dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013; Kashtan et al. , 2014) , and offer a rich source of novel viral sequences. These data will include prophage sequences, as well as viruses from actively lytic infections. Such SAG-based viral signal has already provided insights into marine viral diversity and virus-host interactions in uncultivated protists, bacteria and archea  (Yoon et al. , 2011; Roux et al. , 2014b; Labonté et al. , 2015). Third, large genome fragments of uncultivated microbes and associated viruses can now be assembled from microbial metagenomes  (Sharon et al. , 2009; Sharon et al. , 2011; Narasingarao et al. , 2012; Albertsen et al. , 2013; Anantharaman et al. , 2014). Finally, viral metagenomics (viromics) can be used to survey the sequence data associated with purified viral particles and can also result in assembly of large viral genome fragments  (Emerson et al.
, 2012; Minot et al. , 2013; Roux et al. , 2013; Brum et al. , 2015). Numerous approaches are available to identify prophages in complete microbial genomes including Phage Finder  (Fouts, 2006 ), Prophinder (Lima-Mendez et al. , 2008) , PHAST  (Zhou et al. , 2011) , and PhiSpy  (Akhter, Aziz & Edwards, 2012). Overall, prophage predictors rely on the detection of sequence similarities between regions of the microbial genome and known viral genes. In addition, PhiSpy also identifies "viral-like" genomic features (AT and GC skew, protein length and transcription strand directionality) to enable the detection of viruses absent from databases  (Akhter, Aziz & Edwards, 2012). Prophage predictors also look for prophage "ends" by identifying the attachment sites in the microbial genome for each predicted prophage.
These tools are either designed for a user to download and run locally (PhiSpy, Phage Finder) or to access through a web-server (PHAST). However, new tools are needed that (i) advance viral detection beyond prophages and instances where new viruses closely match those available in databases, and (ii) can handle fragmented and larger-scale microbial genomic datasets. Here, we present VirSorter, an automated tool designed to detect viral signal in genomic datasets, and make this new tool and the associated databases freely available in the Discovery Environment of the iPlant Cyberinfrastructure  (Goff et al. , 2011). Overall, we demonstrate that VirSorter detects prophages in complete microbial genomes as well as current prophage tools, but also offers capabilities to detect viral sequences in fragmented genomic datasets including incomplete genomes, SAGs or metagenomic assemblies, and can be used to flag potential cellular contamination in viromes for removal.
Building reference databases for bacterial and archaeal viruses Two reference databases of viral protein sequences were built for VirSorter and are available in the iPlant Discovery Environment (Data/Community Data/iVirus/VirSorter/Database). The first includes 114,297 proteins from viruses infecting bacteria or archaea in RefSe-qVirus genomes (as of January 2014), hereafter named "RefSeqABVir. " Protein clusters (PCs) were defined using MCL clustering  (Enright, Van Dongen & Ouzounis, 2002)  of these proteins (inflation 2. 0) based on their reciprocal blastp comparisons (threshold of 50 on bit score and 10 -03 on E-value). The 9,735 PCs with at least 3 sequences were used to define a profile database searchable with HMMER3 tools  (Eddy, 2011). The remaining 34,668 unclustered sequences were formatted for a blastp search.
All PCs that did not contain any sequences from Caudovirales and unclustered sequences from viruses other than Caudovirales were marked as "Non-Caudovirales. " The RefSeqABVir database was then augmented by virome sequences sampled from freshwater, seawater, and human gut, lung and saliva, resulting in an extended version of the reference database (hereafter named "Viromes") which includes both virome and RefSeqABVir sequences. This combined reference dataset should help to detect new viruses for which no cultivated reference sequence is available. When only raw reads were available, viromes were assembled using Newbler (threshold of 98% identity on 35bp). The resulting contigs were then checked for the presence of cellular genome sequences, and only the 68 viromes for which no 16S rRNA genes were retained (see Table  S1  for a complete list of these viromes).
