dataesr/scientific-paragraphs-categorization huggingface dataset:
Cattle seroprevalence A total of 1011 cattle were sampled with 46. 7% from the porous (Malipati), 28. 2% from the non-porous (Chizvirizvi) and 25. 1% from the non-interface (Chomupani) areas. The distribution of the sampled animals and their RVF seroprevalence according to interface, sex, abortion history and season is shown in Table  1. The overall seroprevalence was 1. 7% (95% CI: 1. 01-2. 7) and it varied according to interface, but the differences were not significant (p > 0. 05). All positive cattle were females, but again the difference according to sex was not significant (p = 0. 27). Similarly, the difference in seroprevalence according to abortion history in cows was non-significant (p = 0. 44). In contrast, the wet season recorded a significantly (p = 0. 0002) higher seroprevalence compared with the dry season (data are for female cattle only).
Acknowledgements The authors thank the  Ministère Français des Affaires Etrangères  for supporting  Masimba Ndengu 's research through the  FSP-RenCaRe  project (FSP no. 2011/36 ). This work was conducted within the frame work of the  Research Platform 'Production and Conservation in Partnership'  (http://www. rp-pcp. org). They also thank the farmers from  Malipati, Chomupani and Chizvirizvi , and the staff of the  Veterinary Services and National Parks, and Wildlife Management Authority (Gonarezhou National Park ) for their collaboration.
INTRODUCTION Advances of molecular techniques have greatly contributed to the recognition of the importance of microorganisms in every ecosystem. In particular, it is now well established that most metazoans are associated with microbes, forming complex entities referred to as holobionts. In these systems, microbes are interacting together as well as with their host, and host-microbe interactions can have significant effects on the host phenotype  (1). Many animals have microbial associations with one or a few specific symbionts. This is, for example, the case for corals associated with the algal symbiont Symbiodinium (2), squids with Vibrio (3), woodlice with Wolbachia (4) or hemipteran insects with specific obligatory and facultative bacterial symbionts  (5). As symbionts are generally not cultivable outside the host, the whole community is usually sequenced, resulting in a metagenomic dataset mixing host and symbiont reads.
These datasets are unbalanced: the great majority of the reads often originate from the host genome, but since the genomes of the symbionts are often several orders of magnitude smaller than that of the eukaryotic host, symbiont genomes can have large read depth in such samples. This enables the extraction of relevant information about the symbionts, but requires significant effort, since the host reads are a computational burden for most analyses. In this context, providing bioinformatic tools that enable the assembly of a particular genome of interest from a metagenomic sample, ignoring the overwhelming amount of reads from other organisms, would greatly accelerate the characterization of symbiont genomes, and therefore decipher particular host-symbiont relationships. In many cases, some knowledge and genomic resources about the symbiont of interest are already available.
A common problem is to recover the full genomic sequence of the particular symbiont present in the metagenomic dataset, using available genomic resources. The way we address this problem depends on the availability of a reference genome and its closeness to the considered species. In the easiest but rarest case, a reference genome of good quality is available for the considered species, and a mapping-based approach is classically performed. Reads from the whole metagenomic datasets are mapped on this reference genome and small variants are called to characterize the strain at play. This approach does not output per se a genome sequence but rather a list of punctual variants with respect to a reference genome, without any evidence that this list is exhaustive and ignoring potential structural differences, such as large novel insertions or deletions.
They are based on different signals computed on the contigs, including similarity with available genomic resources, but also nucleotide composition and read depth. However, their performances depend on the quality of the initial whole metagenomic assembly. Genome assembly from metagenomic data is a challenging task, because of both the many species coexisting in the samples and the polymorphism within these species. Although many recent software are devoted to this task  (9) (10) (11) , metagenomic assemblies are often very fragmented and come with a high computational cost  (12). In the particular case where reads originating from the targeted organism are a minority in the whole metagenomic readset, this computational cost could be reduced. In addition, one would expect that focusing on a subsample of reads would lead to less fragmented assemblies.
Therefore, combining mapping-based and assemblybased approaches in a so-called targeted genome assembly seems a promising way to facilitate the characterization of specific symbionts in metagenomic datasets. In such approaches, a subset of the metagenomic reads is first recruited by mapping to a reference genome and then de novo assembled. The relative proximity between the targeted and the reference genomes is a key parameter of the approach, and the methods must be able to incorporate non-recruited reads in the assembly, in order not to miss divergent or strain-specific regions. Several tools, such as MITObim  (13) , NOVOPlasty  (14) , LOCAS  (15) , Pilon  (16)  or IMR/DENOM  (17) , were designed following the idea of combining mapping to a reference and de novo assembly. However, they all have limitations that make them unsuitable to handle metagenomic data.
