dataesr/scientific-paragraphs-categorization huggingface dataset:
OKseqHMM generated high-quality cell type-specific RFD profiles and robust IZ calling for all datasets analysed. The average IZ sizes in the different cell types were within the same range (between 26 and 36 kb), demonstrating that it is a common feature of human cells. Conversely, the RFD profiles were cell-type specific, although they were quite conserved among cell lines in some origin-rich regions. The data obtained for similar cell types or for cell lines with a similar origin showed similar RFD profiles (Figure  6B ). For instance, the Pearson correlation R was 0. 87 between the Raji and BL79 cell lines (both derived from lymphoma samples) and 0. 79 between these cell lines and GM06990 human lymphoblastoid cells. Similar results were obtained when comparing the RFD transitions (i. e.
OKseqHMM to analyse the RFD profiles from other sequencing data Besides OK-seq data, the OKseqHMM toolkit can be used to compute the RFD profiles from sequencing data obtained with other techniques. As a demonstration, our  toolkit was tested using previously published eSPAN  (8)  and TrAEL-seq  (11)  datasets. The RFD data computed from the yeast TrAEL-seq data were very similar to those obtained using OK-seq data (Figure  7A , R = 0. 93, P < 10 -15 ). The RFD profile obtained using TrAEL-seq data was of higher quality with less local noise compared with the OK-seq-based RFD profile. This difference does not seem to be explained by the higher coverage of the TrAEL-seq data (∼2-fold more reads) compared with the available OK-seq data. Indeed, TrAEL-seq data displayed a lower local noise profile also after down-sampling to the same coverage as OK-seq data (Figure  7A ).
The better RFD profile obtained with TrAEL-seq data facilitated the detection of replication initiation regions in some cases. For example, an IZ was identified in the TrAEL-seq RFD profile at chrIV:1486452-1486950, but not in the OK-seq data due to the higher local noise (Figure  7A ). To further evaluate the IZs detected with different techniques, the IZs identified with FORK-seq data (28) also were integrated. FORKseq is a nanopore sequencing method that allows mapping replication initiation within single DNA molecules. Comparison of the TrAEL-seq IZs, OK-seq IZs, FORK-seq IZs and yeast ARSs showed that 77% (271/348) of OK-seq IZs and up to 89% (339/380) of TrAEL-seq IZs were found within 2 kb from a known ARS. Moreover, 75% (203/271) of OK-seq IZs associated with ARSs were detected by TrAEL-seq, and ∼69% (186/271) of them were found in FORK-seq data (Figure  7B ).
Notably, a small percentage of initiation sites that were not associated with OriDB origins were robustly detected by analysing OK-seq, TrAEL-seq and FORK-seq data with our toolkit (Figure  7B , Supplementary Figure  S5A-C ). This supports the previous observation that replication initiation in yeast can also occur at loci barely enriched in ARS consensus sequence motifs, although with low frequency  (28). Again, the IZs identified in both OK-seq and TrAEL-seq datasets showed higher firing efficiency and larger size than those identified in only one dataset (Supplementary Figure  S5D, E ). Finally, OKseqHMM was used to compare OK-seq and eS-PAN data (8) of mouse embryonic stem cells (mESC). However, due to the lower amount of reads in the eSPAN dataset, the obtained RFD profiles were too noisy to perform a robust IZ calling, despite using a larger window size (e.
DISCUSSION Genome-wide replication fork directionality data help to understand many biological processes, such as TRCs, replication-associated mutagenesis, replication couple epigenetic maintenance. Here, we presented OKseqHMM, a comprehensive R package that analyses OK-seq data from various cell types and species to generate and visualize RFD and OEM profiles at high resolution and genomewide, as well as to generate the average profiles/heatmaps of the regions/genes of interest. The toolkit also allows accurately detecting replication initiation/termination zones with an HMM algorithm. To our knowledge, this is the first bioinformatics tool to handle and analyse RFD data obtained from sequencing datasets generated using various techniques. The toolkit is based on R, and should be easy to use by bioinformaticians and also biologists (e.
g. via Rstudio). We successfully used OKseqHMM to analyse available OK-seq datasets from different species (yeast and mouse) and also normal and cancer human cell lines (Table  1 ). This toolkit is an important resource for many research communities with different interests (e. g. DNA replication programs, TRCs, replication-associated chromatin organi-zation, replication-associated mutations, genome instability and cancer genomics). Importantly, besides OK-seq, many new techniques have been developed to study DNA replication and to provide replication fork direction data. These include eSPAN and SCAR-seq to perform stranded sequencing of BrdU-or EdU-labelled nascent replicated DNA associated with specific histone modifications, and also TrAEL-seq and GLOE-seq based on the single-stranded end present on specific replicative templates.
