dataesr/scientific-paragraphs-categorization huggingface dataset:
The NCBI NT sequences were identified via an RdRP scan procedure similar to the procedure described above (see RdRP identification). The previously published set was made from an expansive set of Leviviricetes described by  Callanan et al. (2020) , the ''Yangshan-assemblage'' and other described by  Wolf et al. (2020) , and the proposed Plastroviruses group described by  Lauber et al. (2019) , as well as several RdRPs identified in the ocean atlas of genes  (Carradec et al. , 2018; Salazar et al. , 2019). Following their aggregation, these sequences underwent a similar procedure described for the metatranscriptomic sequences identified in this work (i. e. length filtration, clustering, and RdRP core domain extraction). The eventual sequence set was labeled as ''Known'' (i. e. not novel), and noted as such in the data generated by this work (e.
These criteria were selected as a quality assurance measure, so that the recovered contigs would be mostly contained within the ''VR1507'' contig counterpart (this large expansive data set is available in project's Zenodo repository, see data and code availability). This envelopment criteria was added to avoid capture of chimeric, or otherwise uncertain, nucleic regions that extend over the ''VR1507'' query. The filtered bulk contig set was combined with ''VR1507'' and consisted of 2,658,344 contigs (termed ''Add1507''). To ascertain that this procedure was adequately stringent in avoiding the capture of false positives, we verified if we carried it out on non-RNA virus containing DNA metagenome, no contigs would be captured. For this end, we used a recently published high quality bovine (Rumen) DNA metagenome (i.
Taxonomic affiliation of clades Tree leaves with existing taxonomic information were identified by mapping (MEGA-BLAST, E-value < 1e-30, query coverage R 95%, subject coverage R 95%, Alignment length > 200, Identity R 98%, (Alignment_length)/Query_length > 0. 95) VR1507 sequence set to the latest ICTV data at the time of analysis (July 20, 2021 release of the Virus Metadata Repository (VMR) file, corresponding to MSL36, and available at https://talk. ictvonline. org/taxonomy/vmr/m/vmr-file-repository/13175). Overall, 2,765 contigs were mapped, and the ICTV taxonomic information was cloned to the VR1507 queries based on the highest score. For the reminder of VR1507 contigs, we performed a similar procedure using the NCBI's NR database (these amount to an additional 6,878 mapped contigs, though a non-negligible amount of those lacked taxonomic information or matched abolished taxonyms).
0. 1 with default parameters  (Skennerton et al. , 2013). Next, all arrays based on repeats corresponding to known CRISPR arrays in Roseiflexus sp. RS-1 (Table  S3 ) were identified and the corresponding spacers collected and filtered as previously described. RNA virus sequences as well as DNA virus sequences from the IMG/VR v3 database  (Roux et al. , 2021)  were compared to this database of Roseiflexus sp. RS-1 spacer arrays using blastn (v2. 9. 0) with options ''-dust no -word_size 7''. Sequences from putative RNA phages infecting Roseiflexus sp. RS-1 were first identified based on hits to R 1 RS-1 spacer with % 1 mismatch across the whole spacer length. For these selected phages, hits with up to 4 mismatches across the spacer length were then collected to enable the detection of more distant virus-spacer hits.
407,  (Katoh and Standley, 2013)  and used as input to an hhsearch against the virus-focused uniprot public database (uniprot_sprot_vir70), and a custom database made from capsids of known partitiviruses and picobirnaviruses (available in the project's Zenodo repository, see data and code availability  ''Partiti_Picob_CP. tar. gz'' and PC1_PROMALS3D_new. hhr).
Habitat distribution and relative abundance estimation For visualisation purposes, location, ecological, and taxonomic information for each metatranscriptome were obtained from the IMG and GOLD databases. Specifically, GPS coordinates and ecosystem classification were obtained from GOLD, with the ecosystem information further grouped in custom categories (Table  S4 ). To roughly estimate the host diversity present in each metatranscriptome, the taxonomic information of all contigs as predicted by the IMG annotation pipeline  (Clum et al. , 2021)  was queried at the domain level, i. e. Bacteria, Archaea, Eukarya, and Viruses. The ratio between the number of contigs assigned to Bacteria and Archaea and the number of contigs assigned to Eukarya was then used as a proxy to determine ''Prokaryote-dominated'' from ''Eukaryote-dominated'' datasets.
