dataesr/scientific-paragraphs-categorization huggingface dataset:
However, as the TPCs do not have measured masses, and their stellar metallicities are not listed in the TPC catalog, we have to overlook these parameters for now. Therefore, we classified the potential tier 1 targets in a three-dimensional parameter space using the classifications shown in Table  1 , with the bounds for the stellar types being set by using the values from  Pecaut & Mamajek (2013). 9 n Figures  6  and 7 , the distributions of the potential Ariel targets are shown. In each figure, the left column shows the variety of planets available today, which includes those confirmed to date using data from TESS. In the right column, the known population has been combined with the TPCs. While we already known of a diverse sample of planets suitable for atmospheric characterization, these plots highlight the impact TESS will have in increasing this variety yet further.
As well as modeling differing planet metallicities, we imposed different C/O ratios for the planets. We based these on the modeling in  Turrini et al. (2018) , assuming a ratio of 0. 5 and 0. 85 for planets b and c, respectively. We then modeled the spectra for these planets assuming equilibrium chemistry using the ACE package  (Agúndez et al. 2012; Venot & Agúndez 2015) , which is a plug-in for the TauREx 3. 1 retrieval code  (Al-Refaie et al. 2021a , 2021b). We assumed isothermal temperature-pressure profiles and introduced a gray cloud deck at 5e2 Pa (0. 005 bar) and 1e3 Pa (0. 01 bar) for TOI-1130 b and c, respectively, to produce spectral features in the Hubble Space Telescope WFC3 band that are equivalent in size to those seen in similar planets studied with this instrument (e. g. ,  Guilluy et al.
Phage classification with ClassiPhage demonstrated agreement with the reference taxonomy from the International Committee on Taxonomy of Viruses (ICTV), although the generation and testing of the taxon-specific profile HMMs was solely focused on phages targeting members of the bacterial family Vibrionaceae  [23]. More recently, a set of 31,150 profile HMMs representing proteins predicted in viral genomes from NCBI were used as the basis for a set of Random Forest classifiers aimed at assigning viral taxonomy at the order, family, and genus ranks  [24]. These classifiers demonstrated very high classification accuracy (� 89%) at all the aforementioned ranks, suggesting that these profile HMMs (or ViPhOGs as they were named) could be the foundation for a new method that taxonomically classifies viral genomic sequences present in metagenomic datasets.
Existing pipelines such as VirMiner, iVirus and KBase enable taxonomic and functional characterisation of viral contigs in metagenomic datasets  [25] [26] [27]. More recently, Hecatomb and MetaPhage were released to provide the scientific community with scalable and portable resources for the taxonomic profiling of viral communities, using metagenomic reads as input  [28, 29]. Here we present VIRify, a new pipeline that predicts virus-like sequences using canonical viral signals and annotates contigs using a comprehensive collection of profile HMMs, including our set of manually curated ViPhOGs. VIRify reduces false-positive predictions and prioritises portability via its implementation as a containerised Nextflow workflow  [30] , which can be directly integrated into already existing platforms such as MGnify  [31].
The VIRify pipeline is freely available at https://github. com/EBI-Metagenomics/emg-viral-pipeline. We showcase the application of VIRify based on two viral mock communities mostly comprised of phages  [32]  but also eukaryotic viruses  [33] , as well as 243 metagenome assemblies of the TARA Oceans project  [12] , and a previously generated collection of 57,866 viral genomic clusters (VCs) from globally-distributed human gut metagenomic datasets  [34].
Manual curation of virus-specific protein HMMs and definition of modelspecific bit score thresholds We manually curated a set of taxon-specific profile HMMs by searching the original ViPhOG database  [24]  against all entries in UniProtKB (February 2019 version). This was achieved using hmmsearch (v3. 2. 1) and setting a per-sequence reporting e-value threshold of 1. 0x10 -3. The resulting output was analysed using in-house python scripts (https://github. com/ EBI-Metagenomics/emg-viral-pipeline/tree/master/bin/models_vs_uniprot_check) to identify which ViPhOGs could be used as taxon-specific markers, hereafter referred to as informative ViPhOGs. These were identified after applying the following steps to the hmmsearch output of each ViPhOG: 1. Reported taxonomy identifiers (taxid) were recorded, along with the highest bit score obtained for each one of them and their associated taxa at the genus, subfamily, family and order ranks.
