dataesr/scientific-paragraphs-categorization huggingface dataset:
Because taxonomists are often not experienced data submitters, in many cases, they also will need assistance during the submission process, which might exceed the capacity of data centers. This calls for the development of tailored, user-friendly pipelines for fast, reliable, and easy transfer of specimen-based data to the most adequate repositories. Linking and finding species is best achieved via specimen identifiers that can be linked to characters and metadata  (Güntsch et al. 2017; Groom et al. 2017; Triebel et al. 2018). Ideally, identifiers will be globally unique and persistent, unambiguously linking the respective voucher specimens and associated metadata, including a standardized protocol on how the data were collected. Several identifier systems for biodiversity research have been proposed and are in use (see lists in  Guralnick et al.
Minimum requirements for taxonomic data should follow the "better than nothing" approach of the MIMARKS (minimum information on a marker gene sequence) and the MIxS standards (Minimum Information about any (x) Sequence) of the Genomic Standards Consortium (GSC;  Yilmaz et al. 2011) , while allowing for more detailed metadata in predefined or custom fields. Repositories facilitate not only long-term archival, accessibility, and discovery but also can be used to monitor the quality, usage, and impact of data  (Güntsch et al. 2017). Many different data repositories exist, including general-purpose ones like Dryad (https://datadryad. org) and Figshare (https:// figshare. com), topic-based like MorphoBank (https:// morphobank. org), journal-associated like MorphoMuseuM (https://morphomuseum. com) and GigaDB (http://gigadb.
org), MorphoSource (https://www. morphosource. org), and PLAZI (http://plazi. org/resources/treatmentbank/). However, the level of compliance of databases with the FAIR data principles, i. e. , for data to be findable, accessible, interoperable, and reusable  (Wilkinson et al. 2016) , is not always obvious to the data submitters who may not be aware of all the facts concerning databases' eligibility, registration, copyright notice, limitations, and exceptions.
As one possible option to attain this goal, the German Federation for Biological Data  (Diepenbroek et al. 2014 ) could be made more suitable for the taxonomic community. GFBio was established as a service-oriented infrastructure network to support the data life cycle in biodiversity, ecology, and environmental science. Its data archival, publication, and discovery services are built as a single point of contact for the users of long-term data centers, including natural science collections, environmental data publishers, and the European Nuclear Archive (ENA). So far, few taxonomic data sets have been published through the GFBio services  (Bruy et al. 2019; Rakotoarison et al. 2019) , and taxonomist make up about 5% of the users of GFBio (own data, 12 Feb. 2019). Experience with alpha-taxonomic data packages is growing, however.
The published data are linked to the GBIF portal, where their impact via citation metrics is visible. For example, the Bruy et al. (  2019 ) dataset (https://doi. org/10. 15468/l8qg0g) has been downloaded > 50 times in less than 2 months after publication. Despite such success stories, specific aspects of taxonomic data packages still need to be addressed, including the application of templates, which might be community or technology specific. GFBio and the taxonomic community in Germany are currently exploring possible solutions, which we hope will provide test cases triggering a wider adoption of data storage strategies by taxonomists. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Typhi CRISPR-Cas system further and report on its diversity as well. In this work, we analyzed the S. Typhi CRISPR-Cas system using WGS data of 1059 isolates obtained from four major typhoid-endemic countries (Bangladesh, India, Nepal and Pakistan) with the country of isolation, demographic data, and AMR status. Our work identified potential CRISPR-Cas system-related markers that associate specifically with endemic and AMR-related S. Typhi isolates. We further identified unique spacer targets in bacteriophages and plasmids that led to the identification of a specific PAM sequence for S. Typhi. Next, we annotated common and new cas genes, of which one, the gene WYL, could be specifically linked to XDR isolates from Pakistan. Collectively, our study reveals with an impressive dataset that the CRISPR-Cas system in S.
Source and Assembly of the Genome Data We used published WGS data of 536 isolates from Bangladesh, 198 from Nepal, 131 from India, and 20 from Pakistan  [5, 28, 29]. These 885 isolates were considered as "Surveillance" cases. WGS data of 100 isolates from the ongoing XDR S. Typhi outbreak in Pakistan were included and considered as "Outbreak" cases  [6]. Moreover, we included WGS of 74 travel-associated typhoid cases from the UK who traveled from the four above mentioned countries and categorized them as "Travel" cases  [30]. Details of all 1059 cases are provided in Dataset S1. Raw S. Typhi genome data (fastq files) of all cases were downloaded from the European Nucleotide Archive (ENA), following the accessions given in source articles. We used SPAdes v3. 12. 0 (options: cov-cutoff = 'auto') to assemble the fastq files and removed smaller contigs (<300 bp)  [31].
