DOI: 10.5061/dryad.s7h44j1c9
title: Single-cell RNA-seq of the rare virosphere reveals the native hosts of giant viruses in the marine environment

description: Giant viruses (phylum Nucleocytoviricota) are globally distributed in
 aquatic ecosystems. They play significant roles as evolutionary drivers of
 eukaryotic plankton and regulators of global biogeochemical cycles.
 However, we lack knowledge about their native hosts, hindering our
 understanding of their lifecycle and ecological importance. Here, we used
 single-cell RNAseq and samples from an induced E. huxleyi bloom during a
 mesocosm experiment to link giant viruses with their protist hosts. We
 observe active giant virus infections in multiple host lineages, including
 members of the algal groups Chrysophycae and Prymnesiophycae, as well as
 heterotrophic flagellates in the class Katablepharidaceae. Katablepharids
 were infected with a rare Imitevirales-07 giant virus lineage expressing
 cell fate regulation genes. Analysis of the temporal dynamics of this
 host-virus interaction indicated a role for the Imitevirales-07 in the
 collapse of the host Katablepharid population. Our results demonstrate
 that single-cell RNA-seq can be used to identify previously undescribed
 host-virus interactions and study their ecological relevance.
description: <strong>Mesocosm core setup and sampling procedure
 </strong> Samples were obtained during the
 AQUACOSM VIMS-Ehux mesocosm experiment in Raunefjorden near Bergen, Norway
 (60°16′11N; 5°13′07E), in May 2018. Seven bags were filled with
 11m<sup>3</sup> water from the fjord, containing natural
 plankton communities. Algal blooms were induced by nutrient addition and
 monitored for 24 days, as previously described<sup>23</sup>.
 10 samples were collected from four bags, as follows: From bag 3, on days
 15 and 20 (named B3T15, B3T20 correspondingly). From bag 4, on days 13,
 15,19, and 20 (named B4T13, B4T15, B4T19, and B4T20, correspondingly).
 From bag 6, on day 17 (named B6T17). From bag 7, on days 16, 17, and 18
 (named B7T16, B7T17, and B7T18, correspondingly).
 Samples were initially filtered as follows: 2 liters of water
 were filtered with a 20 µm mesh and collected in a glass bottle. The cells
 were then concentrated through gentle gravity filtration on a 3 µm
 polycarbonate filter (Whatman), mounted on a reusable bottle top filter
 holder (Thermo Fischer). The biomass on the filter was regularly
 resuspended by gentle pipetting. For samples B7T16,
 B7T18, B4T15, B3T15, B6T17, B7T17, and B4T19, the 2 liters of seawater
 were concentrated down to 100 ml, distributed in two 50 ml tubes, which
 corresponds to a 200 times concentration. For B4T13, the concentration
 factor was 140 times. For B4T20 and B3T20, the concentration factor was
 100 times. The different concentration factors are explained by filter
 clogging and various field constraints, including processing time. For all
 samples except B3T20, the 50 ml tubes were centrifuged for 4 min at 2500g,
 after which the supernatant was discarded. Pellets corresponding to the
 same day and same bag were pooled and resuspended in a final volume of 200
 µl of chilled PBS. 1800 µl of pre-chilled high-performance liquid
 chromatography (HPLC) grade 100% methanol was added drop by drop to the
 concentrated biomass. For B3T20, the concentrated biomass was centrifuged
 for 4 min at 2500g, resuspended in 100 µl of chilled PBS, to which 900 µl
 of chilled HPLC grade 100% methanol was added. Then, samples were
 incubated for 15 minutes on ice and stored at -80°C until further
 analysis. <strong>Library preparation and RNA-seq
 sequencing using 10X Genomics</strong> For
 analysis by 10X Genomics, tubes were defrosted and gently mixed, and 1.7
 ml of the samples were transferred into an Eppendorf Lowbind tube and
 centrifuged at 4°C for 3 min at 3000g. The PBS/methanol mix was discarded
 and replaced by 400 µl of PBS. Cell concentration was measured using an
 iCyt Eclipse flow cytometer (SONY) based on forward scatter. Cell
 concentration ranged from 1044 cells ml<sup>-1</sup> to 9855
 cells ml<sup>-1</sup>. All concentrations were brought to 1000
 cells ml<sup>-1</sup> to target 7000 cells recovery, according
 to the 10X Genomics Cell Suspension Volume Calculator Table provided in
 the user guide. The cellular suspension was loaded onto Next GEM Chip G
 targeting 7000 cells and then ran on a Chromium Controller instrument to
 generate GEM emulsion (10x Genomics). Single-cell 3' RNA-seq
 libraries were generated according to the manufacturer's protocol
 (10x Genomics Chromium Single Cell 3' Reagent Kit User Guide v3/v3.1
 Chemistry) on different occasions: B4T19 and B7T17 in January 2020 and
 B3T15, B3T20, B4T13, B4T15, B4T20, B6T17, B7T16, and B7T18 in August 2020
 with 12 cycles for cDNA amplification and 15 cycles for library
 amplification. Library concentrations and quality were measured using the
 Qubit dsDNA High Sensitivity Assay kit (Life Technologies, Carlsbad, CA).
