dataesr/scientific-paragraphs-categorization huggingface dataset:
After proximity ligation, crosslinks were reversed and the DNA purified. Purified DNA was treated to remove biotin that was not internal to ligated fragments. Sequencing libraries were generated using NEBNext Ultra enzymes and Illumina-compatible adapters. Biotin-containing fragments were isolated using streptavidin beads before PCR enrichment of each library. The library was sequenced on an Illumina HiSeqX platform to produce ∼30X sequence coverage. The input PacBio de novo assembly and Dovetail Omni-C library reads were used as input data for HiRise, a software pipeline designed specifically for using proximity ligation data to scaffold genome assemblies (19). The Dovetail Omni-C library sequences were aligned to the draft input assembly using bwa (28). The separations of Dovetail Omni-C read pairs mapped within draft scaffolds were analyzed by HiRise to produce a likelihood model for genomic distance between read pairs, and the model was used to identify and break putative misjoins, to score prospective joins, and make joins above a threshold.
The final assembly comprised Tables  Table S1:  Characteristics of the 10 replicate locations studied over time. Years = number of consecutive year pairs analyzed, Observations = total number of observations (i. e. , the sample size in terms of numbers of individuals), D = Bayesian point estimate (median) and 95% equaltail probability intervals for D, p = predicted equilibrium stripe frequency. Location Latitude    Although the same data are used to estimate observed and predicted frequencies, the strong association detected in not a given and would not arise under all models of evolution (e. g. , those with directional evolution rather than fluctuations due to NFDS). Panel (a) shows the general relationship between the mean frequency in a time series and the equilibrium predicted for directional selection favoring a morph or negative frequency-dependent selection.
Acknowledgments. This study is part of a project that has received funding from the  European Research Council (ERC)  to P. N. , under the  European Union 's  Horizon 2020 research and innovation programme  (Grant agreement No. 770826 EE- Dynamics). Z. G. was supported by the  NSF  (DBI No. 1844941 ). The support and resources from the  Center for High Performance Computing at   the University of Utah  are gratefully acknowledged. We thank  Mathieu Joron ,  Luis-Miguel Chevin , and  Tom Reimchen  for insightful discussion.
Data availability. The full time-series dataset is available as Database S1 in the Supplementary Material. All datasets have been deposited at Dryad and are publicly available (DOI: 10. 5061/dryad. xwdbrv1k5I). Reference genomes for T. cristinae and T. podura are available from NCBI (PRJNA1010181 and PRJNA1010204). All key original code has been deposited on GitHub and is publicly available now (https://github. com/zgompert/RepeatableEvolution); final versions of code will be available on Zenodo on the date of publication.
read mapper (http://snap. cs. berkeley. edu). The separations of Chicago read pairs mapped within draft scaffolds were analyzed by HiRise to produce a likelihood model for genomic distance between read pairs, and the model was used to identify and break putative misjoins, to score prospective joins, and make joins above a threshold. After aligning and scaffolding Chicago data, Dovetail Hi-C library sequences were aligned and scaffolded following the same method. The final assembly comprised 1,313,928,163 base pairs (bps) with an N50 of 63,125,338 bps and an L50 of 7. The assembly included 13 large scaffolds corresponding to the 12 T. cristinae autosomes and the X chromosome (as described in more detail below) (FigureS6). Based on BUSCO version 4. 0. 5 (linage dataset = eukaryota odb10, 70 species, 255 BUS-COs), the assembly included 195 complete BUSCOs (188 single copy and seven duplicated), and 10 fragmented BUSCOs; 50 BUSCOs were missing.
MiRNA overexpression in cancer cells Transient miRNA overexpression. 2 'O-methylated miRNA mimics for hsa-miR-662 (MIMIC-miR-662), negative control (MIMIC-negCTRL), and FAMlabelled negative control (MIMIC-negCTRL-FAM+), were purchased from GenePharma, Shanghai. An optimised concentration of 50 nM of miRNA mimic was transfected into cells (MDA-MB-231, NW1, 4T1-luc2), using Lipofectamine 2000 (Invitrogen) as transfecting agent (Supplementary Fig. S1 ). Evaluation of transfection efficiency has been routinely performed by flow-cytometry (BD LSR II Flow Cytometer) followed analysis with Flowing Software 2. 5. 1 (Perttu Terho, University of Turku), and by real-time qPCR, using specific primers (MIMIC-negCTRL: 5'-TTCTCCGAACGTGTCACGTTT-3'; MIMIC-miR-662: 5'-TCCCACGTTGTGGCCCAGCAG-3'). U6 and SNO234 have been used as housekeeping genes (U6: 5'-TTCGTGAAGCGTTCCATATTTTT-3'; SNO234: 5'-TTCGTCACTACCACTGAGA-3').