Here, we demonstrated that OKseqHMM can be used to analyse also data obtained with these techniques with high-quality results (Figure  7 ). Notably, label-free techniques (e. g. TrAELseq) that require fewer cells to generate high-quality RFD profiles, compared with OK-seq, will provide a good alternative to study DNA replication and genomic instability in different cell types exposed to different stress conditions. Importantly, a multi-scale method is needed to extract the replication initiation/termination information from human RFD profiles because replication initiation zones have various sizes, from 10 to 100 kb (Figures  4 5 6 , Supplementary Figure  S3 ). Sizes are even larger for replication termination zones, from 120 to 500 kb. In previous studies, without an adapted bioinformatics tool for OK-seq data analysis, scientists often used peak calling methods to the OEM profile at a fixed scale in order to identify replication IZs and/or TZs  (8, 11, 25).
g. IGV or UCSC genome browser). It should be noted that the initiation parameters, such as the transition and emission probabilities of HMM, are defined based on the OK-seq datasets of human cells. Here, we showed that these parameters are quite robust and can be also applied to OK-seq datasets from yeast (Figure  3 ) and mouse cells (Figure  7 , Table  1 ) with satisfactory results. To further improve IZ/TZ calling, the transition and emission probability matrix and the threshold of minimum read count per bin, which are the main parameters of the functions, could be easily adjusted by users based on the sequencing depth and data quality of their datasets. As highlighted by the comparison of the results obtained from the two biological replicates of yeast OK-seq data, the RFD profiles are very reproducible; however, we strongly recommend to use biological replicates to reduce the potential false detection of IZ calling due to the local variation in the RFD profiles (Supplementary Figures  S1  and S2 ).
Moreover, a comparison between biological replicates and/or between OK-seq datasets obtained with different techniques showed that we missed some bona fide IZs, especially those with lower firing efficiency because we used a strict strategy to reduce false positive detection of IZs (Supplementary Figures  S2, S3  and S5 ). It would be interesting to improve the capacity to discriminate between IZs with low firing efficiency and background noise to reduce false negative results. In the future, with technical improvements, we might be able to extend the OKseqHMM toolkit to study the extrinsic (cell-to-cell) or intrinsic (homolog-to-homolog) variability of DNA replication, if we can obtain data at the single-cell level and/or in an allele-specific manner, as recently achieved for replication timing  (35, 36).
ACKNOWLEDGEMENTS YL thanks  ANR  for providing her PhD fellowship. The authors would like to thank  Joseph Josephides  and  Elisabetta Andermarcher  for critical reading of the manuscript,  Ala Eddine Boudemia  for helping with the bioinformatics analysis, and to acknowledge the  Institute for Integrative Biology of the Cell (I2BC)  high-throughput sequencing facility for the sequencing expertise.
DATA AVAILABILITY The genome assemblies sacCer3 (yeast), mm10 (mouse) and hg19 (human) were used in the analysis. The raw sequencing data of OK-seq are available with the corresponding accession numbers indicated in Table  1. The OK-seq data of HeLa S3 cells have been deposited at Gene Expression Omnibus (GEO) under accession number GSE193547. The known yeast origins (ARSs) were downloaded from the OriDB website (http://cerevisiae. oridb. org/)  (29). The initiation events identified by FORK-seq.
Note that not all confirmed OriDB origins overlapped with OK-seq IZs because all origins might not be active in the culture condition and/or yeast strain used for the OK-seq experiment. Further comparison with origins identified in datasets obtained with other techniques can be found in Figure 7B. (D) Boxplot showing the distribution (in red) of distances between the centre of an IZ detected by OKseqHMM and the centre of the origin from OriDB (grouped in three classes). Such distances are much smaller than the distances between OriDB origins and the random simulation control (in black), as indicated by the Wilcoxon rank sum test; **P < 10 -2 , ***P < 10 -3 , ****P < 10 -4.
It should be noted that there are more origins unique to FORK-seq because this is a single-molecule technique that allows identifying also initiation events with very low frequency. (C) Metagene average RFD profiles computed using the OK-seq and eSPAN datasets from mouse embryonic stem cells (8). The mean and standard error bands are shown only for the eSPAN dataset because the standard error bands for the OK-seq dataset were too narrow to be seen.