Sequences of direct repeats (DR) and spacers were extracted from all S. Typhi, Salmonella spp. and E. coli isolates separately and screened for sequence identity within their groups (ignored if redundant otherwise termed as 'unique'). All unique DR and spacers were given a unique three-part identifier (e. g. , Td29a, Ts32ac, Ss32aak) following the strategy explained in Figure  S1. Spacer arrangements of all group-A and B CRISPR loci were determined separately. To detect the cas genes, we used Prokka v1. 13. 3 (options: gcode = 11) to annotate all 1113 genomes and blastp v2. 7. 1 + (options: evalue = 1 × 10 -9 and qcov_hsp_perc = 80) to search the annotated protein sequences against the cas-gene repository published earlier  [18, [33] [34] [35]. For each detected cas-gene, corresponding nucleotide sequences were extracted from the annotation files and searched against the contigs using blastn (options: evalue = 1 × 10 -9 and qcov_hsp_perc = 95, maximum_bit_score) to find the gene location (sorted by position in the contigs) and orientation (positive or, negative-strand) and define the cas-gene loci.
CRISPR Loci of S. Typhi Versus other Salmonella Species The addition of 91 group-A CRISPR loci from other Salmonella species (84 loci from 48 isolates of 19 serovars) and E. coli (seven loci from six isolates) to the MLT of all S. Typhi group-A CRISPR loci (n = 1054) revealed the presence of a primary clade (bootstrap 98) specific for S. Typhi (Dataset S2 and Figure  4a ). The closest neighbor to the clade was S. enterica subsp. enterica (Figure  4a ). This finding obtained further support from the estimated distance (median distance 3. 12) calculated from the multiple sequence alignment (MSA) of all 1145 group-A CRISPR loci (Table  S2 ). The lowest intraspecies distance (mean 0. 07) among group-A CRISPR loci was found for S. Typhi (Table  S2 ). Furthermore, the average length of these CRISPR loci was the shortest among all 19 different Salmonella serovars studied (after S.
CRISPR Loci of S. Typhi Versus other Salmonella Species The addition of 91 group-A CRISPR loci from other Salmonella species (84 loci from 48 isolates of 19 serovars) and E. coli (seven loci from six isolates) to the MLT of all S. Typhi group-A CRISPR loci (n = 1054) revealed the presence of a primary clade (bootstrap 98) specific for S. Typhi (Dataset S2 and Figure  4a ). The closest neighbor to the clade was S. enterica subsp. enterica (Figure  4a ). This finding obtained further support from the estimated distance (median distance 3. 12) calculated from the multiple sequence alignment (MSA) of all 1145 group-A CRISPR loci (Table  S2 ). The lowest intra-species distance (mean 0. 07) among group-A CRISPR loci was found for S. Typhi (Table  S2 ). Furthermore, the average length of these CRISPR loci was the shortest among all 19 different Salmonella serovars studied (after S.
A closer look at the presence of different DRs revealed a couple of specific patterns (n = 6) as well. The two group-B CRISPR loci specific DRs, Td39a, and Td39b, were more frequently observed among S. Typhi isolates obtained from surveillance (n = 173) than outbreak (n = 7) or travel (n = 0) cases (p < 0. 001) (Table  2  and Figure  3 ). Two of the dominant DRs, Td23a (n = 456) and Td35a (n = 192), were almost absent among the Bangladesh surveillance isolates (n = 3 and n = 6, respectively), whereas the latter DR was only identified in two Pakistan outbreak-related isolates (p < 0. 001) (Table  2 ). A few pairs of spacers and DR sequences (Ts34d-Td35a, Ts55a-Td23a, and Ts54a-Td39a/b) also showed specificity to different countries and study settings (p < 0. 001) (Table  S3 ). Dataset S3 has the sequences of all identified DR and spacers.