Human osteoclastogenesis assay. Primary human osteoclasts were differentiated from human peripheral blood mononuclear cells of healthy donors as previously described  [19, 20]. Briefly, 1 × 10 4 CD14+ monocytes resuspended in RPMI medium containing 10% (v/v) FCS, M-CSF (25 ng/mL) and RANKL (50 ng/mL) were seeded in 96-well plates. After 72 h in a 5% CO 2 incubator, cell culture medium was replaced with fresh medium containing 10% (v/v) foetal calf serum, M-CSF, and RANKL with or without conditioned medium from cancer cells (1:16 dilution). This medium was changed every 3 days. At day 12, cells were fixed and stained for TRAP activity. Mature multinucleated osteoclasts (>3 nuclei) were enumerated, and the total area covered by osteoclasts measured using a Nikon NIS-Elements microscope imaging software.
, San Diego, CA, USA). RNA-seq data was aligned to GRCh38 human genome assembly using STAR v2. 7. 5c (Dobin and Gingeras,  [22] ). Transcript quantification was performed using RSEM v1. 3. 1 (Li and Dewey,  [23] ), and poorly expressed transcripts (less than 0. 5 counts per million in all samples) were eliminated for further analysis. Counts were normalized by weighted trimmed mean of M-values  [24]  using TMM function of EdgeR Bioconductor package  [25]. Differential gene expression (DE) analysis, based on negative binomial generalized linear models, was performed using EdgeR Bioconductor package to compare mimic miRNA-662 transfected cells to mimic negative control cells. Further gene set enrichment analysis (GSEA)  [26]  was performed using ClusterProfiler Bioconductor package  [27]  on genes ranked by the fold change estimated in DE analysis.
TargetScan target prediction and ClueGo Analysis TargetScanHuman 7. 0 software (https://www. targetscan. org/vert_70/, Accessed April, 2021) was used to predict hsa-miR-662 targets in human transcriptome by searching for the presence of 8mer, 7mer, and 6mer sites that matched with miR-662 seed region. Top 200 predicted targets (arbitrary threshold) were used to perform a ClueGo-based analysis  [28]  entering 'GO Biological Process', 'GO molecular functions', 'KEGG', 'Reac Pathways' and 'Reac Reactions' as databases.
Statistical analysis For clinical data, to determine the sample size for the discovery patients' cohort (N = 48), we evaluated that with a sample size of 16 for each group (MET, BONEMET, SOFTMET) for which the effect size d was set at 1. 3 -/+ (α err prob = 0. 01), we achieved a power (1-β err prob) of 0. 815 by a two-tails post hoc compute achieved power analysis. For ROC curve analysis, statistical significance was set at P < 0. 05 (two-sided test). We implemented AUC >. 65 (P < 0. 05) cut-off for generating dot plot or heatmap using Broad institute software Morpheus. For combined ROC curves, a binary logistic regression was calculated using IBM SPSS Statistics. Correlations of expression between the different miRNAs were performed by Spearman's or Pearson's correlation test. Intervene's UpSet module was used to visualize the intersection of multiple cohort sets using UpSet plots (https:// bitbucket.
org/CBGR/intervene). Survival analysis was performed dividing patients into two groups [high (value > 0. 1) and low (value <-0. 1)] based on miR-662 expression (Z-score of 2 -ΔCT ). The Kaplan-Meier estimator was used to determine the relationship between miRNA expression and patient survival. Differences in survival across the strata were calculated using a log-rank P test. For in vitro experiments, all statistical analyses on experimental data were performed using Prism GraphPad 9. 2. 0 (GraphPad Software Inc. , San Diego, CA, USA). Statistical significance was measured using parametric testing (t test or ANOVA), assuming equal variance, or non-parametric testing (Fisher's exact test). ANOVA was followed by Tukey's multiple comparison testing. The ANCOVA analysis of co-variance was performed to evaluate multiple regression for metastatic progression.