Ambiviruses have circRNA genomes with ribozymes Analysis of large ribozyme-bearing circRNAs (~5 kb) unveiled sequence similarity with diverse ambivirus genomes, suggesting that their unusual features derived from 5' RACE analyses could be due to a circular genome (Supplementary Note 1 and Supplementary Fig. 3 ). Moreover, we analyzed published ambivirus genomes for sequence homology to structured RNA covariance models with INFERNAL  18. This uncovered head-to-tail oriented, ambisense HHRz or HPRz motifs in most of the GenBank sequences, reminiscent of subviral circRNAs (Supplementary Table  1 ). The initially reported similarity between ambivirus ORF-A and RdRp was not statistically significant owing to their deep divergence  12, 13. However, searches for ambiviral RdRps in the RDVA and other public databases (see Methods) revealed up to 974 unique RdRpencoding ambiviruses, which clustered into 439 sOTUs at 90% palmprint amino acid identity  19.
Again, these genomes were found widespread in diverse environmental datasets (Supplementary Fig. 4 ). This high number of ambivirus RdRp sequences provided us the opportunity of further exploiting the in silico structural prediction that have extended the capacity to assess for deep protein homology from sequences alone  20. ColabFold-AlphaFold2 prediction of ORF-A supports (pLDDT confidence up to 93. 5 for several models) that these proteins take on the classic right-handed palm domain architecture, with highest structural similarity to negarnavirus RdRps (Influenza A, Z-score >19, RDSM 3. 8 A) (Supplementary Fig. 5 ). Critically, the expanded multiple sequence alignment shows sequence conservation across all the essential polymerase catalytic motifs A, B, C, D, E and F  19  (Fig. 2A  and Supplementary Fig.
Our in vivo demonstration that fungi harbor infectious circRNAs expands the experimentally validated hosts for viroid-like molecules beyond plants and animals to also include the kingdom fungi. With their multiple symbiotic relationship with plant and animal hosts, fungi are indeed a hub for generating trans-kingdom horizontal transfer of Conservation of catalytic residues in motif A, B, and C is seen across all ambiviruses identified in this study and present in databases. B Circular plots of the predicted RNA secondary structures of two representative ambiviruses. On the left, an example of the characteristic rod-like structure predicted for most ambiviruses. On the right, an example of a highly branched architecture predicted for some ambivirus RNA genomes, usually carrying a mixed pair of HHRz/HPRz motifs.
Ribozyme search of the Sequence Read Archive Observing that ribozymes are sufficiently short to be captured on a short sequence read (less than 100 nt), we reasoned it will be possible to screen large volumes of sequencing data to identify libraries potentially containing ribozyme agents. To this end we adapted the ultra-high throughput Serratus cloud computing platform  14  , with the INFERNAL  18  , an algorithm for identifying RNA elements using secondary structure and nucleotide covariation modelling. Using Serratus-INFERNAL (https://github. com/ababaian/serratus/tree/ infernal-dev), we performed a screen of 198,194 publicly available metagenomic/metatranscriptomic sequencing libraries in the Sequence Read Archive (SRA) for the delta, hairpin, hammerhead, twister, and Varkud satellite ribozymes (hatchet, pistol, twister sister and glmS ribozymes were discarded due to the absence of hits in previous analyses).
Bioinformatic detection and phylogeny of ambiviral RdRps RdRps (ORF-A) from either the 19 deposited ambivirus genomes at the GenBank (09-2021), or the 169 ambiviral-like sequences (either putative full circles or genomic fragments) detected though INFERNAL in public metatranscriptomic datasets ("metatranscriptome" OR "metatranscriptomic"[Filter]), were used to build a consensus sequence for the ambivirus motifs A, B and C. Based on the obtained consensus sequence for the ambiviral palm domain, we built a new version of the palmscan software (https://github. com/rcedgar/palmscan) capable of ambivirus RdRp detection. Palmscan searches were performed in the circular contigs from the RDVA, which together with the ones obtained from GenBank resulted in 439 ambiviral RdRps at 90% sequence identity (https://github.
com/ababaian/serratus/wiki/ambivirus_ extended_data/). We performed multiple sequence alignment on the clustered RdRp palmprint sequences using MUSCLE  33  and maximumlikelihood tree is inferred from the alignment using MEGA11  34. Secondary RNA structures of minimum free energy of the ambivirus genomes were calculated with the RNAfold program from the Vien-naRNA Package  35. The obtained structures were visualized by circular plots obtained with the jupiter software (https://github. com/rcedgar/ jupiter).