Primarily, except for NOVOPlasty, these methods rely on the assumption of genomic homogeneity (no polymorphism), which is very rarely met in a metagenomic context. Therefore, they are not suited to detect and characterize coexisting strains in metagenomic datasets. Moreover, these methods generally use the architecture of the reference genome as a starting point for the assembly, and are therefore unable to deal with large structural variants that differ from the reference genome (IMR/DENOM, Pilon, LOCAS). Finally, some tools are designed either for short organelle genomes (MITObim, NOVOPlasty) or for small resequencing datasets (LOCAS) and they are not scaling up to the size of metagenomic datasets. To our knowledge, a single tool has been designed to guide the assembly by a reference genome in a metagenomic context.
A novel method for targeted genome assembly with metagenomic data Approach overview. The method described in this work relies on a two-step pipeline, described in Figure  1. The first step uses a given reference genome to build an incomplete but trustworthy assembly from a subset of the input metagenomic reads. The result of this step is hereafter referred to as the backbone contigs. The second step uses the whole set of metagenomic reads to extend the previously assembled contigs and to form a complete assembly, without a priori on the order and orientation of the backbone contigs. The result of the pipeline is a genome graph encompassing the structural diversity detected among the assembled genome. This graph can be exploited by extracting contigs, or paths of the graph that represent different strains of the targeted genome.
This step is based on a module of the software MindThe-Gap, originally developed for the detection and assembly of long insertion variants  (21). MindTheGap is built upon the GATB library  (22) , which offers memory-and timeefficient data structures for de Bruijn graphs. The 'fill' module of MindTheGap builds a de Bruijn graph of the entire input readset, and performs a local assembly between the left and the right k-mers of each insertion site, by looking for all the paths in the de Bruijn graph starting from the left (source) k-mer and ending in the right (target) k-mer. In this work, we took advantage of this module of MindThe-Gap and adapted it to the problem of simultaneous gap filling between multiple contigs. It has been modified to make possible the gap filling between a source k-mer and multiple target k-mers, enabling the 'all versus all' gap filling within a set of contigs with a linear time increase.
This can be done either by manual inspection using interactive tools such as Bandage  (23)  or by enumerating all possible paths within the graph. Implementation and availability. MinYS is implemented as a python3 program and available at https://github. com/ cguyomar/MinYS and as a conda package in the Bioconda repository. Notably, it relies on the local assembly tool MindTheGap, starting from version 2. 1 that enables the so-called contig mode (https://github. com/GATB/ MindTheGap). The whole pipeline presented in Figure  1  can be run in a single command line with few parameters to tune and is implemented in a modular way enabling to start or resume the analysis at intermediary steps. The most influential parameters concern the two de novo assembly steps (backbone and gap filling). They are standard parameters of de Bruijn graph-based assemblers that depend mainly on the expected read depth of the targeted genome in the sample, namely the size of k-mers and the k-mer minimal abundance used to filter out sequencing errors.
Application to pea aphid metagenomic datasets In this study, we applied MinYS to the assembly of the pea aphid obligatory symbiont, B. aphidicola. We considered 50 pea aphid resequencing samples of Illumina 100-bp paired-end reads  (24). Thirty-two samples contain each the sequences from a single aphid clone. In addition, 18 samples sequenced from pooling 14-35 genetically distinct pea aphid individuals were also analyzed. These samples are more challenging for metagenomic assembly due to their richer symbiotic composition and strain diversity  (24). Scripts and commands used to perform all the analyses and create tables and figures are available and documented on the following GitHub repository: https://github. com/cguyomar/MinYS paper reproducibility.
The levels of divergence of these different reference genomes are supported by phylogenetic studies  (25) , whole genome alignments [average nucleotide identity (ANI)] and relative amounts of mapped reads from the real sequencing samples, compared to the closest reference genome (see Table  1 ). Dataset with simulated structural variations. To assess the ability of MinYS to recover structural variations in samples with strain diversity, we created a synthetic pea aphid sample by adding to a real sample a subset of simulated reads from the previously described incomplete genome (with 20 deletions). A 50× coverage of 100-bp reads was simulated with wgsim of the SAMtools suite  (26) , using the parameters '-1 100 -2 100 -N 131400'.
RESULTS In this study, we applied MinYS to assemble genomes of B. aphidicola (∼640 kb), the obligatory bacterial symbiont of the pea aphid. We considered a large set of pea aphid resequencing samples of Illumina 100-bp paired-end reads, which have already been studied in a previous work, in which the microbiota of each aphid sample was detailed  (24). Among the 50 datasets, 32 resulted from the sequencing of individual clones and 18 from the sequencing of pools of individuals from the same population. The number of reads is on average 84 (198) million for individual (pool) sequencing datasets, with an average coverage of 628× (3694×) for the B. aphidicola genome. In these datasets, >90% of the reads originate from the insect host and are not useful when focusing on symbiont genomes. This dominance of host reads in our metagenomic datasets justifies the choice of a targeted assembly approach, which does not require assembling pea aphid reads.