Coverage of current viral taxonomy and calculation of taxon-specific ViPhOG-to-CDS ratios The set of informative ViPhOGs was screened to keep the ones that were associated with valid taxa in the latest release of ICTV's viral taxonomy MSL#38. These were subsequently employed to determine the extent to which the lineages comprising the current viral taxonomy are covered by our set of informative ViPhOGs. A cladogram that included all viral genera and corresponding ancestral taxa in NCBI's Taxonomy database from January 2023 was built using the ete3 python package  [36, 37]. The number of informative ViPhOGs identified for each viral genus was mapped on the cladogram using the interactive Tree of Life (iTOL) resource and the associated annotation file for plotting numeric data as bars  [38].
Viral taxa linked to the informative ViPhOGs were categorized as prokaryotic or eukaryotic based on their target host. Currently known viral-host relationships were retrieved from Virus-Host DB (release 214 from November 2022), which contained 12,032 viral entries targeting eukaryotes and 5,491 viral entries targeting either bacteria or archaea  [39]. For each taxon associated with any of the informative ViPhOGs or that is part of the viral lineages covered by them, we determined the ratio between the number of associated informative ViPhOGs and the average number of CDS. To calculate this ratio, the number of informative ViPhOGs for taxa in the subfamily, family and order ranks included the models linked to the corresponding descendant taxa, in addition to those directly linked to them. To determine the average number of CDS per taxon, we identified all viral assemblies from NCBI's Assembly database that had been deposited until November 2022 and whose CDS had been annotated.
Preparation of mock metagenome assemblies for benchmarking Paired-end reads of six virus-enriched samples were downloaded from ENA (study: PRJEB6941; runs: ERR575691, ERR575692, ERR576942, ERR576943, ERR576944, ERR576945) and concatenated to make a single input file  [32]. Before assembly, host contamination was removed by k-mer-based decontamination against the mouse genome (Ensembl GRCm38, primary assembly) with BBDuK v38. 79 from the BBTools suite (https://sourceforge. net/projects/bbmap/) using a k-mer size of 27. To include a metagenome assembly that also comprised eukaryotic viruses, we further obtained paired-end reads of seven virus-enriched samples (study: PRJNA319556; runs: SRR3458563-3458569)  [33]. For the combined data sets within both studies, two individual metagenome assemblies were performed by first cleaning the reads with fastp v0.
Thus, prophage prediction was conducted for the following bacterial genome sequences: Pseudomonas savastanoi pv. phaseolicola strain HB10Y (GCF_001294035), Salmonella enterica subsp. enterica serovar Typhimurium str. LT2 (NC_003197), Listeria monocytogenes EGD-e (NC_003210), Bacteroides thetaiotaomicron VPI-5482 (NC_004663), Enterococcus faecalis V583 (NC_004668), Escherichia coli B strain C3029 (NZ_CP014269), Lactobacillus acidophilus strain ATCC 4356 (GCF_000786395), Bifidobacterium animalis subsp. lactis ATCC 27674 (GCF_001263985), B. thetaiotaomicron strain ATCC 29741 (GCF_004349615) and E. coli DSM 30083 (NZ_CP033092). Prophages were predicted from the listed bacterial genomes using VirSorter  [51] , selecting the Viromes database and activating the virome decontamination mode, and PHASTER  [52].
VIRify taxonomic annotation pipeline description Identification of putative viral sequences. Based on the results obtained with WtP and the procedure previously employed in a global oceanic survey of viral populations, it was determined that VIRify's detection of putative viral contigs would be carried out with VirFinder, VirSorter and PPR-Meta, while putative prophage detection would be conducted with VirSorter only (Fig  1 )  [12, 51, 55, 56]. Viral prediction with VirSorter is carried out using parameters '-db 2' and '-virome' when the processed metagenomic assemblies are generated from virome datasets (as was the case for VIRify's benchmarking with the Kleiner and Neto assemblies), otherwise the latter parameter is not set. Putative prophages are retrieved from VirSorter predictions in prophage categories 4 and 5 as defined by the tool.