Contigs assembled from these 68 viromes were then manually inspected (through annotations generated by Metavir;  Roux et al. , 2014a)  and revealed no identifiable cellular genome sequences (i. e. , no sequence contained more than 2 genes that matched a cellular genome and were not found in any known virus). A total of 146,521 complete predicted proteins from this quality-controlled dataset were then clustered with the 114,297 proteins from RefSeqABVir, leading to 15,673 clusters with 3 sequences or more, and 88,052 unclustered sequences. PCs from the combined Viromes database were used to create a profile database searchable with HMMER3, and the 34,338 unclustered sequences from RefseqABVir were formatted for BLAST search (unclustered sequences from viromes were not added to the database to prevent the inclusion of contaminating sequences).
Within these databases, viral "hallmark" genes were defined though a text-searching script looking for "major capsid protein," "portal," "terminase large subunit," "spike," "tail," "virion formation" or "coat" annotations. After a manual curation step removing genes with more general annotation such as "protease" or "chaperone," 826 PCs or single genes were identified as "viral hallmark genes. " This latter point meant removing domains also matching "protease" or "chaperone" domains and was conducted to minimize false positives for our viral hallmark genes category by extra-cautiously avoiding PCs that might include domains that could derive from either both viruses or microbes.
g. , "bridges" between contigs generated during scaffolding) and yielding false-positive matches when compared to protein domain databases. Predicted protein sequences are then compared to PFAM (v27) and to the viral database selected by the user (either RefSeqABVir or Viromes) with hmmsearch  (Eddy, 2011)  and blastp  (Altschul et al. , 1997)  and each gene is affiliated to its most significant hit based on alignment score. Thresholds for significant hits are as follows: minimum score of 40 and maximum E-value of 10 -05 for hmmsearch, and minimum score of 50 and maximum E-value of 10 -03 for blastp.
VirSorter metrics computation Following the sequence pre-processing, viral regions are detected through computation of multiple metrics using sliding windows. The metrics used are (i) presence of viral hallmark genes  (Koonin, Senkevich & Dolja, 2006; Roux et al. , 2014b) , (ii) enrichment in viral-like genes (i. e. , genes with best hit against the viral reference database, either RefSeqABVir or Viromes), (iii) depletion in PFAM affiliated genes, (iv) enrichment in uncharacterized genes (i. e. , predicted genes with no hits either in PFAM or the viral reference database), (v) enrichment in short genes (genes with a size within the 10% shorter genes of the genome), and (vi) depletion in strand switching (i. e. , change of coding strand between two consecutive genes). For all the enrichment and depletion metrics, a score comparable to the one of Prophinder was used  (Lima-Mendez et al.
, 2008). For the detection of viral-like genes enrichment, two different values are computed for each dataset: one based on genes in the entire database (RefSeqABVir or Viromes), and another based on non-Caudovirales genes only. Indeed, Caudovirales genomes represent 81% of RefSeqABVir, and the remaining viral families usually have only a handful of reference genomes. The global rate of viral-like genes in cellular genomes is thus usually one order of magnitude lower when considering only non-Caudovirales genes (viral-like genes ratio across the bacterial and archaeal class for which complete genomes are available at NCBI RefSeq and WGS ranges from 4. 8 to 16%, with an average of 10. 6%, whereas the ratio of non-Caudovirales genes in these same genomes ranges from 0. 01 to 1. 4%, with an average of 0.
Finally, if a predicted region spans more than 80% of predicted genes on a contig, the entire contig is considered viral. A summary of VirSorter detection types is displayed in Fig. 1B. Next, higher confidence predictions are used to refine the sequence space search. Specifically, sequences from all open reading frames from category 1 predictions that do not match a viral protein cluster are clustered and added to the reference database (RefSeqABVir or Viromes depending on the initial user choice). This updated database is then used in another round of search by VirSorter. This iteration where category 1 sequences are used to refine the searches is continued until no new genes are added to the database. Once no new genes are added, the final VirSorter output is provided to the user and includes nucleotide sequences of all predicted viral sequences in fasta files, an automatic annotation of each prediction in genbank file format, and a summary table displaying for each prediction the associated category and significance scores of all metrics.