For TaqMan™ low-density array (TLDA) analysis, CT values were normalised by global mean normalisation (GMN)  [33]. Resulting delta CT values were then converted in their linear numbers (2 -ΔCT ), and subjected to quantile normalisation (QQ)  [34]  followed by differential expression unpaired analyses (DeT, Student's t test, P < 0. 05 being considered significant) using R package DEGandMore (https://github. com/zhezhangsh/DEGandMore). Multiple comparisons have been adjusted by Benjamini-Hochberg False Discovery Rate (FDR). The log2-ratio of group means and -log10 (P values) were used to generate volcano plots using R package VolcaNoseR (https:// github. com/JoachimGoedhart/VolcaNoseR), and Z-score of 2 -ΔCT values were used as an input for generating heat maps using Broad institute software Morpheus (https://software.
broadinstitute. org/morpheus/). For Cancer cell line Encyclopaedia (CCLE) analysis, a database with information on the miRNA content of cell lines obtained by RNA-seq  [35]  has been used to retrieve information of miRNA expression of 44 BC cell lines that belonged to different BC subtypes (Supplementary Table  S2 ). The violin plot was used to visualise the distribution of miRNA expression levels, which were reported as log2 read counts, and the statistical significance was evaluated using the unpaired t test, where values of P < 0. 05 were considered significant. For ClueGo-based analysis, gene/protein networks with a 'Term P value' <0. 05 and 'Group P value' <0. 05 were identified as statistically significant.
5d ), whereas no difference was observed for OCT-04, c-MYC, CD44, CD24, CTNNB1 and EZH2 mRNA expression levels (Supplementary Fig. S13 ). In addition, we performed a ClueGo-based analysis  [28]  on the top 200 predicted targets of miR-662 to evaluate which gene/protein functionally organised in GO/pathway networks could potentially be repressed by miR-662 biological functions. We identified 106 gene/protein networks (Term P value <0. 05, Group P value < 0. 05), among Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Human Reactome (R-HSA) database (Supplementary Table  S6  and Supplementary Figs. S14  and 15 ). Interestingly, in the gene network which refers to 'DOT1L (KMT4) methylates methyl-lysine-80 of histone H3 (H3K79)' we found reactome pathways involving SIRT6, NOTCH1, and NOTCH4 genes, which are very well known to regulate cell stemness  [43].
ACKNOWLEDGEMENTS We thank all lab members of  Prof. Ingunn Holen   Group  ( The University of Sheffield, UK ) for their kind support and scientific discussion. We thank  Dr. Victor Gabriel Canuas Landero  ( The University of Sheffield, UK ) for the technical support in in vivo and in vitro procedures,  Ms. Lamia Bouazza  ( UCBL, France ) for the technical support in osteoclastogenesis assays, and  Dr. Vincent Chaptal  ( CNRS, France ) for the use of the ultracentrifuge. We thank  Skelet. AL Laboratories  ( University of Sheffield, UK ) for the access to scanning facilities and data acquisition.
FUNDING PC is supported by  INSERM Cancer  ( REMOTE  project),  Université Claude Bernard Lyon 1 , the 'Project  LIA/LEA 2016'  (grant no. ASC17018CSA ), the  Weston Park Cancer Charity  (grant no. CA163 ), and the  LabEX DEVweCAN  ( ANR-10-LABX-61 ) of the University Lyon 1, within the program ' Investissements d'Avenir'  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). MC is supported by ' Ligue Régionale Contre le Cancer  ( CD71 )'. CG is supported by ' Ligue Nationale contre le Cancer (Label CG) '.
Our knowledge of viral sequence space has exploded with advancing sequencing technologies and large-scale sampling and analytical efforts. Though archaea are important and abundant prokaryotes in many systems, our knowledge of archaeal viruses outside of extreme environments is limited. This largely stems from the lack of a robust, high-throughput, and systematic way to distinguish between bacterial and archaeal viruses in datasets of curated viruses. Here we upgrade our prior text-based tool (MArVD) via training and testing a random forest machine learning algorithm against a newly curated dataset of archaeal viruses. After optimization, MArVD2 presented a significant improvement over its predecessor in terms of scalability, usability, and flexibility, and will allow user-defined custom training datasets as archaeal virus discovery progresses.
For instance, to date, fewer than 230 marine archaeal viruses have been confidentially identified among multiple metagenomics enabled, or culture-based studies  [18, 32, [34] [35] [36] [37] [38] [39] [40] [41] [42] , while a recent single global oceans survey has revealed over 488 k viral populations, most of which are presumed to be phage  [17]. In total, we estimate that there are now well documented genomes or large genome fragments available from fewer than ~380 archaeal viruses, with another 6027 putative archaeal viruses in the IMG/VR-db v3. 0,  [43]  which is a tiny fraction when compared to the hundreds of thousands of population genomes now available for phages  [17] [18] [19] [20]. An explanation for this may be that novel archaeal virus discovery is largely based on sequence homology searches against public reference databases that are populated by viruses from extreme environments and many archaeal viruses lack homology to these references  [44].