Specifically, datasets with a ratio of Eukaryote-affiliated to Prokaryote-affiliated contigs % 0. 3 or R0. 7 were considered as ''Prokaryote-dominated'' or ''Eukaryote-dominated'', respectively, while other datasets were considered as ''Mixed''. The map was drawn using the packages matplotlib v3. 3. 4 and basemap v1. 2. 2 for python 3. 8. 5  (Hunter, 2007). For read mapping, a dereplicated set of RNA virus sequences (95% ANI over 95% AF, established using CheckV anicalc. py and aniclust. py scripts;  Roux et al. , 2021) , was established, hereafter ''NR-mapping'' dataset. Quality-trimmed reads (sensu;  Clum et al. , 2021)  from 3,998 metatranscriptomes (Table  S4 ) were then mapped to this dataset as follows. First, contigs from each metatranscriptome were compared to the NR-mapping dataset using blastn v2.
9. 0+ (E-value % 0. 01). All contigs with cumulated blast hits of R 90% average nucleotide identity covering R 80% of the shortest sequence were considered as putative RNA viruses. All reads mapping to contigs identified as putative RNA viruses and all unmapped reads were extracted from the existing IMG read mapping information, and mapped de-novo on the NR-mapping dataset using bbmap v38. 81  (Bushnell, 2014)  with the following options: ''vslow minid=0 indelfilter=2 inslenfilter=3 dellenfilter=3''. This step was done to reduce the computing time and the risk of false-positive mapping by excluding all reads mapping to non-viral metatranscriptome contigs. The resulting bam files were then filtered with FilterBam (https://github. com/nextgenusfs/augustus/tree/master/auxprogs/filterBam) retaining only mapping at R50% identity and R50% coverage, and genomecov from bedtools v2.
Domain annotation To perform an initial domain annotation of the proteins encoded by RdRP-containing contigs, we used hmmsearch (from the HMMER V3. 3. 2 suite)  (Finn et al. , 2011; Wheeler and Eddy, 2013)  to match these proteins to hidden markov models (HMMs) gathered from multiple protein profile databases using a maximal E-value of 0. 001 (PFam 34, COG 2020 release, CDD v. 3. 19, CATH/Gene3D v4. 3, RNAVirDB2020, ECOD 2020. 07. 17 release, SCOPe v. 1. 75) (Andreeva et al. , 2014 (Andreeva et al. , , 2020;; Cheng et al. , 2015; Galperin et al. , 2021; Lu et al. , 2020; Mistry et al. , 2021; Sillitoe et al. , 2021; Wolf et al. , 2020). We supplemented this set of HMMs with a custom collection of profiles with bacteriolytic functions (termed ''LysDB'' -available in the project's Zenodo repository, see data and code availability).
LysDB was built from (1) manually reviewed profile entries from public databases which we could link to GO terms related to cell lysis by viruses, or virus exit from host cell, and (2) custom profiles for ''Sgl'' proteins, which were experimentally demonstrated by Chamakura et al to induce cell lysis  (Chamakura et al. , 2020). Additionally, we used InterProScan (v. 5. 52-86. 0) to scan the protein sequences using MobiDBLite (v2. 0), Phobius (v. 1. 01), PRINTS (v. 42. 0), TMHMM (v. 2. 0c)  (Attwood et al. , 2012; Jones et al. , 2014; Ka ¨ll et al. , 2004; Ka ¨ll et al. , 2007; Krogh et al. , 2001; Potenza et al. , 2015). Because the public protein profile databases that were used for initial annotation might contain HMMs that represent polyproteins, which span multiple functional domains, we developed and employed a procedure to identify such profiles which were masked from the subsequent annotation process.
For this procedure, we first used the hmmemit command to convert HMMER profiles into multiple sequence alignments, which were then used as input to an all-versus-all profile comparison performed using HH-Suite. Next, putative polyprotein profiles were identified by flagging the profiles that encompassed at least two other non-overlapping profiles (''get_polyproteins. ipynb'' script, see data and code availability). The unmatched regions between the polyprotein domains were extracted to create a set of conserved, yet unknown domains, termed ''InterDomains''. Additionally, profiles with over 1000 match states (defined as columns with less than 50% gaps) were manually examined using HHpred. Several of the identified polyprotein profiles were split into their constituent domains. Subsequently, all hmmsearch results were aggregated and profile matches were prioritized based on their classification level (uncurated profiles, or ones of unknown function (e.