Detection of putative viral contigs with VirFinder is conducted using the classifiers in the model file VF. mod-EPV_k8. rda, which was trained using sequences from prokaryotic and eukaryotic viruses (available at https://github. com/jessieren/VirFinder). Predicted viral contigs are classified into two different categories within the VIRify pipeline as follows: contigs reported by VirSorter in categories 1 and 2 are placed in the high-confidence category, whilst the low-confidence category includes contigs that satisfy any of the following conditions: • Reported by VirFinder with p<0. 05 and score�0. 9. • Reported by VirFinder with p<0. 05 and score�0. 7, and also reported by VirSorter in category 3. • Reported by VirFinder with p<0. 05 and score�0. 7, and also reported as phage by PPR-Meta.
Finally, CheckV  [57]  is employed to assess the quality and completeness of the categorised viral genome contigs. Annotation of putative viral sequences with informative ViPhOGs. Protein-coding sequences within putative viral contigs and prophages are detected with Prodigal v2. 6. 3  [58] , using the standard bacterial/archaeal translation table (-g 11) and the metagenomic prediction mode (-p meta) (Fig  1 ). A recent study in which the performance of a range of ORF-prediction tools was assessed for the viral genomes available in NCBI's RefSeq reported that Prodigal was the most accurate tool for DNA viruses, which account for ~97% of the viral genomic sequences currently available in public databases  [59, 60]. Amino acid sequences derived from the predicted coding sequences are scanned with the complete ViPhOG database using hmmscan v3.
2. 1  [61]  and the model-specific bit score thresholds defined as previously described, to supersede any thresholding based on statistical significance alone. To apply the defined model-specific thresholds, we set the-cut_ga option during execution of the hmmscan command. However, to account for the set of ViPhOGs for which no bit score thresholds were defined (and therefore set to 0), an additional post-processing filter is applied to the full-sequence e-values reported in the hmmscan output (E�1. 0×10 -3 ). Taxonomic assignment of putative viral sequences. In-house python scripts (see https:// github. com/EBI-Metagenomics/emg-viral-pipeline) are employed for parsing the hmmscan results and to provide a taxonomic assignment for all putative viral contigs and prophages, based on the reported hits against the ViPhOG database.
Taxonomic classification of 57,866 VCs from the Gut Phage Database (GPD) The GPD is a collection of phage genomic sequences that includes prophages from cultured gut bacteria and putative phage sequences retrieved from assemblies of globally-distributed human gut metagenomes  [34]. A representative sequence for each of the 57,866 VCs in the GPD was selected by choosing the corresponding entry with the highest CheckV quality and completion values. The complete set of representative sequences was analysed with VIRify, using the "-onlyannotate" option to skip the viral prediction step and apply the taxonomic classification pipeline to all the input sequences. The results obtained for all the classified representative sequences were collated and summarised in a Sankey plot.
Selection and comparison of virus-specific protein profile HMM databases The taxonomic assignment of VIRify relies purely on our own protein profile HMM database, their taxonomy, and model-specific bit score cutoffs. In an effort to be comprehensive, our pipeline also provides the option of additional annotations by incorporating 25,399 models from the VOGdb (http://vogdb. org/), 25,281 viral protein family (VPF) models from IMG/VR  [60] , 9,911 models from RVDB v17. 0  [65, 66] , and 9,518 models from pVOG  [67]. VIRify can use all databases to individually annotate proteins from all putative viral contigs, using hmmscan with default values and an e-value cutoff of 0. 001. However, the final taxonomic assignment provided for the putative viral contigs relies exclusively on our curated set of ViPhOGs.
To compare the annotations provided by all five databases, we ran hmmscan on all predicted proteins from putative viral contigs detected in the two mock community assemblies (Neto and Kleiner) and the 243 TARA Oceans assemblies. To allow for a better comparison against the other databases, we distinguished hits against the ViPhOG models into hits only based on an e-value cutoff of 0. 001 and hits additionally identified using our predefined model-specific bit score thresholds.
