGear  [17]. For P6, the DYNC1H1 variant in patient and parents was found by a commercial next-generation sequencing gene panel for fetal akinesia (ID 078.
In general, CADD is a genelevel scoring for potential proxy-deleterious variants and has to be treated with caution. The linker mutations in our patient collective show amino acid exchanges with more significant changes in physicochemical properties when compared with variants from a healthy population dataset. The patients' mutations in the motor region are found in highly conserved AAA domains with higher CADD-Phred score values. However, the pathogenic mutations from patients are in regions where allele frequencies and high CADD-Phred scores are "thinned out". For the raw data, please see Supplementary Table  2. Protein regions noted below as in b. e Violin plot for CADD-Phred scores for variants recorded in gnomAD database (left in blue, https://gnomad. broadinstitute. org/); likely pathogenic and pathogenic variants according to ClinVar (middle in orange, https://www.
Quality control parameters were checked throughout the laboratory workflow. Sequence reads were aligned to the human genome (hg19) using Lifescope v2. 1 (Life Technologies), followed by variant calling on the aligned sequence. Variants were annotated using a custom analysis pipeline. Samples were automatically checked for quality (e. g. , median coverage). For further information on the sequencing procedure, please see previously published data  [18]. In P9, we performed WES with Agilent Clinical Research Exome Kit on an Illumina HiSeq 2000 with 2 × 100 bp reads, mean depth of coverage of 103×, and quality threshold of 95. 9% (percentage of XomeDx which is covered by at least ten sequence reads/10× coverage). The data were aligned to reference NM sequence based on GRCh37/ hg19 and analyzed for sequence variants using a customdeveloped analysis tool (Xome Analyzer).
Genotype-phenotype analyses including retrospective literature analysis To further characterize the phenotype of patients with mutations in DYNC1H1, we searched via "Pubmed" for publications of patients with mutations in DYNC1H1 from 01. 01. 2010 to 01. 01. 2020 and identified 24 publications with 120 patients  [1, [3] [4] [5] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38]. We used IBM SPSS Statistics 25 (IBM Corp. Released 2017, Armonk, NY) to generate a database of all patients including our ten patients (n = 130) and assigned each patient for 12 clinical manifestations (upper and lower limb strength, intellectual disability (ID), behavior, seizures, MRI abnormalities including pachygyria, heterotopias, enlarged ventricles, hypoplasia of corpus callosum, of the brain stem, and the cerebellum) either a physiological phenotype "0" or an abnormal phenotype "1".
Statistical analyses We performed further in silico and genomic analysis  [39]  for all variants, checking in population databases such as gnomAD (http://gnomad. broadinstitute. org), 1000 Genomes  [40] , GME  [41] , and ExAC browser (http://exac. broa dinstitute. org/). To evaluate the pathologic potential of the resulting variants, we used MutationTaster (http://www. mutationtaster. org/), SIFT (http://sift. jcvi. org/), PolyPhen2 (http://genetics. bwh. harvard. edu/pph2/), GERP++  [42] , REVEL  [43] , CADD-Phred (https://cadd. gs. washington. edu/), and MutPred2 (http://mutpred. mutdb. org/). The missense tolerance ratio (MTR, Fig. 1c ) calculates the number of observed missense DNA variations relative to the number of all observed (missense and synonymous) single base variants, then divided by the number of expected missense mutations relative to the number of all possible variations in that segment  [44].
To evaluate the evolutionary conservation, we performed multiple sequence alignment for all patients using Clustal Omega (https://www. ebi. ac. uk/ Tools/msa/clustalo/) (Fig. 2 ). In order to judge the pathogenicity on a domain-specific protein level and investigate the domain-specific vulnerability and conservation of DYNC1H1, we analyzed datasets on DYNC1H1 variants in a healthy population (gnomAD database) with a patient collective. We performed twofactor analysis of variances (ANOVA, SigmaPlot 12. 5, SYSTAT, USA) for CADD-Phred score values and MTR score values between the groups "reports" (healthy subjects vs patients) and the groups "protein regions" (tail vs linker vs motor vs MTBD). For these analyses, we pooled the ten patients from our collective with pathogenic and likely pathogenic DYNC1H1 mutation reports from the ClinVar database (Supplementary Table  2 ).
Genomic analysis and profiling We identified nine heterozygous novel pathogenic or likely pathogenic variants in DYNC1H1; P9's variant was reported previously in ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) (Table  1 , Fig. 1a-e ). All variants described were missing in population databases, classified as likely pathogenic according to the ACMG criteria and were evaluated based on gene constraint statistics (Fig. 1 , Table  2 ). Three mutations were in the tail domains (P1, P3, and P10), one mutation was in the linker domains (P6), four mutations were in the motor domains (P4, P5, P7, and P9), and two mutations in the stalk or MTBD (P2 and P8) (Fig. 1b ). Two mutations were located inside (P1, P2) and three mutations close to the coiled coil (CC) domains (P3, P6, and P8), four mutations in the AAA domains (AAA2: P4; AAA3: P7, P9; AAA4: P5) and one in a DYNC1LI2 interaction domain (P10).
Genomic profiling of population datasets for the domain-specific impact of genetic variation All mutations in our ten patients showed high CADD scores, high intolerance toward missense variations (MTR, Fig. 1c ) in evolutionary well-conserved domains (Fig. 2 ), and no reports in population databases (Table  2 ). When comparing healthy with patient report datasets, we observed that pathogenic mutations with significantly higher CADD scores in patients (Table  2 ) in all domains due to evolutionary conservation and drastic effects on physicochemical properties cause by amino acid exchanges at the mutational sites (p < 0. 001). This is also evident from multiple lines of in silico pathogenicity scores (Supplementary Table  2 ). Next, we looked at domain-specific statistical pathogenicity prediction tools.