These "extreme" archaeal viruses are perhaps not good representatives of those archaeal viruses from relatively non-extreme environments, regardless of the incredible array of morphologies and lifestyles they exhibit  [29, [45] [46] [47] [48]. Thus, distinguishing bacteriophage from archaeal viruses in datasets from relatively non-extreme environments, using current approaches, remains a challenge, despite the clear genomic and evolutionary differences between phages and archaeal viruses  [29, 45, 46, [49] [50] [51] [52]. As a consequence, the ecological roles of archaeal viruses in relatively non-extreme environments remain mostly unclear, even while evidence suggests that they may be integral to biogeochemical cycling and host community dynamics  [36, [53] [54] [55]. The current approach to archaeal virus identification from metagenomic data is based on sequence similarity searches among reference databases, which is severely limiting given the dearth of non-extreme archaeal virus reference genomes.
Nevertheless, using this approach we previously developed an annotation-based tool, the Metagenome Archaeal Virus Detector or MArVD, to identify archaeal viruses and used it to discover 43 archaeal viruses from a marine oxygen minimum zone metagenomic dataset  [32]. MArVD is now ripe for an update for three reasons: (i) the original tool is reliant on other unsupported software  [56] , (ii) machine learning has emerged as powerfully enabling in virus ecogenomics for this type of classification task (i. e. , DeepVirFinder  [57] , MARVEL  [58] , VIBRANT  [59] , and Virsorter2  [60] ), and (iii) there is a growing set of new reference genome data available due to the efforts of several groups manually identifying archaeal viruses from metagenomic sequencing datasets  [32] [33] [34] [35] [36] [37]  and isolate-based datasets  [38] [39] [40] [41] [42].
Here we introduce and extensively benchmark MArVD2 (Metagenomic Archaeal Virus Detector v2. 0) as a machine learning-based upgrade to MArVD that uses curated archaeal virus data from both extreme and non-extreme environments to better leverage the genomic features representative of such archaeal viruses for novel archaeal virus discovery. MArVD2 takes as an input a dataset of viral contigs, pre-identified from tools, such as DeepVirFinder  [57] , MARVEL  [58] , VIBRANT  [59] , and Virsorter2, and returns a list of viruses with their probability of being an archaeal virus.
RESULTS AND DISCUSSION MArVD2 is a random forest classifier, implemented in the scikitlearn python package for novel archaeal virus discovery (Fig. 1 )  [61]  where it's trained and tested with separate datasets of archaeal viruses to best represent its performance in a variety of environments (Fig. 1 ). Integrating MArVD2 with machine learning introduces several practical and performance improvements over MArVD (version 1)  [32] , including enhanced usability, with less dependence on other end-user software, increased sensitivity, and greater flexibility to adapt as new archaeal virus databases emerge. MArVD2 retains the very high precision of its predecessor with increased accuracy, enabling robust wide-scale archaeal virus detection from metagenomic datasets.
Building MArVD2 Development of reference, training, and benchmarking archaeal virus datasets. To better represent archaeal viruses from both extreme and relatively non-extreme environments, we first curated several collections of archaeal viruses and phages from a variety of habitats to serve as reference datasets for comparison with the training data, training data for feature generation, and benchmarking datasets for model validation. Reference databases used for genomic feature identification included archaeal virus and phage protein clusters from publicly available repositories (NCBI nr,  [62]  and pVOGs  [63] ) and a custom made database of 206 archaeal viruses from the oceans, where new archaeal viruses are being rapidly discovered, curated herein as the OcAVdb or the Ocean Archaeal Virus Database (see below and methods for curation details)  [18, 32, 34-39, 41, 42].
The training dataset for the Fig. 1  Schematic diagram of the MArVD2 workflow. A representation of the main data processing steps and datasets leveraged in developing and benchmarking MArVD2. MArVD2, as described herein, operates in three modes. First (in green) a training dataset of curated archaeal viruses and phage and several databases of reference archaeal viruses and phage are used to develop a model for archaeal virus identification. Second (in orange) the model is implemented with additional curated archaeal viruses and phage as a benchmarking dataset to evaluate the models' performance. Third (in blue) a user will supply their own dataset of unclassified dsDNA viruses for archaeal virus prediction using the benchmarked model. See text for dataset description. random forest model generation includes 70 non-marine archaeal viruses from the curated VirSorter curated database  [64] , 350 marine putative archaeal viruses identified from the Eastern Tropical South Pacific (ETSP)  [65, 66] , and 437 randomly selected bacteriophages from viral RefSeq (v85)  [62] , the VirSorter curated database  [64] , and the ETSP dataset (Fig.
