dataesr/scientific-paragraphs-categorization huggingface dataset:
The two phage genomes assembled as four contigs with two of the contigs covered 2660-fold and 3321-fold (pair 1) while the other two were covered 920-fold and 833-fold (pair 2). For each contig pair, a "front half " and "back half " of an Stb20-like genome could be identified, leading to the conclusion that these were two Stb20-like phages which could not be assembled as only two contigs, likely due to similar sequences being present in the two genomes (sequences corresponding to ssDNA binding protein, replication initiator and helicase genes). Read mapping of 10F reads to the highly similar 10AX genome confirms the presence of reads covering these three genes in the 10F dataset and shows that they are identical to the two phages identified in this forearm sample. The front and back halves of each contig pair were combined to form pseudo-genomes, designated 10F_5 and 10F_7 with which to compare to the other identified genomes (Fig.
As the proteins in the "back halves" of some of these genomes show highest homology to both CNPH82 or vB_SepiS_phiIPLA5, these recombinations may have happened at the single gene level. BLASTn of the Stb20 genome sequence against the NCBInr database (February 15th, 2017) show substantial regions of nucleotide identity (55% of the genome at 89% identity) covering the structural genes, to portions of three S. epidermidis genomes from Finland (HG813242. 1), Australia (LT571449. 1  47  and Russia (KT429161. 1; 2015), as well as one Staphylococcus xylosus genome (LN554884. 1)  48, 49. This suggests that these phages are closely associated with Staphylococcus species from human skin. These observations suggest three distinct possibilities: 1) That these phages perhaps infect hosts other than S. capitis and could suggest a wider host range 2) That genetic exchange between phages capable of infecting S.
Pseudomonas phages. One phage which was identified in samples from all individuals is most closely related (80% nucleotide identity over 19% of the length of the genome) to prophages identified on Pseudomonas savastanoi NCPPB 3335 genome (CP008742), and more distantly to Pseudomonas infecting phage vB_PaeP_ Tr60_Ab31. Remarkably, the sequence of all contigs identified was perfectly conserved at the nucleotide level. One possible explanation is that these sequences were derived from contamination of the samples, in which case one could expect them to be present in all samples, which was not the case. Furthermore, it could be identified in other skin metagenomic datasets (see below). This may point to the host being ubiquitous in the environment and often shared between individuals, resulting in the same sequence being detected, or that strong selective pressure exists to maintain the genome as is.
No other contig of equal coverage, potentially harbouring the terminase, could be identified in these same samples. The high coverage of some of these genomes together with the recovery of the same contig length from several individual samples (which were circularly permuted relative to each other; 37851 bp contig ex 9 S detected as circular) suggests that these genomes are complete, or nearly so. It may be that the terminase, in this case, is radically different from those found in current databases or this role may be fulfilled by other terminases present in the host analogous to the ΦAH14a and ΦAH14b satellite phage helper system  60. The highly novel, narrow host range, siphovirus phiPSA1 that infects Pseudomonas syringae was shown to be temperate  61. Twenty-seven contigs showed best BLAST hit to phiPSA1 (Supplementary Table  S2 ) and as for this phage, they show highest similarity to prophages on a wide variety of Pseudomonas genomes, all of which encode integrases indicating that they are likely temperate.
A phage with relation to Pseudomonas infecting cytotoxin-converting phage phiCTX was also identified in sample 3S  67  , however no ctx toxin could identified on the genome (Supplementary Fig. S7 ). Acinetobacter phages. Two of the phage genomes that were detected as circular from sample 2F (2F_1 and 2F_2; Supplementary Table  S2 ) likely originate from Acinetobacter species. The host assignment is based on high nucleotide identity for segments of the phage genomes (BLASTn 34% query coverage/77% identity for 2F_1 and BLASTn 5% query coverage/85% identity for 2F_2) as well as high amino acid identity of many of the proteins encoded by these phages to Acinetobacter genomes on GenBank, suggesting that there are prophage relatives on these genomes. However, these were not identified as possible hosts by purely looking at the taxonomic assignment given to contigs, showing the current limitations of viral metagenome analysis for both these phages, a variety of proteins of which share highest protein similarity to a range of prophage genomes as opposed to just one (Supplementary Fig.