Spacer Targets and PAM Identification We thus identified specific spacers, DRs, combined spacer patterns, and DR-spacer pairs of S. Typhi that could potentially serve as biomarkers to help identify regional endemicity and AMR amongst others. Only a few spacers harbored 100% (or, nearly 100%) identity with the bacterial, plasmid, phage, viral, and AMR-related sequences (see Doc S1 and Figure  S7  for more details and about the databases and filter settings). For all the obtained spacer target hits, the possible PAM sequences (10 bp downstream and upstream of the protospacer) were not conserved, except for the spacers targeting the plasmid sequences (Figure  6a-e ). Indeed, the potential PAM regions of plasmid sequences were highly conserved and were marked by the motif TTTCA (upstream) and TGCGT (downstream) (Figure  6b ).
2. 2 (Table  3 ). Ts32g was also ubiquitously present (n = 1054) and had a target against Sinorhizobium phage phiN3 (Tables  3  and 6 ). In contrast, all plasmid sequences targeted by the S. Typhi spacers were part of only four different "hypothetical" proteins from either the species Salmonella enterica or the family Enterobacteriaceae (Table  6 ). None of these proteins showed any hit in the Pfam database (https://pfam. xfam. org/). Genes 2020, 11, x FOR PEER REVIEW 16 of 24 sequences, all were short in length (23 or 32 bp), mostly present in group-A loci (Tables  3  and 6 ). In contrast, five spacers (Ts34j, Ts53a, Ts53b, Ts53c, and Ts59a) that harbored protospacers in the plasmid sequences showed specificity to the group-B CRISPR loci and longer in base-pair length (34, 53 or 59 bp) (Tables  3  and 6 ).
Each phage-targeting spacer had a different viral target, but none, except Ts32i, targeted a Salmonella spp. phage (accession: MK268344. 1) (Table  6 ). Ts32i was present in 91% (974/1059) of the isolates except in genotype 3. 2. 2 (Table  3 ). Ts32g was also ubiquitously present (n = 1054) and had a target against Sinorhizobium phage phiN3 (Tables  3  and 6 ). In contrast, all plasmid sequences targeted by the S. Typhi spacers were part of only four different "hypothetical" proteins from either the species Salmonella enterica or the family Enterobacteriaceae (Table  6 ). None of these proteins showed any hit in the Pfam database (https://pfam. xfam. org/). Conserved regions could be the protospacer adjacent motifs (PAM) for S. Typhi.
001; Figure  1a  and Table  1 ). Local and highly clonal S. Typhi lineages have been reported from both countries  [5, 6]  and of these lineages had higher average numbers of CRISPR loci (Figure  1b ). Hence, clonality could be a contributing factor for the lower number of CRISPR loci identified in these isolates. Haplotype specificity of the S. Typhi spacer arrangement patterns has been described  [11]. We could not confirm those associations  [11] , primarily, because we were unable to identify the same spacers, except one, Ts32v (match 31/32 bp of a spacer from CRISPR2 described by Fabre et al. [11] ). However, our study revealed multiple spacers (Ts32g, Ts32h, and Ts32i), spacer arrangement patterns (a2 and a5), DRs (Td23a, Td35a, and Td39a-b), and DR-spacer pairing patterns (Ts34d-Td35a, Ts55a-Td23a, and Ts54a-Td39a/b) specific to different AMR, country, genotype or surveillance, travel, and outbreak characteristics (Figures 2, 3 and 5, Tables  2  and 4 , Figure  S6 , Table  S3 , and Dataset S1).
The identified spacer, DR, and DR-spacer patterns could, therefore, be further exploited by CRISPR-based diagnostic platforms like SHERLOCK or DETECTR for clinically relevant samples  [36, 37]  to identify AMR among endemic isolates that are spreading in and beyond South Asian countries  [29, 38]. The spacer sequences of S. Typhi showed remarkable conservation, and only 47 unique spacers were detected in 1919 CRISPRs identified in the genomes of 1059 S. Typhi isolates (Table  3  and Dataset  S3 ). Many spacers in group-A loci (Ts32c, e, g, h, i, and l) were almost universally present in all S. Typhi isolates, whereas specific spacers (Ts55a, Ts54a, Ts34d) showed high numbers of presence in group-B loci (Figure  2  and Table  3 ). Reports on CRISPRs identified in other pathogens described a higher number of unique spacers, i.
Supplementary Materials: The following are available online at http://www. mdpi. com/2073-4425/11/11/1365/s1, Dataset S1: Complete data of all S. Typhi isolates used in this study, including the ENA accessions, metadata from the source articles, and the results we generated for each isolate like-MLST, genotype, number of CRISPR loci, spacer patterns, and presence of newly described cas-genes. Dataset S2: Accession numbers of 48 other Salmonella (19 different serovars, excluding Typhi) and six E. coli isolates. Dataset S3: Fasta sequence file of all S. Typhi direct repeats (DR) and spacers identified in this study. Doc S1: Describes the additional parts of the Method and Material section. Figure  S9 : Presence of the identified cas genes of other types of CRISPR-Cas system than type-I-E in all 1059 isolates.