 Libraries were pooled according to targeted cell number, aiming for a
 minimum of 20,000 reads per cell. Pooled libraries were sequenced using
 the NextSeq<sup>® </sup>500 High Output kit (75
 cycles). <strong>Bioinformatic
 pipeline</strong> A step-by-step description of
 the bioinformatic pipeline from this step onward, including all in-house
 scripts used, is detailed in the GitHub repository under
 github.com/vardilab/host-virus-pairing.
 <strong>Detection of infected cells in the single-cell
 RNA-seq data using a custom viral genes database</strong>
 To detect viral transcripts, a reference was built from a
 database of highly conserved genes<sup>6</sup> from all NCLDV
 in the Giant Virus Database<sup>9</sup>, such as family B DNA
 polymerase, RNA polymerase subunits, and the major capsid protein. The
 genes were clustered using CD-HIT v. 4.6.6  at 90% nucleotide identity To
 remove redundancy<sup>43</sup>. From this database of 34866
 genes, a reference was created using the 10X Genomics Cell Ranger mkref
 command. The Cell Ranger Software Suite (v. 5.0.0) was used to perform
 barcode processing (demultiplexing) and single-cell unique molecular
 identifier (UMI) counting on the raw reads from 47391 cells using the
 count script (default parameters), with the deduplicated NCLDV database as
 a reference. For downstream analysis, 972 cells that highly expressed
 multiple NCLDV genes and were considered "highly infected" were
 selected.  These 'highly infected' cells were selected based on
 the following criteria: (a) cell expresses in total ≥10 viral
 UMIs<sup>22,24</sup>, (b) expression of more than one
 viral gene (&gt;1), (c) expression of at least one gene with a UMI
 count greater than one (&gt;1). Cell selection was wrapped
 using an in-house script (choose_cells.py).
 <strong>Identifying the taxonomy of individual cells by
 sequence homology to ribosomal RNA</strong> Raw
 reads from each cell were pulled by the cell's unique barcode
 identifier using seqtk v. 1.2. Reads were then trimmed (command:
 trim_galore --phred33 -j 8 --length 36 -q 5 --stringency 1 --fastqc -e
 0.1), and poly-A was removed (command: trim_galore --polyA -j 1 --length
 36), using TrimGalore (v. 0.6.5), a Cutadapt wrapper
 <sup>44</sup>. Trimmed reads from each cell were assembled
 using rnaSPAdes 3.15<sup>45</sup> with kmer 21,33. Raw reads
 pulling, trimming, and assembly was wrapped using an in-house script
 (assemble_cells.sh).  To identify the taxonomy of the cells, assembled
 contigs from each cell were matched against 18S rRNA sequences from the
 Protist Ribosomal Reference (PR2)<sup>46</sup> and
 metaPR2<sup>47</sup>. To remove redundancy, the sequences in
 each database were clustered using CD-HIT v. 4.6.6 at 99%
 identity<sup>43</sup>. Contigs were filtered using SortMeRNA
 v. 4.3.6<sup>48</sup> with default parameters against the PR2
 database and then aligned to the PR2 and metaPR2 databases using
 Blastn<sup>49</sup>, at 99% identity, E-value ≤
 10<sup>-10 </sup>and alignment length of at least 100 bp.