Acinetobacter species have emerged in recent years as a significant threat to human health  69  and phage therapies are sought that could be employed to target these pathogens. The identification here of two novel phages putatively targeting these microbes adds to the list of phages that could potentially be used to combat such infections. Presence of skin phages in other metagenomic datasets. To determine whether the phages described here were unique to our dataset or if they could be identified in other publicly available skin metagenome and metavirome datasets, reads and contigs from four select skin metagenome and metavirome studies were mapped against our phage genomes (Supplementary Table  S3 ). For the Oh and Human Microbiome Project (HMP) datasets, only retro auricular crease (behind the auricle of the ear) data was interrogated.
Reads from the total Hannigan dataset could be mapped to 130 of our contigs and to 29 from the HMP retro auricular crease data (Supplementary Table  S3 ). Of the unique reads mapped to our contigs, the bulk mapped to Staphylococcus phage-like contigs with 79% from the HMP data and 61% from the Hannigan data. Non-Staphylococcus phage-like contigs only recruited a small number of reads each from these datasets (Supplementary Table  S3 ) with two exceptions; the phiCTX-like phage and a phiSM-like Cellulophaga phage from subjects 3 and 9 also present in the Hannigan dataset. Although large portions of the Stb20-like genomes could be reconstructed from the Hannigan dataset, and to a lesser degree the HMP data, they could not be identified in the Chng or Oh datasets (Supplementary Table  S3 ). They were the group of phages that recruited most reads from both these datasets with 54% of unique mapped reads from the HMP data and 23% from the Hannigan data.
The 12562 reads that mapped to the 16872 bp contig and 6705 reads that mapped to the 13110 bp contig, both from sample 7S, were not considered as genuine, as the bulk of these reads were small (>45 bp or >80 bp) and mapped to only one location on either contig. Only one of our contigs could tentatively be identified in the Oh data, the Staphylococcus infecting IME_SA4-like phage, with one 200 bp read mapping to its terminase large subunit (3S, 42828 bp). Given the evidence of a global distribution for Stb20-like phages in our study and its substantial presence in the Hannigan dataset, where skin viruses were targeted, it is surprising not to find them in the Oh or Chng datasets. However, differences in participant demographics and/or health status could account for this. The Hannigan and Oh datasets were produced from North American volunteers, while the Chng data was from Asian volunteers.
It has been demonstrated that the Chinese community has a different microbial community composition compared to Western individuals, with lower relative abundance of Staphylococcus species  70, 71. Furthermore, the Chng study data was generated from individuals with atopic dermatitis, a condition that may result in an altered skin microbiome and therefore phage community. These two factors may explain the absence of these phage signatures. It appears that the technique (metavirome vs metagenome) used to specifically probe for skin viruses (Hannigan study) resulted in a dataset from which we could map reads to the greatest number of our contigs. It may be expected that there could be differences between the Chng and Oh/Hannigan data, however, our inability to detect even traces of these phages in these datasets, either indicates their absence, or that deeper sequencing or targeted sampling is required to detect them.
To establish whether the phages identified here were unique to skin or are readily found in other environments, we compared our contigs to those in the "earth virome" contig set  72. No contigs larger than 10 kb found a full-length match in the "earth virome" contig set indicating the novelty of the phages identified here (Supplementary Table  S3 ). We could however detect the presence of related phages. Of the unique phages, large portions of three could find matches in this dataset. The phage with the closest match to a contig in the "earth virome" was phiILPA-C1C with >108 kb of its genome recovered at 100% nucleotide identity, also from a human skin metagenome sample. Large portions of Streptococcus 20617-like phage (63% of the genome) and Vibrio VP882-like phage (65% of the genome) from 3S could also find sequence similarity.