By providing the predictions and the underlying significance scoring, users can evaluate each prediction and apply custom thresholds on significance scores through a simple text-parsing script, even for large-scale datasets. VirSorter is available as an application (App) in the iPlant discovery environment (https://de. iplantcollaborative. org/de/) under Apps/Experimental/iVirus (see Fig. S1  for a step-by-step guide of VirSorter app on iPlant). This application allows users to search any set of contigs for viral sequences using either the RefSeqABVir or the Viromes database. The reference values of VirSorter metrics will be evaluated on the complete set of input sequences, hence mixed datasets should be sorted (when possible) by type of bacteria or archaea in order to get the most accurate result possible.
In addition to these reference origin that are annotated as "major capsid protein," "portal," "terminase large subunit," "spike," "tail," "virion formation" or "coat" and manually removing all protein domains with a potential overlap with microbial functions. (B) Examples of viral sequence detection by VirSorter. On top is the clearest case, in which a sequence harbors several viral hallmark genes as well as enrichment in viral-like genes (or virome-like when the genes are most similar to a viral metagenome sequence, when using the Viromes database). This type of detection is considered as the most confident. The three examples below are different cases in which only one of the primary metrics is significant. Notably, these examples display how VirSorter can detect new viruses based on a significant depletion in characterized genes associated with a viral hallmark gene (case 3), and how the same number of genes can be a non-significant enrichment when considering all viruses, yet significant when looking at only the non-Caudovirales (case 4).
These detections are still considered confident, although less sure than case 1. Finally, a last example (case 5) displays a more ambiguous situation, in which a sequence displays only secondary viral metrics but neither viral gene enrichment nor a viral hallmark gene. For these detections, one of the metrics (at least) must have an E-value lower than 10 -04 (note that significance scores used in VirSorter output files are computed as negative log 10 transformations of E-values, and would here correspond to a score of 4 or more). databases, the VirSorter App on iPlant allows users to input their own reference viral genome sequence already assembled or to-be assembled using iPlant Apps prior to analysis with VirSorter. Assembled sequences are processed as follows: (i) genes are predicted with MetaGeneAnnotator  (Noguchi, Taniguchi & Itoh, 2008) , (ii) predicted proteins are clustered with sequences from the user-selected database (either RefSeqABVir or Viromes), and (iii) unclustered proteins are added to the "unclustered" pool.
All the additional detections were manually checked to verify if the region was indeed viral (originating from a prophage in one of the microbial genomes rather than from a viral genome) or a false positive. The same approach was used for the simulated microbial and viral metagenomes results. For each set of predictions, two metrics are computed. First, the Recall value corresponds to the number of viral sequences correctly predicted divided by the total number of known viral sequences in the dataset, and reflects the ability of the tool to find every known viral sequence in the dataset. Second, the Precision value is computed as the total number of viral sequences correctly predicted divided by the total number of viral sequences predicted, and indicates how accurate the tool is in its identification of viral signal.
Simulation of draft genomes and metagenomes A total of 10 Single-cell amplified genomes, 10 microbial metagenomes and 10 viral metagenomes were simulated with NeSSM  (Jia et al. , 2013). Microbial genomes were randomly picked within the bacterial and archaeal genomes available in RefSeq and WGS (as of January 2014). Viral genomes were picked within the most recently submitted genomes (since June 2014), thus are not in VirSorter reference database. Simulated inputs for each genome group (viral and microbial) followed a power-law distribution of abundances within the microbial and viral communities. The proportion of viral reads varied from 5 to 20% for microbial metagenome, and from 75 to 99% for viral metagenomes (Tables  S4  and S5 ). For each simulated dataset, 100bp paired-end reads similar to those obtained with HiSeq Illumina were generated (100,000 for SAGs, 1,000,000 for metagenomes), QC'd with fastq quality trimmer with a threshold of 30 (part of the fastx toolkit, http://hannonlab.
cshl. edu/fastx toolkit/), and assembled with Idba ud  (Peng et al. , 2012). To identify viral sequences in the assemblies, the resulting contigs were compared to the viral genomes with nucmer  (Delcher, Salzberg & Phillippy, 2003) , and all sequences matching one of the viral genomes at 97% nucleotide identity or more were considered as viral. All simulated contigs and composition table (i. e. , relative abundance of each genome in the simulated dataset) are available in the iPlant Discovery Environment alongisde VirSorter results for each of these simulated datasets (/iplant/home/shared/imicrobe/VirSorter/Benchmark datasets and Benchmark results respectively).