Analyses of RNA self-cleavage in vitro Self-cleavage activity of HHRz and HPRZ of representative ambiviruses (CpAV1, TuAmV1 and TuAmV4) and VSRz of the mito-like virus detected in the SRR10849745 library was tested in vitro by transcription of recombinant plasmids containing the cDNA of the respective ribozymes. The cDNAs of viral RNA fragments containing the ribozymes were either amplified using the appropriate primers listed in Supplementary Data 1 or purchased as gBlock Gene Fragments (Integrated DNA Technologies), subcloned in either pBlue-Script KS or pGEM-T easy Promega, (Madison, WI, United States) and sequenced to confirm the orientation and absence of unwanted mutations. The recombinant plasmids, linearized with the appropriate restriction enzyme, were transcribed in vitro with T7 RNA polymerase (Thermo-Fisher Scientific) at 37 °C for 2 h using buffer provided by the manufacturer and the reaction products separated by denaturing 5% PAGE 8 M urea and 1× TBE (89 mM Tris, 89 mM boric acid, 2.
5 mM EDTA, pH 8. 3), stained with ethidium bromide and UV visualized. The 5' terminal nucleotide of the 3' self-cleavage product of HHRz and HPRz motifs was determined by 5' RACE experiments. Briefly, the 3' RNA fragment generated by the selfcleavage of each tested ribozyme during the in vitro transcription was eluted from the gel by grinding with a mixture (1:1) of watersaturated phenol and buffer (100 mM Tris-HCl pH 8. 9, 1 mM EDTA, 0. 5% SDS), and recovering the nucleic acid from the aqueous phase by ethanol precipitation  38  then their 5' terminal sequence was determined by 5'RACE as described above using the primers indicated in Supplementary Data 1.
qPCR on the terminal sequences of the cleaved genome In order to investigate whether the terminal fragments of the genomic and antigenomic strands of the ambivirus RNAs were fully complementary, we performed strand-specific qPCR analysis on TuAmV1 RNAs. cDNAs were synthesized from the same amount of RNA with primers specifically annealing either on the genomic or on the antigenomic strand (Supplementary Data 1) and designed either on the terminal putative sequence found by RACE, or on a region up/downstream. A pair of primers was designed on the ORF A and the qPCR product obtained was used as internal reference for the quantification of the terminal sequences. The cDNAs were columnpurified (DNA Clean & Concentrator TM 25 Zymo Research) and a standard qPCR was performed using the same primers: briefly, qPCR reaction was performed in 10 μL final volume using the SYBR® Green Master Mix (Biorad).
Single spore isolates were transferred after 5 days, and single fungal colonies were tested for ambivirus presence/absence by qRT-PCR. cDNA was synthesized with the High Capacity cDNA Reverse Transcription Kit (Appliedbiosystems, by Thermo Fisher Scientific) following the manufacturer's protocol. To diagnose CpAV1 in the isolates, qRT-PCR was performed using a 1700 fast SDS Real-Time PCR detection system (Applied Biosystems). The PCR reaction was performed in 10 μL using the I-Taq supermix (Biorad) and Taqman probe. Each 10 μL reaction mix contained 5 μL supermix, 0. 15 μL probe, 4 μL of sterile water and 0. 2 μL of forward and reverse primers (Supplementary Data 1), and 1 μL of cDNA. Amplifications were carried out in 96-well plates in a CFX Connect Real-Time PCR Detection System (Biorad) with thermocycling conditions of 3 min at 95 °C, 20 s at 95 °C, and 30 s at 60 °C for 40 cycles.
Statistical analysis Analyses of the data referred to the measures of lesion or canker size in the virulence assays (apples and chestnut cuttings) were performed with one-way analysis of variance (ANOVA) using the R-statistical program  39. Significant differences were assessed with Tukey's post hoc test (p < 0. 05). Validation of RNA-only nature of viroid-like and delta-like elements in selected fungal metatranscriptomes We screened HTS read libraries present in two bioprojects: PRJNA524447 corresponding to a collection of Rhizoctonia solani isolates  40  and PRJNA629308 corresponding to an orchids and ericoid mycorrhizal fungi collection 12 using the same pipeline described above. All the selected contigs were checked in each of the biological samples to track contig-positive samples using the primers in Supplementary Data 1 in a standard PCR reaction using OneTaq® DNA Polymerase (New England Biolabs, Ipswich, MA, USA).