37% of the top hits were related to HMP data (skin, nares, stool or tongue dorsum), suggesting that these phages are most closely associated with human samples (Fig. 4 ; Supplementary Table  S3 ). The remainder (63%) have best BLAST hit to other sources indicating that many of these contigs could come from sources other than the skin microbes themselves. Whether this resulted from the acquisition of the phages from the environment on the day of sampling or due to a database bias toward environmental samples cannot be discerned. 97% of the Staphylococcal-phage hits were to sequences derived from human samples and for the Stb20-like contigs, most hits were to retro auricular crease data, and mostly absent from environmental datasets. Of the unique contigs, 55% were human associated and 22% of shared phages.
We therefore examined whether there are CRISPR "adaptive immunity" systems targeting the phages described here by BLASTn comparison of the genomes identified here against the CRISPR finder database. Spacers could tentatively be identified for 115 of the 159 contigs. For many, the organism in which potential spacer hits were found are evolutionarily distant from the proposed host identified in our analysis (Supplementary Table  S4 ). Only 34 of the 115 identified contigs had spacers in genera that correlated with the taxonomic assignment, and these were mostly dominated by Pseudomonas-infecting related phages. For the Ab31-like Pseudomonas phages a spacer (32 nt) was identified with two nucleotide mismatches in the recently sequenced (2015) Pseudomonas sp. MRSN12121 GCF_000931465, a multidrug resistant isolate from urine.
X513 CRISPR arrays and in the Caldicellulosiruptor saccharolyticus DSM 8903 genome. These results are not expected to be a true reflection of the phages' host range, and warns about the use of CRISPR spacer detection for host range determination. The absence of CRISPR spacers targeting this phage is surprising given its apparent ubiquity in genomes. This absence may be a database limitation, although Staphylococcus genomes are some of the best represented in current databases, or could point to a mechanism of CRISPR evasion or selection against protospacer sequences employed by these phages to avoid CRISPR mediated destruction, which has not yet been described.
Conclusion This study describes an enriched human skin metavirome that shows similar phage signatures to the only other dataset dedicated to studying human skin virus populations. A caveat of our analysis is that as it is an enriched metavirome, and therefore is by no means a complete representation of the skin virome. It does not reflect which phages are present on the skin at a particular point in time but gives insight into which phages are present in skin-associated bacteria. Our study does highlight one of the limitations associated with current metavirome analysis. We show two examples of hybrid phages (Stb20-like and Ab31-like) which, as smaller contigs in a metavirome study, could be assigned different taxonomies based on sequence similarity. This begs the question as to how many such fragments are present in current metavirome datasets artificially increasing the diversity of phages present when using reference based taxonomic assignments.
The solution, where whole phage genomes can be reconstructed, may be to compare contigs across metagenomic datasets and first assign all contigs with similar (identical) sequence to one taxonomic affiliation. A long k-mer based analysis such as that used by the crAss program can be useful to develop a better understanding of the variability between individuals and body sites or metaviromes of any study site  73. Despite most studies indicating that the microbial composition is unique per person and per body site, the identification of several phages from our dataset in those generated in geographically distant locations seems to support the notion that although the microbiomes are overall unique, there are clearly shared bacteria and viruses as noted previously  72. The question is whether these are identical or more distantly related.
This can only be answered with the recovery of full length genomes. Furthermore, the presence of the phiPSA1-like and Ab31-like phages described here seems to show that highly similar, if not identical, phages may be in operation on the skin of people who live in close geographical proximity. This should be expected in urban settings where the exchange of skin microbes may be a frequent occurrence. If identical or nearly identical phages are found to be universally present on healthy skin, but not on "diseased" skin, they could be explored as biomarkers for detecting imbalances in skin microbial communities. If we consider our BLASTn results as a better predictor of the viral host for the various contigs described here, then many of them infect species, the genomes of which are adequately represented on the NCBInr database, from where the CRISPR database derives.