 Contigs were ranked by their bitscore, and only the best hit was kept for
 each contig. Each contig was assigned to one of the following taxonomic
 groups that were prevalent in the sample: the classes Bacillariophyta,
 Prymnesiophyceae, Chrysophyceae, MAST-3, and Katablepharidaceae, the
 divisions Pseudofungi, Lobosa (Amoebozoa), Ciliphora (Ciliates),
 Dinoflagellata and Cercozoa. Contigs that matched other groups were
 assigned as "other eukaryotes". Contigs that matched more than
 one of these taxonomic groups were considered non-specific or chimeric and
 were therefore ignored. This downstream analysis of Blast result was
 wrapped using an in-house script (Sankey_wrapper_extended.ipynb). To avoid
 detection of doublets and predators, Cells that transcribe 18S rRNA
 transcripts homologous to more than one taxonomic group were
 conservatively omitted. Of the 972 infected cells detected, 418 (43%) were
 omitted because we could not assemble specific 18s rRNA contigs from them
 or because their identity was ambiguous.  None of the cells that were
 assigned "other eukaryotes" had contigs with conflicting
 annotations (contigs matching different classes).
 <strong>Identifying the infecting virus using a homology
 search against a custom protein database</strong>
 To identify transcripts derived from giant viruses, reads from
 the detected 972 infected cells were compared to a custom protein database
 using a translated alignment approach. To ensure that as many giant
 viruses as possible were represented, a database was constructed by
 combining RefSeq v. 207<sup>50</sup> with all predicted
 proteins in the Giant Virus Database<sup>9</sup>. The proteins
 were then masked with tantan<sup>51</sup> (using the -p
 option) and generated the database with the lastdb command (using
 parameters -c, -p). To identify the infecting virus, the raw sequencing
 reads in each of the 972 single-cell transcriptomes were compared to the
 constructed database using LASTAL v. 959<sup>52
 </sup>  (parameters -m 100, -F 15, -u 2) with best matches retained.
 The same procedure was done for the assembled transcripts from each cell
 to identify viral transcripts. The results were analyzed at different
 taxonomic levels, consistent with the Giant Virus Database (for giant
 viruses) or NCBI taxonomy<sup>33</sup>(everything
 else). 754 Cells whose best matching virus was
 coccolithovirus were omitted from the downstream analysis since
 EhV-infected cells were already reported to be abundant in the algal
 bloom<sup>25</sup>, and our analysis aims to explore other
 host-virus pairs. Plotting host-virus pairs in a Sankey
 plot for host cells and their infecting giant viruses
 Of the 218 cells detected as infected by viruses other than EhV,
 71 were selected that could be identified using assembled 18S rRNA
 transcripts and have at least 10 reads aligned to one of the virus
 families (Supplementary Data 1). Only links representing at least 10% of
 the aligned reads in each cell are shown in order to highlight the strong
 links. The Sankey plot was constructed using Holoviews v. 1.15.4; see
 sankey_wrapper.ipynb in the GitHub repository.
 <strong>Phylogenetic trees of viral and host marker
 genes</strong> For phylogenetic analysis, 31
 cells were chosen based on a strong correlation (≥90% of viral reads
 matched one virus family) between the host and a virus.
 To obtain reference 18S rRNA sequences to include in a phylogeny,
 all transcripts assembled from these cells were compared to the PR2
 database<sup>46</sup>  using BLASTN v. 2.9.0+ (parameters
 -perc_identity 95, -evalue 10<sup>-10</sup>, -max_target_seqs
 20, -max_hsps 1). Sequences shorter than 1000 bp were removed from the
 reference, and the remainder of the sequences were de-replicated with
 cd-hit v. 4.7<sup>43</sup> (-c 0.99) to prevent the inclusion
 of excessive nearly identical references. Sequences were aligned with
 Muscle5<sup>53 </sup>(default parameters), and diagnostic
 trees were created with FastTree 2.1.10<sup>54 </sup>for quick
 visualization of trees and for pruning long branches. The final
 phylogenetic trees were constructed with IQ-TREE v.
 2.1.2<sup>52</sup> (parameters -m GTR+F+G4 -alrt 1000 -T AUTO
 --runs 10).  To identify major capsid protein sequences in the single-cell
 transcriptomes, proteins were first predicted using FragGeneScanRs v.
 1.1.0<sup>56</sup> (parameters -t, illumina_10). The resulting
 protein sequences were compared to MCP proteins in the Giant Virus
 Database with BLASTP v. 2.12.0+ (parameters -evalue
 10<sup>-3</sup>, -max_target_seqs 20, -max_hsps 1) as well as
 to a custom MCP HMM that were previously designed6 using hmmsearch in the
 HMMER3 v. 3.3.2 package<sup>57</sup> (E-value ≤
 10<sup>-3</sup>). The results of these searches were manually
 inspected, and sequences were subsequently aligned with Muscle 5 (default
 parameters). Similarly, as with the 18S rRNA sequences, diagnostic trees
 were first made with FastTree 2.1.10 and pruned long branches before
 making a final tree with IQ-TREE v. 2.1.2 (parameters m LG+F+G4 -alrt 1000
 -T AUTO --runs 10). Cells for which transcripts are present in both viral
 and host trees were denoted (Supplementary Data 4). All the codes used to
 produce the trees are wrapped in the folder "marker_gene_trees"
 in the GitHub repository. <strong>Single-cell
 RNA-seq data alignment to a custom reference</strong>
  A new host-virus reference database was curated from the
 transcriptome of the infected cells (Fig. 2). Repetitive sequences were
 removed using BBduk (BBtools 38.90)<sup>58</sup>. An
 Additional long repetitive sequence was removed manually. A database of E.