g. ''DUF'') were deprioritized) and by their relative alignment statistics. To improve the quality of the functional annotation of the domain profiles and to assign functions to unannotated profiles we identified clusters of similar profiles (clans, hereafter). First, profiles with at least one hit in the initial annotation pass were extracted from their original DB, reformatted as HH-Suite's HHMs (as described above) and used for an additional all-versus-all step. The output of this profile comparison was then used as input to a graph-based clustering process using the Leiden algorithm (''get_clan_membership. ipynb'' script, see data and code availability), which identifies clans as communities of highly similar domains. Clan membership was then used to improve the coverage of the functional annotation by transferring annotation from functionally annotated profiles to other clan members.
Briefly, this procedure followed a consensus-based label assignment. For example, a clan with 12 profiles labeled as ''RdRP'', and 2 ''unclassified'' profiles, was set as an ''RdRP'' clan and the 2 unclassified members were reclassified as ''RdRP''. Cases of conflicts were either left unresolved, or by opting to the lowest denominator. For example, a clan with 4 ''unclassified'' profiles, that also had 12 member profiles labeled ''Super family 2 Helicase'' and an additional 10 member profiles labeled ''Super family 1 Helicases'', was set to ''Helicase-uncertain'', and this label was extended to those 4 ''unclassified'' members. All subsequent profile matches passing a predefined cutoff (E-value % e-7, score R 9, alignment length R 8[AA]). were used to generate a new custom profile database, in a process similar to the one used for RdRPs (see above).
Only clusters with R 10 sequences, sharing the same functional classification, were used to generate HMMs. This profile set was then supplemented by most of the profiles from the above-mentioned RNAVirDB2020 database, as well as several dozen select profiles from the other databases (this final profile database termed ''NVPC'' is available via the projects Zenodo repository, see data and code availability). Finally, we queried the six-frame translations of the 330k contig set using hmmsearch as described above, using the new profile database. (Figure  S3  -Annotation pipeline). Subsequently, we generated tentative genome-maps for z4-20 representative contigs for each of the 400+ identified families (novel and established) using GGGenomes (https://github. com/thackl/gggenomes), which were then manually examined to identify novel domains as well as uncommon domain fusion and segmentations.
Prompted by this observation, we searched the entire VR1507 contig set against the SILVA rRNA database (BLASTn against SILVA SSU & LSU Ref NR99, default parameters)  (Quast et al. , 2013) , and manually examined 40 contigs encoding ribosomal proteins identified in the ''domain annotation'' section, to ribosomal protein profiles in the public databases (e. g. Ribosomal protein L3 PF00297. 24) Over all, we flagged 75 potential chimeras of these types, (23 of which originate from the previously published sources, see Table  S6 , sheet ''rRNA_summary'' for details). Only the RdRPs of these suspect contigs were used in downstream analyses, whereas the rest of the contig was disregarded. 4. The DNA subtraction we performed drastically reduced the abundance of chimeras that consisted in part from RNA virus sequences and in part from DNA encoded ones, whether rRNA or mRNA.
Obviously, however, this procedure cannot eliminate chimeras that consist of portions of different RNA virus genomes. Because such chimeras would be difficult to differentiate from bona fide recombinant virus genomes, we employed a heuristic to identify these using the domain annotations to detect contigs with duplicated full-length RdRP footprints. These were deemed chimaeric because RNA viruses normally encode a single (full length) RdRP. We found a single such case, ND_250651, a chimera that is part levivirus, part cystovirus. Quantitative comparison with recently published RNA virus discovery endeavors 44,779 RdRPs from the Tara project were downloaded from https://datacommons. cyverse. org/browse/iplant/home/shared/iVirus/ ZayedWainainaDominguez-Huerta_RNAevolution_Dec2021. Serratus project RdRPs were represented by 296,623 unique PalmDB sequences that were downloaded from https://github.
p2 against the subject of length L and the hit footprint of q1. q2, the query was trimmed to max(1,p1-q1-1). min(K,p2+L-q2) to account for missing parts of the subject). Queries without a significant hit to PalmDB were left untrimmed. The full set of sequences was pooled together and clustered using MMseqs2 with sequence identity thresholds of 0. 9 and 0. 5 (-min-seq-id 0. 5/0. 9 -c 0. 333 -e 0. 1 -cov-mode 1 -cluster-mode 2). All sequences were classified into four categories: i) "known" (GenBank and other published sources from the current e9 Cell 185, 4023-4037. e1-e10, October 13, 2022 Resource dataset (see STAR Methods ''contig set augmentation with published genomes''), ii) "RVMT" (RNA Virus MetaTranscriptomes from the current dataset), iii) Serrarus and iv) Tara. Clusters were examined for the presence of members from each of the four sets, and the cluster set intersections are listed in Table  S8.