Metagenomic Sequence Analysis The fosmid DNA of the clone hits was extracted using the NucleoBond Xtra Midi kit from Macherey-Nagel (France) on the recommendation of the supplier. Fosmids were then sequenced using the MiSeq System (Illumina) at the GeT-PlaGe Genomics Platform (Auzeville, France). Read assembly was performed using Masurca 1. The contigs were cleaned from the pCC1FOS vector sequence using Crossmatch 2. The three annotated contig sequences were deposited in the DDBJ/ENA/GenBank Nucleotide Sequence Database under accession numbers LT674548, LT674550, and LT674549. Open-reading frames (ORFs) of at least 20 amino acids were predicted using MetaGene  (Noguchi et al. , 2006). ORF functions were inferred based on BLASTX analysis against the NCBI non-redundant and environmental databases (e-value < 10 -8 , identity > 35%, and query length coverage ≥ 50%).
The ORFs were assigned to clusters of orthologous groups of proteins (COGs) using RPS-BLAST analysis against the COG database (e-values ≤ 10 -8 ). A comparison was performed using BLASTP analysis against the Laccase and multicopper oxidase engineering database (LccED) (e-value < 10 -8 , identity ≥ 20%, and query length coverage ≥ 20%)  (Sirim et al. , 2011). The protein signatures were detected using the InterProScan web service 3  (Jones et al. , 2014). Contig taxonomic assignment was carried out using MEGAN v5. 10. 6  (Huson et al. , 2011) , based on BLASTX analysis against the non-redundant NCBI database (min. score = 35, min. support = 1). Contigs were assigned to a class, genus, or species only if at least 50% of the ORFs were assigned to the same organism. 1 http://www. genome. umd. edu/masurca.
Functional Annotation The results of the BLASTP comparison with the NCBI_NR and Swissprot protein databases, as well as protein signature detection using InterProscan, are provided in Supplementary Table  S1. Mining our metagenomic sequences for laccase encoding genes by comparing these with the LccED database did not produce significant results. This would be consistent with the fact that copper is not essential to the activity we detected for these three clones. Functional annotation allowed the identification of at least one gene for each clone that might be responsible for the activity detected (Supplementary Table  S1 ). In Contig 1, ORF 15, annotated as D-3-phosphoglycerate dehydrogenase (PGDH), was the most probable target. The best BLASTP hit with proven activity was a distant D-lactate dehydrogenase from Lactobacillus pentosus (from the phylum Firmicutes;  Taguchi and Ohta, 1991) , which shares 29% identity across 65% of the sequence length.
Finally, their suitability for use in biorefineries will have to be established by testing these clones on native lignin matrixes, and the way they function on this kind of substrate will also need to be studied further. The datasets generated in the course of this study are available in the repository of the DDBJ/EMBL/GenBank Nucleotide Sequence Database under accession numbers LT674548, LT674549, LT674550 4.
ACKNOWLEDGMENTS We would like to cordially thank  Nadège Beury ,  Emilie Amblard , and  Zhi Wang  for their technical assistance. The high-throughput screening work was carried out at the  Laboratory for BioSystems and Process Engineering (Toulouse, France ) using tools provided by  ICEO , a service dedicated to the screening and discovery of new enzymes. ICEO was supported by grants from the  Région Midi-Pyrénées , the  European Regional Development Fund , and the  Institut National de la Recherche Agronomique (INRA).
FUNDING This research was funded by the  French Ministry of Education and Research (Ministère de l'Enseignement Supérieur et de la Recherche) , the  INRA  metaprogramme  M2E  (project  Metascreen ), the  French National Research Agency  (project  REFLEX , grant number  ANR 2011 NANO 007 03 ), and the  European Union 's framework programme  Horizon 2020  ( H2020-BBI-PPP-2015-720303 , Zelcor).
The summary effect sizes estimated for the 9 measures are largely inconsistent with the theoretical expectations and show a significant amount of residual heterogeneity. Variables such as dispersal distance, geographic and environmental coverage of the data, appear important in explaining the variation observed between different species, but the results are contrary to those originally hypothesized, and inconsistent across centrality/marginality measures and the datasets used. We show that addressing common pitfalls in previous studies does not provide more support to the abundant-centre hypothesis, with support being very dependent on the centrality/marginality measure tested, the geographic extent considered for the test, and geographic and environmental coverage of the data. The abundant-centre hypothesis so far remains an appealing speculation with little and variable empirical support.
