dataesr/scientific-paragraphs-categorization huggingface dataset:
CRISPR-Cas system detection and CRISPR array validation. CRISPR-Cas systems in Bathyarchaeia MAGs were detected using DefenseFinder  77  and further classified using CRISPRCasTyper  78. CRISPR array in Bathyarchaeia MAGs were searched with MinCED 79 (Default setting). To remove ambiguous CRISPR array that might not belong to Bathyarchaeia, all CRISPR array-containing contigs encoding proteins were searched against the nr database (2022-09) using Diamond (2. 1. 6) BLASTP  80  (-evalue 1e-5, --more-sensitive), and only contigs with at least 3 proteins assigned to Bathyarchaeia were retained. The validated CRISPR repeats were clustered with 90% identity using an all-against-all BLASTN 81 search (E-value cutoff 1e-5, word size 7).
Bathyarchaeia virus identification and annotation. We first carried out the CRISPRbased virus-host assignments. Spacers from validated CRISPR array were used as a nucleotide database to compare to viral contigs in the IMG/VR v3 41 database using BLASTN  81  with parameters (-task blastn-short -evalue 1e-5). Only viral contigs harboring protospacers with 100% coverage and a maximum of one mismatch were considered as Bathyarchaeia viruses. In addition, some viruses might form proviruses that cannot be detected by CRISPRprotospacer search. Therefore, we used VirSorter2 v. 2. 2. 3  82  to identified potential viral sequences in Bathyarchaeia MAGs. Specifically, all the Bathyarchaeia contigs were filtered using VirSorter2 (--include-groups dsDNAphage, ssDNA --min-length 5000 -min-score 0. 5) and only contigs with identifiable viral structural proteins and at least one ORF identified through a BLASTP search against the nr database as Bathyarchaeia were retained.
Virial genome completeness estimation and the potential host regions trimming were conducted by CheckV 0. 8. 1  83. Coding sequences were identified using Prokka  84  with parameters (--kingdom viruses --gcode 1). All the Bathyarchaeia viral proteins identified as describe above were annotated with DRAM  85  (viral mode) and HHblits  86  (MSA generated with UniRef30_2020_06 with 3 interactions, Evalue 1e-6, MSA was compared against the PDB_mmCIF70_14_Apr, SCOPe70_2. 07, Pfam-A_v35 and UniProt-SwissProt-viral70_3_Nov_2021 databases with parameters -Z 250 -loc -z 1 -b 1 -B 250 -ssm 2 -sc 1 -seq 1 -dbstrlen 10000norealign -maxres 32000). All the viral genome maps were visualized using Proksee  87.
Bathyarchaeia viral taxonomy assignment. To determine the taxonomic status of Bathyarchaeia viruses, the whole genome network analysis was carried out with vConTACT2  88  (default parameters) against the Viral RefSeq v207 database as well as archaeal viruses proposed in the latest International Committee on Taxonomy of Viruses Report (VMR_MSL38_v1). The resulting networks were visualized using Cytoscape v3. 9. 1  89  with an edge-weighted spring embedded model. For viruses in the realm Duplodnaviria, proteome-scale phylogeny was constructed using the VipTree version 3. 4  90. The analysis was carried out with archaea viral families in the realm Duplodnaviria, according to the latest ICTV taxonomy classification (04/25/2023). For filamentous viruses, proteome-scale phylogeny was constructed by using VICTOR  91  with all known members of the Tokiviricetes class.
Bathyarchaeia viral CRISPR spacer-protospacer match analysis CRISPR array in Bathyarchaeia viral genome were detected with MinCED (-minNR 1). All the spacer obtained from Bathyarchaeia virus were BLASTN search (-task blastnshort) with viral database IMG/VR v3 and plasmid sequences database PLSDB database  98  (v. 2021_06_23_v2) as well as Bathyarchaeia MAGs in this study. Direct repeats were searched against CRISPR-CAS++ databases  99  and Bathyarchaeia CRISPR repeat dataset with BLASTN (-evalue 0. 01).
Acknowledgments We would like to express our deepest gratitude to the following professors for their generous support and data sharing, which has been invaluable to our research. Special thanks go to  Professors Natasha Ivanova ,  Robert Kelly ,  Tanja Woyke ,  William P. Inskeep ,  Jim Fredrickson ,  Roland Hatzenpichler ,  Brian P. Hedlund , and  Ramunas Stepanauskas  for their enthusiastic assistance and valuable suggestions. We sincerely appreciate their contributions. Also, thanks to  Dr. Jie Pan ,  Mr. Chengxiang Gu  and  Mr.