General implementation of VIRify VIRify is implemented using the workflow management system Nextflow  [30] , and an overview of the pipeline is given in Fig 1. All third-party tools are encapsulated in software containers to allow easy distribution of the pipeline on local, cluster, or cloud systems. Custom Python scripts connect the output and input of the tools used and are available via GitHub (https:// github. com/EBI-Metagenomics/emg-viral-pipeline/tree/master/bin). With configured Nextflow and Docker  [68]  installations, VIRify can be simply downloaded and run with a single command: nextflow run EBI-Metagenomics/emg-viral-pipeline -r v1. 0-help. All required databases and metadata files are automatically downloaded and stored for later (offline) reuse. We always recommend running a stable release version from the repository, which can be selected via the -r flag.
Viral diversity is comprehensively covered by the set of manually curated informative ViPhOGs The original ViPhOG database  [24]  consists of 31,150 profile HMMs that were created using proteins from viral genomes found in NCBI's databases. Based on the search of homologous sequences in the UniProtKB database and the subsequent analysis conducted on the output data, we identified 20,266 informative ViPhOGs that could serve as taxonomic markers for viral taxa present in the current version of ICTV's taxonomy (MSL#38). Fig 2  indicates  the number of informative ViPhOGs obtained for each of the analysed taxonomic ranks (genus, subfamily, family and order), and for each rank, it illustrates the number of targeted taxa that correspond to either prokaryotic or eukaryotic viruses. Overall, 83. 1% of the informative ViPhOGs were linked to taxa at the genus level and the numbers identified for the remaining ranks decreased from the subfamily to the order level.
VIRify accurately classifies viruses on co-assemblies of two largely viral mock communities VIRify was tested on datasets generated from viral mock communities to evaluate its viral sequence prediction performance and compare its taxonomic assignments with the taxonomy of the viruses present in the communities. The selected datasets were generated for two different studies that evaluated a range of viral enrichment and purification procedures that were applied to mock microbial communities. Kleiner et al. [32]  sequenced a mock microbiome prepared in mouse faeces using bacterial and phage cultures (germ-free C57BL/6 J mice). The samples contained six different phages in varying concentrations: P22, T3, T7, ɸ6, M13 and ɸVPE25. Neto et al. [33]  constructed a mock microbiome comprised of four common gut bacteria and nine highly diverse viruses, most of which represent the eukaryotic viral families Circoviridae, Parvoviridae, Polyomaviridae, Alphaflexiviridae, Reoviridae, Coronaviridae, Herpesviridae and Mimiviridae, and including a phage from the family Ackermannviridae (formerly classified in the family Myoviridae).
The nine viruses differ highly in genome length (1. 8 kb to 1,180 kb), genome type (dsDNA, dsRNA, ssDNA, ssRNA), and genome compositions (linear, circular, segmented). Co-assemblies of datasets obtained from viral-enriched samples were generated for each of the selected studies and used as input for the VIRify pipeline. The Kleiner co-assembly comprises 5,310 contigs, of which 224 are � 1. 5 kb with an N50 of 74,309 bp. The Neto co-assembly comprises 341,587 contigs, of which 1,686 are � 1. 5 kb with an N50 of 16,751 bp. Thus, 95. 78% of the contigs in the Kleiner assembly, and 99. 51% of the contigs in the Neto assembly are shorter than 1500 bp. VIRify comprehensively selects putative viral contigs. We ran a modified version of WtP (see Methods) on both mock community assemblies (Kleiner, Neto) to compare ten different tools for virus prediction.
The taxonomic classifications provided by VIRify were compared with the taxonomy of the genomes from which the analysed contigs were derived. We determined as a result that VIRify's taxonomic classification had an accuracy of 94. 2% for the putative viral contigs detected in this assembly. Contigs classified as members of subfamilies Sepvirinae and Tunavirinae matched entries in NCBI's nr database corresponding to phages of E. coli, suggesting that these were most likely derived from DNA contamination by the bacterial component of the mock community. No contigs from the viruses that represented the families Circoviridae, Polyomaviridae, and Alphaflexiviridae were detected among the length-filtered contigs by blastn, and therefore no contigs were assigned to any of the corresponding taxonomic lineages.