Second, many of these studies limited their analyses to a given geographic area, irrespective of the range-wide distribution of the species considered  (Blackburn et al. 1999b , Martínez-Meyer et al. 2013 , Dallas et al. 2017) , which can result in important biases in the estimate of distance from centre to edge, and in large underestimation of species niches  (Guisan and Thuiller 2005, Pironon et al. 2017). Third, several approaches have been used to define species geographic distribution. Dallas et al. (2017) , for example estimated species geographic ranges by fitting a minimum convex polygon (MCP) around their abundance sampling sites, and repeated the analysis using an MCP fitted around occurrence records collected from the GBIF database (< www. gbif. org/ >), which is notoriously affected by errors and geographic biases in species occurrences  (Beck et al.
Data collection We obtained population density estimates for bird and mammal species from the TetraDENSITY database  (Santini et al. 2018a). This database encompasses global georeferenced population density estimates for terrestrial vertebrates. We excluded all migratory birds from the selection, for which the geographic range is divided in breeding and non-breeding areas and the definition of a unique geographic or environmental space is challenging. To match the environmental predictors we only retained population density estimated after 1980. We defined independent locations as locations in different 1-degree cells (~ 110 km at the equator), and excluded all density records with a spatial precision lower than 1-degree. We selected species for which density estimates were available in at least 5 independent locations within the species range and retained multiple population density estimates at the same locations when available.
This criterion ensured to have density estimates that were not clumped in both geographic and environmental space and covered several hundred kilometres. The final dataset included 9 bird and 99 mammal species (Supplementary material Appendix 1 Table  A1 ). We used species range polygons as mapped by the IUCN Red List assessment and BirdLife International (BirdLife International and Handbook of the Birds of the World 2017, IUCN 2017) resampled at 1-degree resolution to meet the spatial accuracy of the data. To model species niches we considered the bioclimatic variables available from CHELSA  (Karger et al. 2016)  for the period 1979-2013, the land cover variables available from HYDE 3. 1  (Klein Goldewijk et al. 2011)  for the period 1980-2010, and the AVHRR NDVI 3g v. 1 time series from < https://ecocast.
Sensitivity to more stringent criteria of data selection Because density estimates are influenced by the sampling method and this may reduce their comparability, we repeated the analysis for a subset of density records that met more stringent criteria. For each species we selected only estimates derived from the same methodological approach (e. g. distance sampling, mark-recapture, censuses, …)  (Santini et al. 2018a). This restricted our dataset to 2 species of birds and 56 species of mammals, and included multiple correlation coefficients for some species (one per sampling method) (Supplementary material Appendix 1 Table  A2 ). We ran a Crosses indicate the location of the density estimates. CD/ED and envCD/envED were log-transformed for visualization purposes, they were not log-transformed for the analysis as Spearman's rank correlation is insensitive to transformation.
(2000)  and  Santini et al. (2013) , respectively. We obtained species body mass from the EltonTrait database  (Wilman et al. 2014). The geographic distribution of data points over species range can influence our ability to detect a signal in the data. We measured geographic coverage as the area percentage of the minimum convex polygon around density records, over the minimum convex polygon around the species geographic range. Similarly, the extent to which environmental conditions vary across sampled locations within the species ranges can determine whether we should expect a corresponding gradient in population density. Following  Dolédec et al. (2000) , we calculated niche environmental tolerance (niche breadth) for each species using the values of mean annual temperature, temperature seasonality, annual precipitation and precipitation seasonality observed in the location of the density estimates.
All analyses were entirely conducted in R 3. 0. 3 (R Core Team), using the package 'rgeos'  (Bivand and Rundel 2013) , 'geosphere'  (Hijmans et al. 2015) , 'maptools' (Lewin-Koh and Bivand 2011) and 'raster'  (Hijmans and van Etten 2014)  for GIS operations, 'biomod2'  (Thuiller et al. 2009 ) and 'adehabitatHS'  (Calenge 2006)  for the species distribution and ENFA models, 'wCorr' package for estimating weighted correlation coefficients  (Emad and Bailey 2017) , and 'metafor'  (Viechtbauer 2010)  for the meta-analysis.