, 2017) , TEtranscripts  (Jin et al. , 2015) ). In the past years, many methods have surfaced to take advantage of short-read sequencing datasets and circumvent the multi-mapping problem in order to develop copy-level analysis (for a review see  (Lanciano & Cristofari, 2020) ). These methods are based on different algorithms that are able to statistically reassign multi-mapped reads to unique locations, for instance with the expectation-maximization algorithm used in TEtranscripts  (Jin et al. , 2015) , SQuIRE  (Yang et al. , 2019)  and Telescope  (Bendall et al. , 2019). In the past years, long-read sequencing has become an attractive alternative to study TE biology. Such reads are able to refine TE annotation  (Jiang et al. , 2019; Panda & Slotkin, 2020) , pinpoint new TE insertions  (Mohamed et al.
Reference genome and annotation The dmgoth101 genome assembly was produced from Oxford Nanopore Technologies (ONT) longread DNA sequencing and described in  (Mohamed et al. , 2020). Genome assembly has been deposited in the European Nucleotide Archive (ENA) under accession number PRJEB50024. Gene annotation was performed as described in  (Fablet et al. , 2022). Briefly, we used LiftOff  (Shumate & Salzberg, 2021)  on FlyBase D. melanogaster annotation (dmel-all-r6. 46. gtf. gz), with the option -flank 0. 2. In order to locate and count the reads aligned against TE insertions, we produced a GTF file with the position of each TE insertion. We have used RepeatMasker with DFAM dataset from D. melanogaster TE copies and then used OneCodeToFindThemAll to merge LTR and internal parts of TE in one unique feature.
It is important to note that we do not have replicates for the long-read dataset. Our primary goal for this experiment was to evaluate the potential of this technique to identify the largest number of expressed TE copies and isoforms. Enriched full-length cDNAs generated from ovaries and testes were then processed into libraries using the SQK-LSK109 ligation kit (ONT) using 3 µg as starting material. The two libraries were sequenced separately in two flow cells R10 (FLO-MIN110) with a MinION device (Guppy version 2. 3. 6 and fast basecalling). We obtained 1 236 000 reads for ovaries and 2 925 554 for testes that passed the default quality filter (>Q7). Quality control was performed with NanoPlot v1. 39. 0  (De Coster et al. , 2018). The median read length was 1. 4 Kb, and the median quality was 8.
4 (i. e. one error every 8 bases, Figure  S2 ). Data are available online at the BioProject PRJNA956863. Reads were mapped to the dmgoth101 genome using minimap2 (version 2. 17-r974-dirty)  (Li, 2018)  using the splice preset parameter: "minimap2 -ax splice FC30. fastq dmgoth101_assembl_chrom. fasta -o FC30. bam". Ninety percent of reads were mapped to the genome (Table  S1 ). The vast majority (99%) mapped to a unique location (i. e. had no secondary alignment reported by minimap2). Out of the reads with several locations reported, 90% had a unique best location, i. e. the alignment score (AS flag in the SAM format) was higher for one of the locations. Indeed, if a read has several alignments with the same alignment score, then this means the read stems from exact repeats in the genome and they cannot be told apart, hence, we cannot know which copy is transcribed.
However, if a read has several alignments with distinct alignment scores, then it means that the read stems from inexact repeats. The presence of this read in the dataset means that one of the copies is transcribed and we consider that it is the one with the highest alignment score. While it could be possible that the read actually stems from the copy with suboptimal alignment, this is highly unlikely because it would mean that there is a sequencing error at the position of the divergence between the two copies of the repeat. A sequencing error in any other position of the read would cause a decrease in the alignment score of both locations. An example of a read that maps to several locations, one with an alignment score larger than the others is given in Figure  S3. We also noticed that some reads were only partially mapped to the genome.