 huxleyi and EhV genes, which were shown to be abundant in the
 samples<sup>25,</sup> was also added to this reference to
 specifically detect E. huxleyi cells and to avoid a non-specific alignment
 of reads from these cells to other contigs. For EhV, the predicted CDSs in
 the EhVM1 were used as a reference<sup>59</sup>. For the host,
 an integrated transcriptome reference of E. huxleyi was used as a
 reference<sup>60</sup>. Viral transcripts in the database were
 identified using a homology search against a custom protein database as
 described above. A reference was created from the database using the Cell
 Ranger mkref command. Raw reads were aligned to this reference database
 using 10X Genomics Cell Ranger v. 5.0.0 count analysis.
 <strong>Preprocessing of transcript abundance and
 dimensionality reduction</strong> A total of
 28,656 cells from the 10 samples were initially aligned to the reference
 database. Cells with zero UMIs and cells with the lowest 1% number of
 UMIs, as compared with the distribution of transcripts per cell in the
 entire dataset, were removed for downstream analyses. To prevent cases of
 doublet or multiplet cells, which can be biological (cell digestion) or
 technical (fused cells), cells with the highest 1% number of UMIs were
 also removed. The raw UMIs of 28,015 cells were further preprocessed using
 the Python package scprep v. 1.0.10: Low expressing genes were filtered
 with filter.filter_rare_genes and min_cells=2. This number was chosen
 because we did not want to include genes mapped to only one cell, but we
 also did not want to exclude low-expressed genes, as they might represent
 gene expression of low-abundant organisms. Expression was normalized by
 cell library size with normalize.library_size_normalize, and the data was
 scaled with transform.sqrt. Preprocessing was wrapped in an in-house
 script; see 00.01.filter_normalize_scale_single_cell_data.py in the GitHub
 repository. To represent the cells in two dimensions
 based on their gene expression profiles, dimensionality reduction was
 performed using scprep v. 1.1.0 package PCA (method='svd',
 eps=0.1) and Uniform Manifold Approximation and Projection (UMAP)
 dimensionality reduction was conducted using the UMAP method in the
 manifold package of the Python library scikit-learn v. 0.24.1 (minimum
 distance=0.4 spread=2, number of neighbors=7). Dimensionality reduction
 was wrapped in an in-house script
 (00.02.dimentionality_reduction_single_cell_data.py).
 <strong>Assigning taxonomy to each detected cell using rRNA
 homology search</strong> To identify the taxonomy
 of each detected cell, reads from each cell were assembled independently.
 The taxonomy of the cells was determined by 18S rRNA homology to one of
 the following groups, which were abundant in the population: the classes
 Bacillariophyta (diatoms), Prymnesiophyceae, Chrysophyceae, MAST-3 and
 Katablepharidaceae, the divisions, Ciliphora (Ciliates), Dinoflagellata
 and Cercozoa. Other taxonomic groups were clustered under "Other
 eukaryotes".  16,358 cells were identified this way, and 11,657 cells
 that could not be identified were excluded from the plot for convenience.
 Cells with 18S rRNA contigs homologous to more than one taxonomic group
 were also conservatively omitted. As described above, cells expressing at
 least 10 viral UMIs were considered infected<sup>1,2.</sup>
 This section was wrapped in a Jupyter notebook
 (Coexpression_wrapper_extended.ipynb).