Typhi isolates with other Salmonella serovars, we added sequences of 48 complete chromosomes of 19 different Salmonella serovars (excluding S. Typhi) from NCBI-genome (https: //www. ncbi. nlm. nih. gov/genome/genomes/152, downloaded on 12 October 2018). We also included six representative reference genomes of E. coli (https://www. ncbi. nlm. nih. gov/genome/genomes/167, downloaded on 12 October 2018). Accession numbers of all 54 complete chromosomes of different Salmonella species/serovars and E. coli isolates are listed in Dataset S2.
(Gln310_Ala316del) patient (male) with fibroblasts from a NIPBL-haploinsufficient patient (mutation NIPBL p. (Leu1365*), female) and healthy control fibroblasts (female). Notably, the patient carrying the nonsense NIPBL mutation displays a severe CdLS phenotype, in line with its associated NIPBL haploinsufficiency  (Braunholz et al. , 2015). All samples were sequenced in triplicates and genes locating at the sex chromosomes were omitted from the analysis. The transcriptomes of the two patients were compared with that of the control fibroblasts, and the differentially expressed genes of the patient cells were subsequently correlated. Despite the limitation of this dataset, gene expression changes clearly correlated between the two patients. This indicates that overall gene expression is altered in a similar way in cells with MAU2 and NIPBL mutations (Figure  2A ).
0. 0), and samtools (version 0. 1. 19) was used to remove reads with mapping quality less than 30, and to keep only aligned reads. Duplicated reads were removed, after alignment, using Picard (version 1. 97). For statistics of the ChIP-sequencing see Table  S6. Peaks were called using MACS (macs 2) and Peaks were filtered using P value. UCSC tracks were generated after duplicate removal, using HOMER (version 4. 3) and deeptools (version 3. 0. 2). Heatmaps were generated using Deeptools (version 3. 0. 2) and NGS plot (version 2. 61). Peaks were annotated to specific regions using ChIPseeker (Bioconductor package version 1. 14. 2). The data are accessible at GSE122299.
Library quantification was performed using qPCR together with the Kapa Library Quantification Illumina/Light Cycler 480 (Roche). The validated libraries were pooled in equimolar quantities and sequenced via 150 bp paired-end on an Illumina HiSeq 4000 platform following Illumina's recommended protocol. Raw data were demultiplexed with the Illumina bcl2fastq 2. 17 into individual fastq files. Reads were aligned using the mem algorithm of bwa 0. 7. 8 and aligned to the hg19 reference into which decoy sequences had been added, PAR regions had been masked and the mitochondrial DNA had been replaced with the rCRS (revised Cambridge Reference Sequence) to match MITOMAP and most publicly available resources for mtDNA variants. Base quality scores were recalibrated using GATK BaseRecalibrator with enlarged context size for SNVs and Indels with respect to default (4 and 8 base pairs instead of 2 and 3).
ACKNOWLEDGMENTS We thank Jaulin's team for access to the microscope,  Koichi Tanaka  and  Kim Nasmyth  for sharing the NIPBL antibody, and  Jan-Michael Peters  for sharing the MAU2 antibody. We thank  David Fitzpatrick  and  Morad Ansari  for sharing the AG0885 LCLs used to prove MAU2 instability in CdLS patients with early truncations. We thank  Ifigeneia Thomopoulou  for help with western blotting and  Maarten Fornerod  for advice on transcriptome analyses. This work was funded by the  German Federal Ministry of Education and Research (BMBF) (CHROMATIN-Net  to F. J. K. ), by the  German Research Foundation (DFG)  (research unit  FOR2488  to F. J. K. ), by  E-Rare-2 TARGET-CdLS  (to F. J. K. , K. S. W. , and E. W. ), and by the  Medical Faculty of the University of L€ ubeck  ( J09-2017  to I.
As a case study, we use sequences encompassing gene starts from the RefGene database as positive examples and random genomic sequences as negative examples. We then demonstrate that models trained using data from one organism can be used to predict gene-start sites in a related species, when using training sets providing good genome-wide performance. This cross-species application of convolutional neural networks provides a new way to annotate any genome from existing high-quality annotations in a related reference species. It also provides a way to determine whether the sequence motifs recognised by chromatin-associated proteins in different species are conserved or not.