Eight contigs ranging from 1,543 to 2,356 bp and coming from viruses belonging to the families Parvoviridae, Sedoreoviridae and Herpesviridae were detected among the set of low-confidence viral contigs. One of these was correctly classified at the subfamily rank (Alphaherpesvirinae) and another was correctly classified at the genus rank (Rotavirus). No contig was classified within the viral lineage that includes the family Parvoviridae. One 7,051 bp putative viral contig was classified in the subfamily Orthoretrovirinae, which does not match the lineage of any of the viruses included in the mock community. However, when this contig was used as query for a search of NCBI's nr nucleotide database with megablast, the top hits corresponded to porcine endogenous retroviral sequences, including several complete genomes of this type of virus (e.
ViPhOG models comprehensively cover viral proteins Our comparison of virus-specific protein profile HMM databases included in VIRify showed that the ViPhOG models covered a large proportion of potentially viral CDS in agreement with the other databases (Fig  7A ). In total, we predicted 1,204,455 CDSs from all high-confidence viral contigs that VIRify reported for the 243 TARA assemblies, of which the ViPhOGs covered 56. 7% (49. 4% with applied bit score threshold) and thus were only slightly outperformed by the VOGdb (56. 8%). The largest set of shared annotations comprised 284,791 CDS annotated as viral based on models from all compared databases except the RVDB (Fig  7A ). In addition, the RVDB had the least predictions, with only 15. 3% annotated CDS compared to all other databases. Our model-specific bit score filtering reduces the amount of CDS annotations derived from ViPhOGs by 7.
3% (87,869 CDS) (Fig  7A ). While this might lead to the loss of potentially informative annotations that could be used for taxonomy assignment, we also remove false positive model hits, as shown in Fig 7B. Interestingly, the VPF derived from the IMG/VR database, which also includes novel viral sequences derived from metagenome approaches, comprises a large proportion of unique models that match 117,962 CDS (9. 8%) that are not covered by any of the other annotated databases. On the other hand, a significant number of CDS are annotated by models from the other databases but missed by the VPF models.
Discussion Metagenomic surveys of different environments during the last few decades have had a profound impact on the rate at which novel viruses are discovered  [75]. Despite the resulting sharp increase in the number of publicly available viral genomes, viruses remain relatively understudied in many environments and the taxonomic profiling of viral communities is still challenging due to the lack of universal genetic markers that support the phylogenetic resolution of viral taxa  [76, 77]. As a contribution to the currently available repertoire of tools designed to address these challenges, we designed and implemented the VIRify pipeline for detecting and taxonomically annotating viral contigs in metagenomic datasets. The results presented here for the mock community assemblies demonstrated that VIRify is a suitable pipeline for generating highly accurate taxonomic profiling of viral communities present in metagenomic datasets.
This strategy has been successfully implemented previously in Pfam and TIGRFAMs, some of the most widely used protein family databases for the functional annotation of genomes and metagenomes  [35, 81]. Another novelty of our curated set of informative ViPhOGs is the use of TSR to set taxonomic assignment thresholds specific for each taxon. The TSR measures the density of assigned informative ViPhOGs for each taxon present in the viral lineages covered by our set of models. Thus, setting the taxonomic assignment threshold with this parameter allows the algorithm to adjust its level of stringency for each individual taxon. This feature is particularly advantageous for taxa characterised by relatively small genomes, for which there is a fewer number of informative ViPhOGs in our collection. This benefit was especially evident during the analysis of the Neto assembly, where the use of this feature increased the number of classified contigs from several of the eukaryotic viruses present in the mock community.
Furthermore, short contigs will generally contain fewer complete CDS, and thus will be less likely to have the minimum number of ViPhOG hits required by the pipeline to provide a taxonomic assignment. VIRify is a novel HMM-based resource for viral taxonomic classification that can be broadly classified as a protein similarity-based method. There are other types of approaches that have been efficiently implemented for viral taxonomic profiling, such as the use of kmer matching to a reference database of complete phage genomes. Phanta, a recently developed tool for phage-inclusive gut microbiome profiling, takes advantage of a much larger number of phages discovered through extensive analyses of the human gut microbiome, in comparison with other biomes  [84]. This tool uses a large database of gut phages and their associated taxonomic information to perform taxonomic profiling using kmer-based searches, which allows the analysis of phage communities from metagenomic reads.