 <strong>Identifying the Leucocryptos host and its virus
 using homology search</strong> To better identify
 the detected Katablepharidaceae cells and to identify their infecting
 virus, 26 infected Katablepharidaceae cells from bag #4, day 20, were
 selected. Reads from these cells were retrieved using the unique molecular
 identifier and then trimmed using TrimGalore v.  0.6.5, a Cutadapt
 wrapper<sup>44</sup>. Trimming was wrapped in an in-house
 script; see pull_trim_clean.sh in the GitHub repository. Trimmed read
 files from all these cells were concatenated into one file and assembled
 altogether using rnaSPAdes v. 3.15<sup>45</sup>. To identify
 the specific Katablepharidaceae host, assembled contigs were matched
 against the PR2 rRNA database using blastn at 90% identity, E-value ≤
 10<sup>-10</sup>, and alignment length ≥ 100bp. Contigs best
 matched to an unknown Katablepharidaceae (&gt;99% nucleotide
 identity), but after removing unidentified genera, these contigs best
 matched (&gt;95% nucleotide identity) the Katablepharidaceae species
 Leucocryptos marina. Transcripts that matched classes other than
 Katablepharidaceae were matched against the entire NCBI database using the
 NCBI web server<sup>61</sup>. They, too, mostly matched
 Katablepharidaceae genes, specifically 28S rRNA or internal transcribed
 spacer (ITS) sequences (Supplementary Data 3). To identify the specific
 infecting virus, transcripts were matched against an NCLDV gene marker
 database<sup>6</sup> at 90% identity, E-value ≤
 10<sup>-10</sup>, and alignment length ≥ 100bp. After finding
 homology to Leucocryptos and the virus
 GVMAG-M-3300020187-27<sup>1</sup>, gene expression was
 calculated using RSEM v.1.3.1<sup>62</sup>
 (rsem-calculate-expression -p 10 --bowtie2
 --fragment-length-mean<sup> 58</sup>). The genomic features of
 the virus were taken from Schulz (2020)1, and the viral genome was plotted
 using ShinyCircos v. 2.0<sup>63</sup>. Gene expression in the
 plot is measured in expected counts after log 2 transformation. The
 relative abundance data in Fig. 4 was obtained from an 18S rRNA amplicon
 sequencing on a size fraction of 2-20µm in bag #4 during the mesocosm
 experiment<sup>23</sup>. Days 19, 22, and 23 were sampled
 twice; all other days were sampled once. In Fig. 4c, relative abundance is
 calculated per taxa as a fraction of all amplicon sequencing variants
 (ASV), excluding metazoans. Fig. 4d shows the fraction of
 Katablepharidaceae out of all ASVs matching Katablepharidaceae (excluding
 metazoans). E. huxleyi abundance was measured by flow cytometry based on
 high side scatter and high chlorophyll signals. These data were obtained
 from the source data of the same study<sup>23</sup>.
 <strong>Phylogenetic tree of Katablepharidaceae ASVs and
 18S rRNA genes</strong> To verify the taxonomy of
 the ASVs, A phylogenetic tree was constructed of 89 ASVs identified as
 Katablepharidaceae, selected 18S rRNA sequences of Katablepharidaceae and
 other species from the PR2 database, and the longest single cell assembled
 contig from the infected Katablepharidaceae cells. Sequences were aligned
 with ClustalOmega v. 1.2.4 (default parameters)<sup>64</sup>.
 A diagnostic tree was first made with FastTree
 2.1.10<sup>54</sup> for pruning long branches before making
 the final tree with IQ-TREE<sup>55</sup>. All but three ASVs
 and one PR2 sequence clustered together with the assembled Leucocryptos
 transcript, verifying the taxonomy of 97% of the ASVs used in the relative
 abundance analysis (Extended Data Fig. 4).
 <strong>Phylogenetic trees of viral heat-shock proteins and
 metacaspase</strong> To examine the evolutionary
 history of the heat-shock proteins encoded in GVMAG-M-3300020187-27,
 phylogenetic trees of these proteins were constructed together with
 homologs present in eukaryotes, bacteria, archaea, and other giant
 viruses. For this, a custom database of proteins from reference genomes
 was compiled from EggNOG v. 5.0<sup>65</sup>  (eukaryotes),
 bacteria and archaea (the Genome Taxonomy Database v.
 95)<sup>66</sup>, and other giant viruses (the Giant Virus
 Database9). For bacterial and archaeal genomes in the GTDB, proteins were
 predicted first with Prodigal v. 2.6.3<sup>67</sup> using
 default parameters. Proteins were searched against Pfam models for each
 protein using hmmsearch with the noise cutoff (--cut_nc) and subsequently
 aligned sequences with ClustalOmega v. 1.2.3 (default parameters).
 Phylogenetic trees were constructed using IQ-TREE v.