Reference-dependent and general genome features used to detect viruses VirSorter is designed to predict viral sequences in complete or fragmented genome sequence data from bacteria and archaea. Viral sequences are identified through a combination of "primary metrics" linked to the detection of significant similarities with known viral sequences and "secondary metrics" associated with viral-like genome structure (Fig. 1A ). VirSorter first builds a probabilistic model for each metric using the microbial genomic data provided by the user (i. e. , the complete genome or the entire contig dataset for draft genomes or metagenomes) that is then used as reference to calculate enrichment/depletion statistics. A "statistical enrichment in viral gene content" for a set of genes thus indicates that the region evaluated displays more viral-like genes than would be expected by chance alone based on the overall frequency of viral-like genes in the whole dataset.
Viral-like genes are identified through comparison to RefSeq viral genomes ("RefSeqABVir" database hereafter) or to a custom database built from RefSeqABVir to which curated virome datasets were added to improve novel virus detection capabilities (hereafter "Viromes" database, Fig. 1A  and Table  S1 ). Through the VirSorter application (App) on iPlant, users can also add their own viral genome sequence(s) (in fasta format), which predicted protein will be added to the user-selected database (either RefSeqABVir or Viromes).
Virsorter prophage prediction is comparable to existing tools To evaluate VirSorter performances, we first examined its prophage prediction capability as compared to existing tools. Specifically, we used a set of 267 manually annotated prophages from 54 bacterial genomes  (Casjens, 2003)  to compare the prophage prediction performances of VirSorter, PhiSpy, Phage Finder, and PHAST. We evaluate performance using two metrics: (i) "Recall," the number of viral regions detected divided by the total number of viral regions (also known as "Sensitivity") and (ii) "Precision," the number of correct predictions divided by the total number of predictions (also known as "Positive Predictive Value"). All of the tested prophage prediction tools perform well on these complete genome datasets as Recall values range from 64 to 85%, and Precision values range from 74 to 93% (Fig.
, 2014b). Of the 69 known viral contigs in this dataset, 62 were detected by VirSorter (including 29 as category 3), with the remaining 7 being too short (5. 1kb on average) to provide significant enrichment scores. In contrast, PHAST, PhiSpy and Phage Finder detected only 15, 1 and none of these sequences, respectively. Beyond the fragmented nature of these SUP05 SAGs, these data likely represent a worst case scenario for the prophage prediction tools as these 69 SUP05 viruses represented new viral genera, and thus no closely related reference sequence were available in databases  (Roux et al. , 2014b).
VirSorter alone is able to mine viral signal from bacterial and viral metagenomes Next, we evaluated VirSorter's capability to recover viral sequences in fragmented genomes assembled from metagenomic datasets. To this end, we created 10 'metagenomes' from 15 microbial and 15 viral genomes at varying representative abundances (Table  S4  and see 'Methods'). These simulated datasets total 192,941 contigs, so the scale is quite largenone of the prophage predictors were able to even process the data in a reasonable time (i. e. , less than several days). Given that metagenome-derived contigs also represented fragmented genomes, we expect that performance would have been poor for prophage prediction tools on these datasets-likely even worse than the SAGs performance testing above. VirSorter, however, was designed for and is thus able to handle such datasets.
This time, we simulated metagenomes where viral reads represented a larger proportion of the dataset, ranging from 75 to 99% (Table  S5 ). Here, all microbial genomes available in RefSeq and WGS (as of January 2014) were used by VirSorter to model microbial genomic metrics instead of the whole dataset, since viromes largely lack microbial sequences. This usage case of VirSorter is implemented in the iPlant application and is available by checking the box "virome decontamination" in the submission form. As found above for prediction of viral sequence data from the microbial metagenome simulations, VirSorter performance as a 'virome decontaminator' improves as contig size increases (Table  2  and Table S5 ). When considering all contigs (>500bp), the Recall of viral sequences is 32% on average, but increases up to 86% for contigs >3kb and 97% for contigs >10kb.