Short-read RNA-seq and analysis RNA extraction and short-read sequencing were retrieved either from  (Fablet et al. , 2022) , at the NCBI BioProject database PRJNA795668, or performed here and available at BioProject PRJNA981353. Briefly, RNA was extracted from 70 testes and 30 ovaries from adults aged three to five days, using RNeasy Plus (Qiagen) kit following the manufacturer's instructions. After DNAse treatment (Ambion), libraries were constructed from mRNA using the Illumina TruSeq RNA Sample Prep Kit following the manufacturer's recommendations. Libraries were sequenced on Illumina HiSeq 3000 with paired-end 150 nt reads. Short-read analysis was performed as in  (Fablet et al. , 2022) , and SQUIRE  (Yang et al. , 2019)  was used for mapping and counting TE copy-specific expression. A detailed protocol on SQUIRE usage in non-model species can be found here https://hackmd.
To rule out these cases, all reads covering less than 10% of the annotated TE were discarded. Second, in many instances, the read overlapped both a gene and one or several TEs. In these cases, the feature for which the coverage was best (see Methods and Figure  S6-7 ) was selected. Overall, after applying these filters, 1 322 reads in ovaries and 8 219 reads in testes were assigned to TE copies (Table  S2 ). Out of these, 37-48% overlapped only one TE, 8-18% overlapped with multiple TEs, 16-21% were located in the intron of a gene, and 18-34% overlapped with a gene exon (but were assigned to the TE, not the gene) (Figure  1E ). To ensure this long-read dataset is able to recover transcripts encompassing all TE copy lengths present in the genome, we compared the length distribution of all TE insertions with the length of all mapped reads (Figure  1F ).
B. Comparison of TE expression ratio between testes and ovaries ONT long-read datasets. C. Comparison between Illumina and ONT datasets for estimating the expression levels at the TE family level. Each dot is a TE family. TEcount is used for short reads. The correlation between quantifications by both technologies is higher for testes (rho=0. 79) than for ovaries, (rho=0. 59) where the coverage is weaker. In both cases, it is compatible with what is observed for genes.
Introduction The various types of non-coding RNA molecules exert their biological functions either by base-pairing mechanisms or through their three-dimensional structure. As for proteins, experiments for determining the spatial conformation of RNA chains are costly, which has led to the development of computational methods for predicting the biologically active ("native") fold from the sole ribonucleic acid sequence  (1) (2) (3) (4) (5). However, these methods dedicated to RNA have yet to reach the same accuracy as their protein-specific counterparts, such as AlphaFold2  (6)  or ESMFold  (7). The fact that the native structures available in the Protein Data Bank (PDB) are far less numerous and diverse for RNA than for protein molecules mainly explains this slower rate of progress in solving the RNA folding problem.
In this paper, we bring a comprehensive interpretation of quality measurement and model quality assessment of RNA 3D structures. We describe our RNAdvisor tool, before presenting the benchmarks we conducted thanks to RNAdvisor. Different benchmarks were carried out, considering three datasets available online. We evaluated the scoring functions, and measured the relationship between the different metrics, and between the scoring functions and metrics. We also measured the running time, as well as the CO 2 equivalent consumption. All these benchmarks are reproducible, open-source, and accessible at https://github. com/EvryRNA/rnadvisor_results.
A. General metrics The general metrics give an overall idea of the quality of a prediction. They are usually based on an overall distance averaged throughout the structure. The most used metric is the RMSD, which gives an overall predicted model evaluation. An improvement of this metric was proposed with RMSD  (19) , which incorporates RNA features. On the other hand, the CLASH score  (20)  assesses the overlaps of atoms and doesn't consider the atom deviations compared to the previously mentioned metrics. The main advantage of general metrics is the quick overview of the nativity of the structure. It gives a unique value, an averaged similarity score over the reference. The RMSD is almost always used as a criterion to assess the quality of a computational approach in a database. Nonetheless, it is limited in explaining the limits of a prediction.
D. Knowledge-based scoring functions Prediction-based methods like NAST  (11) , HiRE-RNA (4) or SimRNA (  12 ) use an adaptation of the free energy in their discriminative phase. A common approach uses knowledgebased statistical potentials considering structures to create a quality measurement score. It has been proven to work well for proteins, such as the one used in AlphaFold 2  (6). These potentials are said to be derived from Boltzmann formulations. They rely on a comparison with non-native base pair interactions, known as a reference state. The reference state should ideally come from a set of non-redundant decoy conformations where no interactions between atoms appear. Unfortunately, no ideal dataset exists  (25) , but approximations of reference states have been proposed through the years  (26) (27) (28) (29) (30) (31).