This is due to the fact that the networks are optimised on a similar number of positive and negative examples during training, but that they will usually face very different ratios of negative over positive classes when used on a full chromosome sequence  (He & Garcia, 2009). Alternative approaches  (Alipanahi et al. , 2015; Kelley, Snoek & Rinn, 2016)  used unbalanced datasets for training (i. e. , with more negative than positive examples) to predict DNA-binding sites for proteins and genome accessibility. In these two studies, however, the prediction performance of the model is also assessed on test sets derived from training sets, not on full genomic sequences. The task of genome-wide prediction has been assessed in a more recent study aiming at identifying cell type specific regulatory elements  (Kelley et al.
For instance, protein coding genes can have either CG di-nucleotide (CpG) rich or poor sequences upstream their GSS  (Deaton & Bird, 2011). We show that using training sets with a higher ratio of negative over positive examples, we can faithfully retrieve GSS positions, with performances varying for different classes of genes such as coding or non coding genes. We then propose a new application of CNNs in genomics that leverages the fact that similar organisms tend to have similar regulatory mechanisms, i. e. , rely on homologous molecular machinery and on homologous DNA regulatory motifs. Exploiting these homologies, we first train a model on a dataset corresponding to a given organism and use it to predict the annotation on the genome of a related organism, opening new opportunities for the task of de-novo genome annotation.
Input generation Genomic sequences were downloaded for the reference genomes for Human (hg38), Mouse (mm10), Chicken (gg4) and Zebrafish (dr10) via the URLs shown in Table  1. Similarly, GSS positions for each genome were extracted from their respective NCBI RefSeq Reference Gene annotations (RefGene). As a positive input class, we use regions of 299 bp flanking GSS (i. e. , ±149 bp around the GSS) which are supposed to contain multiple sequence signals indicating the presence Unbalanced datasets are produced using different values of Q ranging from 1 to 100. For Q = 100, the negative class encompasses 100×299×31k ≈ 1Gb, which represents one third of the human genome. For the other genomes a similar procedure was implemented. The total number of GSS used was 25,698 for the mouse, 6876 for the chicken and 14805 for the zebrafish.
Implementation We implement CNN using Keras library  (Chollet et al. , 2015)  and Tensorflow  (Abadi, Agarwal & Barham, 2015)  as back-end. Training on a GPU is typically faster than on a CPU. We use here a GTX 1070 Ti GPU. We use Adaptive Moment Estimation (Adam) to compute adaptive learning rates for each parameter  (Kingma & Ba, 2014). Adam optimiser is an algorithm for first-order stochastic gradient-based optimisation of functions, based on adaptive estimates of lower-order moments. The network architecture (see Fig. 1 ) is detailed in Table  2. The models are trained for 150 epochs and they mostly converge rapidly (around 30-35 epochs, we use early stopping to prevent overfitting). Hyper-parameters tuning is detailed in the supplementary materials. Source codes are available at https://github.
Training models for genome annotation of GSS The problem of detecting human GSS using deep neural networks has been tackled in  (Umarov & Solovyev, 2017). We first follow a similar approach and use a balanced dataset (see Methods for details). The model is trained/validated on an equal number of 299 bp long positive and negative examples and is evaluated on a test set composed of 15% of the original data that was left aside prior to training. The specificity (Sp), the sensitivity (Sn) and the Matthews Correlation Coefficient (MCC, Chicco & Jurman, 2020) (see Supplemental Information 1 for definition) were found to be similar to the ones found in  (Umarov & Solovyev, 2017)  which used a similar approach albeit separating the sample data into TATA-containing GSS and non-TATA GSS (Sp = 0. 94, Sn = 0.
Applied over all the 299-bp sequences of chromosome 21, the model encounters many more examples of the negative class and fails to generalise to the new examples. To address this issue and train a network for genome annotation, we propose a heuristic where more negative examples are added into the balanced dataset to reduce the importance of the positive class during training and to allocate more weight to the negative class. We call these augmented datasets limited unbalanced datasets. The parameter Q is the ratio between negative and positive training examples and denote as Q * models trained with the corresponding ratio. For instance, on Fig. 2A  the model trained on the balanced data yielding to blue signal predictions is denoted as 1 *. We train our CNN model on a 100* dataset (Q = 100) and assess the efficiency of the trained model.