1 )  [65, 66]. Finally, a benchmarking dataset used to examine the performance of MArVD2 under a variety of constraints was comprised of 230 putative archaeal viruses and a random selection of phages with genomes larger than 10 kb from the IMG/VR-db v2. 0  [67] , along with 25 newly identified marine archaeal viruses from two stations in the Tara Oceans GOV2. 0 dataset  [17]  in environments enriched for archaea (Stations 72_MES and 122_MES). Thus, in addition to those archaeal viruses already available in the NCBI and pVOGs databases, we leverage a total of 881 other archaeal viruses from marine, hypersaline, hot spring, and anoxic environments for reference, training, and benchmarking datasets, establishing a robust base to represent archaeal viruses from both extreme and relatively non-extreme environments.
We next sought to confirm that the collected archaeal viruses for the reference, training, and benchmarking datasets were indeed archaeal viruses. Previously, this was done through meticulous manual screening of gene sharing networks, phylogenetic analysis, sequence homology comparisons, and functional and taxonomic annotations, each of which have revealed that archaeal viruses are distinguishable from phages using these approaches  [18, 32, [34] [35] [36] [37]. Thus, manual confirmation of archaea as the likely host for the reference archaeal viruses in OcAVdb, the training archaeal viruses, and the benchmarking archaeal viruses was conducted as follows. First, vConTACT2  [68]  was used to determine if the archaeal viruses would cluster amongst themselves and separate from phages as has been previously observed (Fig.
2 )  [32, 49, 50, 52]. Second, manual inspection of the per-gene functional and taxonomic annotations, provided by DRAMv  [69] , was used to identify archaeal or archaeal virus signatures in each sequence (Supplementary Table  1 ). First, network analysis  [68]  with the OcAVdb references and training archaeal viruses used for model development revealed that the majority of these archaeal viruses clustered with each other and/or other reference archaeal viruses (Fig. 2 ) (Supplementary Table  2 ). Out of the 626 archaeal viruses selected for the OcAVdb reference database and the training data, 569 were clustered into 71 viral clusters or VCs (approximately genuslevel taxonomic groups  [68] ) with 45 outliers and 12 singletons. Together these represented 18 network modules (interconnected viral clusters sharing a fraction of their genes  [68] ) that shared no overlap with phages (Supplementary Table  2 ).
The vConTACT2 network analysis further revealed groupings of archaeal viruses into modules seemingly associated with the Poseidonales or Nitrososphaeria separately, largely corroborating the predicted hosts of these viruses from their respective studies (Supplementary Table  2 )  [18, 32, [34] [35] [36] [37]. Further inspection of the functional annotation of the OcAVdb reference and the training archaeal viruses revealed that on average 17% (stdev 11%) and 27% (stdev 23%) of ORFs per sequence received any annotation, respectively, from KEGG  [70]  or viral NCBI  [62]  according to DRAMv  [69]  (Supplementary Table  1 ). The training dataset likely received more annotations due to its inclusion of a higher proportion of archaeal viruses from hypersaline environments where archaeal viruses are better characterized  [52].
Of the archaeal virus ORFs receiving any annotation in the OcAVdb reference database and the training dataset, 55% (stdev 25%) and 71% (stdev 21%), respectively, affiliated with reference archaea or archaeal viruses. In OcAVdb and training datasets, all but 6 and 2 sequences, respectively, encoded at least one archaeal virus-like ORF, and those that did not generally have a very low proportion of their genes annotated at all (Supplementary Table  1 ). Notably, early in the curation of OcAVdb, 20 contigs were removed as probable falsely identified archaeal viruses due to a low proportion of genes affiliating with reference archaeal viruses or archaea (only 9 out of 358 total annotated ORFs), and network clustering inconsistent with what is expected of archaeal viruses  [32, 49, 50, 52]. Most of these were originally identified by k-mer frequency-based host prediction methods, which can be faulty if the host dataset does not well represent the diversity of the concurrent microbial community (Supplementary Table  2 )  [71].