Adaptation to RNA has been studied  (32)  and remains limited by the lack of a large and representative RNA dataset. Most knowledge-based approaches to assess RNA structure nativity employ an all-atom distance potential and use averaging reference states, like 3dRNAScore  (33)  or RASP  (16). The challenge is to find good structural features that consider RNA conformational specificities to distinguish native and non-native folding. Methods like SCORE  (19)  or DFIRE-RNA  (18)  consider relative orientation to incorporate RNA flexibility. Short and long-range interactions are considered differently with different reference states in the new potential rsRNASP  (17). The main limitation of knowledge-based scoring functions is the lack of a dataset of reference state decoys.
E. Deep learning scoring functions With the recent success of AlphaFold2 (  6 ) and its deep architecture, MQAP scores have been developed like RNA3DCNN  (34)  or ARES  (35). They input different characteristics like chemical type or atom position. They use available native conformations to learn a score without explicitly using a reference state. The objective is an RMSD-like metric, meaning that the network learns atom deviation properties to assess structure predictive quality. The architecture is based on a neural network with either convolutional layers or graph neural networks. They rely on decoy datasets generated by either FARFAR 2 (2) for ARES, or relaxed structures by molecular dynamics from PDB for RNA3DCNN. ARES and RNA3DCNN scoring functions remain limited by the current deep learning drawbacks: the lack of interpretability and the need for large datasets.
As the number of solved RNA 3D structures is low, deep learning approaches could easily lack generalization to new unseen structures. Datasets considered are biased by either the chosen model creating decoys or the method to relax structures. No ideal scoring function exists, and the available scores can also be complementary: one score can weigh more dihedral angles, whereas the other could consider chemical types. As no ideal metric exists, some scoring functions could be more linked to a given metric, making the ranking more difficult. A complete description of the different scoring functions is available in Supplementary Materials, as well as the different implementation languages and the source codes.
Benchmark To evaluate the performance of a scoring function, a common practice  (16, 17, 34, 35)  is to compare the rank obtained by the native structure in a set of decoys. In this section, we first describe the three datasets of decoys used for the experimentation, followed by a study of the link between existing metrics. Then, we examine the performance of scoring functions, followed by a study of their correlation with metrics. We finally provide a benchmark of computation time and CO 2 emissions for scoring function and metrics.
F. Datasets We used three datasets, named Test Set I, Test Set II and Test Set III, to assess the relations between scoring functions and metrics. The first two datasets have decoys generated by two distinct strategies widely used to compare scoring functions  (16, 17, 34, 35). The last dataset is a real-case scenario where 3D structures from different model predictions should be ranked by nativity. Test Set I 1 is composed of 85 RNAs with decoys generated by MODELLER  (39) , a predictive model that is used to create decoys with different set of parameters. It uses Gaussian restraints for atom distances and dihedral angles, leading to 500 decoy structures for each RNA. The decoys are close to the native structures as only minor changes are made in the decoy creation. Test Set II 2 corresponds to the prediction-models (PM) subset from rsRNASP  (17).
It consists of 20 non-redundant single-stranded RNAs with decoy structures generated by four RNA 3D models (10 per model): FARFAR 2 (2), RNA-Composer  (40) , SimRNA (12) and 3dRNAv2. 0  (41). It leads to 20 RNAs with 40 decoy structures for each native RNA. The created decoys are less close to the native structure as they use predicted models to create the decoys. Test Set III 3 is the RNA-Puzzles_standardized dataset. It comes from the competition that reproduces the protein CASP challenge for RNA: RNA-Puzzles  (37). It contains 21 RNAs and dozens of decoy structures for each RNA. It is commonly used as the most realistic test set to assess the generalization properties of models. The decoys are not all close to the native structure.
H. 3. Scoring functions and metrics relationship. We computed the ES and PCC scores for each data set, each available scoring function and metric. We considered for the metrics the RMSD, INF (also named INF all , as we considered the averaged value over base-pairing and base-stacking interactions), DI, MCQ, TM-score, GDT-TS, CAD-score, lDDT and RMSD. We did not include P-VALUE or CLASH score, as P-VALUE is like a condition-metric to assess the nonrandomness of the prediction. The CLASH score computation failed for most RNA molecules, leading to non-reliable results for this metric. The results for the different datasets are given in Supplementary file. A summary of the most correlated metrics for each scoring function is provided in Table  4 , where each best-related metric is counted for all three datasets.