ADDITIONAL RESOURCES In hope of providing a long lasting community resource, we created an accompanying interactive web portal (riboviria. org) that allows users to download portions of the data generated in this work based on phylogeny and data type (e. g. , a subset of the domain annotations for all contigs affiliated with a certain family). Both programmatic and graphical access to the data are supported through the web portal. The website's code is also available under the MIT License at github. com/Benjamin-Lee/riboviria. org. For all taxonomic levels, this platform includes raw nucleic sequence, phylogenetic trees, metadata, and annotations. Flowchart diagram visualizing the procedures used in the domain identification and functional annotation sections of the project.
ResourceData and code availability All original data and code produced in this work is freely and fully available through several venues (DOIs also listed in the key resources table):d All the data, code, results produced in the course of this project, as well as the latest release of the accompanying interactive web portal (https://riboviria. org), are available via CERN's Zenodo repository (https://doi. org/10. 5281/zenodo. 6553771). This project is intended to serve as a community wide resource. As such, the Zenodo repository includes the additional information and various intermediary results and secondary analyses, such the predicted coding sequences, host assignments, phylogeny and taxonomic affiliation, raw domain hidden markov model (HMM) search matches, additional domain profile databases generated in this work (e.
g. alignments, HMMs, original seed sequences and predicted function) as well the nucleic sequences for both the expanded (2. 6M metatranscriptome derived) contig set and the manually consolidated ''Reference Set'' (see STAR Methods). d As noted above, the Zenodo deposit includes the original code produced in this study, which corresponds to the latest version of the project's GitHub repository, which is available under the open-source MIT License at https://github. com/UriNeri/RVMT. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
ACKNOWLEDGMENTS The authors would like to thank  Shai Zilberzwige-Tal ,  David Burstein ,  Adi Stern ,  Leah Reshef , and  Omry Lieber  for helpful discussions. U. G. and U. N. are supported by the  European Research Council  ( ERC-AdG 787514 ). U. N. is supported by a fellowship from the  Edmond J. Safra Center for Bioinformatics at Tel Aviv University. Y. I. W. and E. V. K. are supported through the  Intramural Research Program  of the  US National Institutes of Health (National Library of Medicine ). V. V. D. was partially supported by  NIH/NLM/NCBI   Visiting Scientist Fellowship. The work of the  U. S. Department of Energy Joint Genome Institute  (S. R. , A. P. C. , I. M. C. , N. I. , D. P. -E. , N. C. K. , and all JGI co-authors), a  DOE Office of Science User Facility , is supported by the  Office of Science of the U.
S. Department of Energy  under contract no. DE-AC02-05CH11231. M. K. was supported by l' Agence Nationale de la Recherche  grants  ANR-20-CE20-009-02  and  ANR-21-CE11-0001-01. D. K. was funded by the  European Social Fund  under no. 09. 3. 3-LMT-K-712-14-0027. D. A. B. is supported by grant  NNX16SJ62G  from the  NASA   Exobiology program , and by grant  DE-FG02-94ER20137  from the  Photosynthetic Systems Program, Division of Chemical Sciences, Geosciences, and Biosciences (CSGB) ,  Office of Basic Energy Sciences of the U. S. Department of Energy. We gratefully acknowledge the contributions of many scientists and principal investigators, who sent extracted genetic material for isolate genomes, environmental metagenomes, and metatranscriptomes, or sequencing results as part of the  Department of Energy Joint Genome Institute Community Science Program  and allowed us to include in our study the RNA virus sequences detected in these publicly available data sets regardless of publication status.
Materials availability This study did not generate new unique reagents, physical samples, or specific biological material. As a computational project, the input for this study is publicly available as detailed below in ''metatranscriptome acquisition''. All results and output of this study are described below in the ''data and code availability'' section. REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data All original data and code produced in this work This paper https://doi. org/10. 5281/zenodo. 6553771 Original code produced in this study This paper https://github. com/UriNeri/RVMT accompanying interactive web portal This paper https://riboviria. org Software and algorithms MMseqs2 Steinegger and So ¨ding, 2017 https://github. com/soedinglab/MMseqs2 NCBI BLAST+ suite Altschul et al. , 1997; Johnson et al.