 2.1.2<sup>55</sup> (parameters m TEST -bb 1000 -T 6 --runs 10)
 using ultrafast bootstraps and with the best model determined with
 ModelFinder<sup>68</sup>.  Substation matrixes used for the
 phylogenetic trees: Bax-1 - VT+F+R7; Metacaspase - VT+R7; HSP90 -
 LG+F+R10; HPS70 - LG+F+R10.
description: # Single-cell RNA-seq of the rare virosphere reveals the native hosts of
 giant viruses in the marine environment Supplementary Files used in the
 project. These are the main intermediate files that can help reproduce the
 data. For a detail description on how to reproduce these files and using
 them, Go to the GitHub site:
 [https://github.com/vardilab/host-virus-pairing](https://github.com/vardilab/host-virus-pairing) ## Description of the data and file structure Fromm_2023_Data_Availability Sequences GVDB.markergenes.90.fna # De-duplicated database of NCLDV marker genes Transcripts_Cells_Ehux-EhV.95.fasta # The host-virus reference, based on the single-cell transcriptomes of infected cells, to which we added genes from EhV and E. huxleyi. Transcripts_Katablepharidacea.fasta # Transcripts assembled from a highly infected subpopulation of Katablepharidacea Blast_results first_cells.transcripts.edit.metaPR2.csv # Blastn results of single-cell transcripts assembled from highly infected cells (~970) detected against metapr2 database. first_cells.transcripts.edit.PR2.csv # Blastn results of single-cell transcripts assembled from highly infected cells (~970) detected against pr2 database. all_cells.transcripts.edit.metaPR2.csv # Blastn results of single-cell transcripts assembled from all cells (~28,000) detected against metapr2 database. all_cells.transcripts.edit.PR2.csv # Blastn results of single-cell transcripts assembled from all cells (~28,000) detected against metapr2 database. cells.filtered.blastx.csv # Blastx results of single-cell assembled transcripts against refseq database (to find viral transcripts). DATA-SPECIFIC INFORMATION FOR: all_cells.transcripts.edit.metaPR2.csv 1. Number of variables: 12 2. Number of rows: 1409286 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * length: alignment length (sequence overlap) * mismatch: number of mismatches * gapopen: number of gap openings * qstart: start of alignment in query * qend: end of alignment in query * sstart: start of alignment in subject * send: end of alignment in subject * evalue: expect value * bitscore: bit score DATA-SPECIFIC INFORMATION FOR: all_cells.transcripts.edit.PR2.csv 1. Number of variables: 12 2. Number of rows: 1549087 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * length: alignment length (sequence overlap) * mismatch: number of mismatches * gapopen: number of gap openings * qstart: start of alignment in query * qend: end of alignment in query * sstart: start of alignment in subject * send: end of alignment in subject * evalue: expect value * bitscore: bit score DATA-SPECIFIC INFORMATION FOR: first_cells.transcripts.edit.metaPR2.csv 1. Number of variables: 12 2. Number of rows: 56763 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * length: alignment length (sequence overlap) * mismatch: number of mismatches * gapopen: number of gap openings * qstart: start of alignment in query * qend: end of alignment in query * sstart: start of alignment in subject * send: end of alignment in subject * evalue: expect value * bitscore: bit score DATA-SPECIFIC INFORMATION FOR: first_cells.transcripts.edit.PR2.csv 1. Number of variables: 12 2. Number of rows: 67864 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * length: alignment length (sequence overlap) * mismatch: number of mismatches * gapopen: number of gap openings * qstart: start of alignment in query * qend: end of alignment in query * sstart: start of alignment in subject * send: end of alignment in subject * evalue: expect value * bitscore: bit score DATA-SPECIFIC INFORMATION FOR: cells.filtered.blastx.csv 1. Number of variables: 10 2. Number of rows: 2021 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * bitscore: bit score * domain: domain of life the of subject * phylum: phylum the of subject * family: family the of subject * genus: genus the of subject * species: species the of subject * cell barcode: cell barcode of query UMI_tables_first_cells data_raw.pickle.gz # Data combined from all UMI tables of fastq files mapped to the NCLDV marker gene database UMI_tables_scatterplot data_raw.pickle.gz # Data combined from all UMI tables of fastq files mapped to the host-virus reference data.pickle.gz # Preprocessed ata combined from all UMI tables of fastq files mapped to the host-virus reference metadata_dimentionality_reduction_1_1.2_.pickle.gz # Metadata of preprocessed data, after dimensionality reduction. ## Access information The PR2 database can be accessed from here: [https://github.com/pr2database/pr2database](https://github.com/pr2database/pr2database)