MCQ has a computation time of less than 20 seconds compared to less than 1min17 for RNAs of 2800 nucleotides. CADscore has a high computation time, with more than 2min20 for RNAs of 2800 nucleotides. Finally, the TM-score has a high computation time of 5min30 for a sequence of 2800 nucleotides. As computation methods can have environmental impacts, we also included carbon footprints of structural assessment methods. We computed for each dataset the equivalent CO 2 measurements for an RNA (averaged over the different decoys), using CodeCarbon tool  (42). The results are shown in Figure  5. We observe an overall higher consumption of CO 2 for Test Set I, which is explained by the long RNAs in this dataset. rsRNASP and CAD-score have the highest CO 2 consumption (with around 0. 011 g/CO 2 per RNA), followed by RASP and TM-score.
I. Discussion In these experiments, we found that rsRNASP outperforms the other scoring functions in finding the native structures among the three datasets while being correlated to lDDT, TM-score, INF, RMSD, GDT-TS and CAD-score. It comes with a price: its computation time highly increases with the number of nucleotides of an RNA sequence. Metrics also correlate between them: INF implies having good interaction accuracy, reducing the contact-area differences and the associated CAD metric. It is also correlated to RMSD, an improved RMSD considering the differences in the base interaction network. The MCQ metric seems to be the only metric that isn't correlated to the others. Atomic deviation metrics tend also to be correlated: lDDT, TM-score, RMSD and RMSD. We advise using MCQ as an evaluation metric to assume RNA nativity with DI (RMSD and INF) and lDDT (or TMscore or GDT-TS, as they are correlated).
ACKNOWLEDGEMENTS This work is supported in part by  UDOPIA-ANR-20-THIA-0013  and performed using HPC resources from  GENCI/IDRIS  (grant  AD011014250 ). It was also partially supported by  Labex DigiCosme  (project  ANR11LABEX0045DIGICOSME ), operated by  ANR  as part of the program " Investissement d'Avenir " Idex ParisSaclay  ( ANR11IDEX000302 ).
SCORE and DFIRE-RNA have almost identical results, with 99 and 103 found structures out of 126 overall. rsRNASP does not perform as well as the other scoring functions for Test Set I but outperforms them for the two other datasets. It leads the overall native structures found with 111 out of 126. Details of the average rank of the native structure for each dataset are provided in Supplementary file. H. 2. Scoring function ranking.
Pan-transcriptome dataset generated To gain a comprehensive overview of gene expression variation at the species level, we performed RNA sequencing for a collection of 1,032 S. cerevisiae natural isolates  20, 22  and obtained 969 high quality transcriptomes with at least 1 million mapped reads (Figure  1 , Table  S1 ). The genomes of all these isolates have been previously completely sequenced and extensively characterized, reflecting the broad genetic diversity of the species in terms of single nucleotide polymorphisms (SNPs), gene copy number variants (CNVs), genome content variation (e. g. , introgressions, horizontal gene transfers) as well as aneuploidy and ploidy level variation (Figure  1A-B ). The final set of 969 isolates are distributed across 26 well-defined clades that capture the ecological and geographical diversity of the species, including various domesticated and wild subpopulations (Figure  1A-C ).
Using the previously determined yeast pangenome  20  , we obtained the expression levels for 6,445 transcripts, including 4,977 core ORFs as well as 1,468 accessory ORFs that are variably present in isolates (Figure  2A , Table  S2 ). This dataset constitutes the most comprehensive pan-transcriptomic catalogue within a single species to date and is essential to better understand what are the main drivers of gene expression variation, and consequently the key players that shape the transcriptional landscape in a population (Online Datafile 1).
Accessory genes have unique transcriptional behavior This population-scale dataset first allowed us to explore the transcriptional behavior of each of the 6,445 genes. Within this population, each gene can be characterized by its overall expression level as well as its variation across the 969 isolates. We therefore defined two metrics, namely "abundance", which corresponds to the average expression level for a given gene across samples; and "dispersion", which describes the variance across samples. The abundance is calculated as the mean log2 of the normalized read counts (transcript per million, or TPM) across isolates for which the gene is present in their genome. For the dispersion, we used the mean absolute deviation (MAD), which is more robust to outliers and do not assume normality of the expression levels compared to standard deviation (Methods).