First, non-redundant RNA virus sequences were compared to 1,568,535 CRISPR spacers predicted from whole genomes of bacteria and archaea in the IMG database  (Chen et al. , 2021)  using blastn v2. 9. 0 with options ''-dust no -word_size 7''. To minimise the number of false-positive hits due to lowcomplexity and/or repeat sequences, CRISPR spacers were excluded from this analysis if (i) they were encoded in a predicted CRISPR array including 2 spacers or less, (ii) they were % 20bp, or (iii) they included a low-complexity or repeat sequence as detected by dustmasker (v1. 0. 0)  (Morgulis et al. , 2006) (options ''-window 20 -level 10'')  or a direct repeat of R 4bp detected with etandem (v6. 6. 0. 0)  (Rice et al. , 2000)  (options ''-minrepeat 4 -maxrepeat 15 -threshold 2''). To link RNA viruses to CRISPR spacers, only blastn hits with 0 or 1 mismatch over the whole spacer length were considered.
The spacer and array with hits were further inspected to check (i) whether the spacers were of consistent length throughout the array, and (ii) whether Cas and/or RT genes were found in the putative host genome, and if so whether these were adjacent to the CRISPR array with the hit. To expand the search for CRISPR link beyond bacteria and archaea for which a draft genome is available, we next used the same approach to compare non-redundant RNA virus sequences to 53,372,161 CRISPR spacers predicted from metagenome assemblies available in the IMG database. Spurious spacers were filtered out using the same methods as for the genome-derived CRISPR arrays (see above), and only hits for which the RNA virus and the CRISPR spacers originated from the same ecosystem (as defined in the GOLD database) were retained.
The bottom panel displays the relative abundance of both segments across a metatranscriptome time series. (E) Relative abundance of different prokaryotic RNA virus groups across biomes. Only datasets dominated by prokaryotic sequences (''P-dominated'') containing at least 10 prokaryotic RNA viruses were considered. The right panel shows a breakdown of the biome distribution for each group, calculated from a balanced dataset composed of random subsamples of 50 samples per environment (random subsampling was performed 100 times, and the mean values were plotted). See also FiguresS3 and S4.
g. alignments, HMMs, original seed sequences and predicted function) as well the nucleic sequences for both the expanded (2. 6M metatranscriptome derived) contig set and the manually consolidated ''Reference Set'' (see STAR Methods). d As noted above, the Zenodo deposit includes the original code produced in this study, which corresponds to the latest version of the project's GitHub repository, which is available under the open-source MIT License at https://github. com/UriNeri/RVMT. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. e1 Cell 185, 4023-4037. e1-e10, October 13, 2022.
RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression, and dysfunctional RBPs underlie many human diseases. Proteome-wide discovery efforts predict thousands of novel RBPs, many of which lack canonical RNA-binding domains. Here, we present a hybrid ensemble RBP classifier (HydRA) that leverages information from both intermolecular protein interactions and internal protein sequence patterns to predict RNAbinding capacity with unparalleled specificity and sensitivity using support vector machine, convolutional neural networks and transformer-based protein language models. HydRA enables Occlusion Mapping to robustly detect known RNA-binding domains and to predict hundreds of uncharacterized RNA-binding domains. Enhanced CLIP validation for a diverse collection of RBP candidates reveals genome-wide targets and confirms RNA-binding activity for HydRA-predicted domains.
However, to avoid overfitting and maintain robustness of these models, large training datasets are necessary. To alleviate this problem of small positive training sizes, pretraining methods such as self-supervised learning and transfer learning have been used successfully for CV and NLP implementations  14. These methods have also been applied in protein classification problems. A Transformer-based protein language model named ProteinBERT recently achieved state-of-the-art performance on multiple protein classification tasks covering diverse protein properties (including protein structure, post translational modifications and biophysical attributes)  15. Traditional machine learning models, such as SVM and Random Forest, work well on small datasets and address the problem of overfitting. Thus, intuitively, ensemble learning that integrates deep learning with traditional machine learning models offers a promising strategy to design more precise and robust RNA-binding classifiers.