VirSorter's strengths and weaknesses VirSorter represents a novel, scalable, and community-available tool for detecting and identifying viral genome sequences from diverse microbial datasets. Its performance for prophage prediction is largely comparable to that of available prophage prediction tools when applied to complete microbial genomes, but it outperforms available tools when making predictions from modern microbial datasets which tend to be fragmented and larger-scale or when searching for viruses beyond those "known" in current databases. Thus, VirSorter complements existing tools to help elucidate bacterial and archaeal viral sequences among myriad modern microbial genomic data types. However, VirSorter does have limitations. First, VirSorter was designed and optimized for detection of bacterial and archaeal viruses, so it does not detect eukaryotic viruses well.
This is because the database lacks eukaryote viruses, and the viral genome features were only evaluated on bacterial and archaeal viruses. VirSorter will still detect eukaryote viruses, often as category 3 because of their singular genome composition (compared to a typical cellular genome), but its capacity is extremely limited in its current build. Second, short (<3kb) viral contigs will tend to only be detected by VirSorter when they contain hallmark genes. Pragmatically, this means that viral signal detection in non-assembled reads or in contigs assembled from (meta)transcriptome data will usually be inefficient. Third, prophage prediction tools also look for additional signs of prophages such as the presence of integrase genes, att sites, or repeat features to demarcate the 'ends' of a prophage genome, none of these features are examined by VirSorter.
Field Data Collection Field data collection was conducted from December, 2013, to January, 2014. Twenty five randomly-stratified 1-ha plots were established in the forested portion of Angkor Thom (Figure  1 ), as per the RAINFOR protocol  [22]. One-hectare plots were chosen, as smaller plot sizes may increase the dominance of larger trees, causing an increase in the root-mean-square error (RMSE)  [23, 24]. Stratification was conducted on the basis of the four forest quadrants that lead to the north, south, east and west gateways of Angkor Thom. All of the forested areas leading inwards have faced different levels of anthropogenic disturbance and have been occupied by different disturbance niches. Plots were randomly located within each of the quadrants. Diameter at breast height (DBH) was recorded for all trees where DBH ≥10 cm.
Analysis of Aerial Imagery Data Fifty-centimetre resolution Digital Globe data (images dated 26 December 2012) were extracted over Angkor Thom, north-western Cambodia directly from Google Earth™ using Google Satellite Map Downloader ® (Version 7. 20)  [27]. These data have three spectral bands (red, green and blue (RGB)) and covered the entire Angkor Thom area (Figure  1 ). Eight-centimetre resolution aerial multispectral data were acquired in April 2013, using a 40-megapixel Leica RCD105 medium-format camera, collected by a helicopter at a flying height of 800 m above ground level. Aerial multispectral data also consisted of three RGB spectral bands. The wavelengths of RGB bands for high-resolution aerial data, including those from Google Earth™, are 0. 45 to 0. 52 nm, 0. 52 to 0. 60 nm and 9.
Random Forests Models RF models fit multiple decision trees to input data using a random subset of the input variables for each tree constructed. For a dataset with n features, square root of n may be used in each subset. A multitude of trees are constructed, and their average is used to construct a new predictive model  [37]. RF offers the advantages of being non-parametric, handling a large number of input variables and skewed data and preventing over-fitting  [38]. Both of the above modelling techniques were implemented using the caret package in the R programming language. For biomass modelling purposes, 14 of the 25 plots were randomly used for training/calibration purposes, and the remaining 11 plots were used for testing/validation purposes. Plots were randomly assigned to the training or testing set via the "random" function available in R.
The final predicted model was used with the test data (in this case, 11 plots) to generate predictions for the target variable. Two ML algorithms were used for generating predictions of field AGB using vertical canopy height, canopy cover and texture measures derived from different methods. The performance of the three models-(i) included vertical canopy height, horizontal canopy structure and texture variables as predictors; (ii) with canopy cover and texture variables; and (iii) with texture variables only-was compared using two different aerial datasets and ML algorithms.