Acknowledgements This work is dedicated to the memory of  Professor Ricardo Flores  who largely contributed to the study of viroids and viroid-like RNAs. M. T. is grateful to  Mr. Damiano Fontana  for providing with chestnut cuttings. M. d. l. P. is grateful to the  Bioinformatics Service at IBMCP (UPV-CSIC)  for excellent technical assistance. M. F. was supported by a post-doctoral fellowship from the  National Research Council of Italy  ( Assegno di Ricerca IPSP 078 2021 TO ). M. F. A. was supported by of a mobility stay grant funded by the Erasmus + -KA1 Erasmus Mundus Joint Master's  Degree Programme  of the  European Commission  under the  PlantHealth Project. A. B. was supported by the  Canadian Institutes of Health Research (CIHR)  Banting Postdoctoral Fellowship # 453974. M. d. l. P.
Once both networks are fully trained, the generator can create unseen data samples with high similarity to the real data. Earlier efforts to simulate EHR data have used Bayesian network learning  23  , and deterministic differential modeling, e. g. , as implemented in the popular open-source software Synthea  24. This opensource software package is designed to simulate the lifespans of synthetic patients but is based on fixed demographic properties extracted from public data and does not learn through a training procedure. GANs have so far been the primary deep-learning method to generate synthetic EHRs  9, [25] [26] [27]. Notably, Choi et al. proposed medGAN  9  , a neural network model that generates highdimensional discrete variables to represent EHR events. Baowaly et al. 25  derived two enhanced versions of medGAN with a more complex (Wasserstein) architecture, and Yale et al.
EHR data source The Medical Information Mart for Intensive Care (MIMIC-IV) database was the source of all our numerical experiments  34. MIMIC-IV provides critical care data for thousands of patients admitted to the intensive care units at the Beth Israel Deaconess Medical Center. The patient, visit (encounter), diagnosis (condition), and medication (medication request) data tables were migrated to a labeled property graph (LPG) database. These data tables cover healthcare events at the emergency unit and the following inpatient stays. They are a subset of a complete graph model for healthcare data developed by one of the authors  35  , which follows the FHIR standard for healthcare data  36. The LPG database was constructed by connecting all visits (encounters) for a patient in chronological order.
Conditions and medications for the patient are then attached to the encounters. Descriptive nodes for the types of conditions and medications are shared among the patients in the database. Labels are the only node and edge attributes included in this work. Our results are easily extended to include more metadata stored as key-value pairs on nodes and edges. A patient graph (trajectory) is defined as the subgraph constructed with its origin at a patient node, following edges outwards until reaching terminal nodes (type-describing nodes that have no outgoing edges). Patient graphs defined in this way are directed acyclic graphs (DAGs), i. e. , they do not contain directed cycles. We extracted a subset of patients whose trajectories contain any of the ICD-10-CM diagnosis codes I48. 0, I48. 1, I48.
Each Encounter is described by an EncounterCategory and is shaped like a star graph with Condition/ConditionType and MedicationRequests/MedicationType pairs for the diagnosis and medication events. The edge labels are ATTENDS between Patient and each Encounter, NEXT between neighboring Encounters, OF_CATEGORY to describe the Encounter, DIAGNOS between Encounter and Condition, ADMINISTRATED between Encounter and Medication, and OF_TYPE to describe the Condition/Condition-Type and MedicationRequests/MedicationType pairs. The edge labels are uniquely determined by the label pair of the ancestor and successor nodes. Note that edge labels are omitted from Fig. 1  to simplify the presentation. Figure  2  shows the frequency of the node and edge labels in the training data (see Table  1  for more details on the source data).
First, the patient visit length is the number of consecutive Encounters in the patient's trajectory. Figure  8  shows the frequency of visit lengths for real and synthetic patient cohorts, respectively. The two distributions are very similar, and both real and synthetic cohorts are dominated by short visit lengths (less than 20 Encounters, even though the input data contains a 144-length outlier). We computed an MMD of 0. 025 between the two distributions. This very low value further verifies that the generated trajectories are realistic. Next, we calculated comorbidities, defined as two or more medical conditions that exist simultaneously in the patient trajectory regardless of causal relationships. Following ref. 45  , we define the comorbidities for atrial fibrillation as Heart failure (ICD-10-CM code I50), Hypertension (I10-I15), Diabetes mellitus Fig.