Assembly of coexisting structural variants MinYS was applied to a pea aphid sample in which simulated reads from a rearranged B. aphidicola genome were added to a real pea aphid resequencing sample, simulating the coexistence in a metagenomic dataset of two strains with structural differences (here 20 deletions whose sizes ranged from 300 bp to 20 kb). In the resulting genome graph, 17 out of the 20 simulated deletions were fully recovered, with both the deleted and complete versions of the genome assembled (Figure  3 ). Extracting the longest path from the graph resulted in a single contig of 641. 5 kb, compared to the 642 kb of the closest reference genome. Similarly, the shortest path extracted from the graph was 526. 4 kb long, compared to 525. 6 kb for the simulated deleted genome. The longest structural variants (up to 20 kb) were all successfully recovered.
Finally, we compared MinYS to a full metagenomic assembly approach with MEGAHIT, followed by BLASTbased contig filtering. On individual clone samples, MEGAHIT performs on par with MinYS, resulting in complete one-contig assemblies in most cases. However, when applied to samples of pooled individuals with more polymorphism, MEGAHIT does not perform as well, with more and shorter contigs (the median size for the largest contig is 57% of the expected genome length, compared to 101% with MinYS). An explanation for this lower performance could be that highly polymorphic regions may be assembled by MEGAHIT into distinct contigs that break the contiguity, while MinYS often represents them as bubbles in the genome graph. Notably, the metagenomic assembly approach with MEGAHIT performed poorly compared to MinYS on the metagenomic dataset simulating the co-existence of structural variants.
MinYS is a novel method leveraging the benefits of both reference-based and de novo assembly. By using a reference genome as a primer for the assembly, MinYS significantly reduces the computational burden of genome assembly from a metagenomic sample. In the first step, the assem-bly is restricted to a subset of the reads, and is therefore usually straightforward. During gap filling, although the whole readset is used, performing local assembly between specific source and target k-mers is also less demanding than complete metagenomic assembly. In addition, as long as trustworthy contigs are assembled in the first place, this local gap filling is less prone to include contaminant sequences. This makes the assembly both more reliable and faster. In this context of mining a particular symbiont genome out of a host-dominated sequencing dataset, MinYS was up to 12 times faster than a full metagenomic assembly.
Although full metagenomic assembly also delivers contigs for the other organisms of the community, we consider that the speed increase along with the contiguity improvement makes MinYS a worthy alternative to analyze particular components of microbial communities. Dedicated host-symbiont contig binning approaches, such as BlobTools and Autometa, rely heavily on the quality of the initial full metagenomic assembly. Bypassing this full assembly also makes MinYS a more generalizable approach to a variety of situations where de novo assembly is difficult, such as low-coverage host genome resequencing, pooled sequencing or more complex metagenomic datasets.
However, a direct comparison of the effects of OsHV-1 µvar and a reference variant has yet to be investigated in a single study, so it is unknown how a potential emergence of an OsHV-1 µvar would affect Pacific oysters if it were to spread within the US. Multiple OsHV-1 variants have emerged since its original detection in the 1990s, and these variants have spread rapidly across borders; therefore, it is crucial that an OsHV-1-resistant oyster line can withstand exposure to multiple OsHV-1 variants, not just a single OsHV-1 µvar. The Molluscan Broodstock Program (MBP) through Oregon State University has been selecting C. gigas families for yield and survival in estuaries along the West Coast since 1996, with seven generations of oyster families produced so far  [46]. More recently, MBP has begun identifying families for resistance to OsHV-1 and its variants based on survival when exposed to OsHV-1 in the field and OsHV-1 µvar in the laboratory  [47].
Larvae in this family still experienced mortality when exposed to OsHV-1 and accumulated OsHV-1 DNA in their tissues, which may become latent and spread at a later life stage  [66]. Furthermore, whether exposed larvae that survive can settle and continue to develop remains unknown  [66]. It will be important to select for families that demonstrate resistance (versus tolerance) to OsHV-1 across multiple life stages and are resilient to environmental stressors, to ensure survival when exposed to OsHV-1 variants in the field [47]. S4 ) were shipped overnight (25 June 2018) on ice from the Molluscan Broodstock Program (MBP) at Oregon State University to the University of Arizona Aquaculture Pathology Laboratory (UA APL). Prior to being shipped to France [47], a routine health exam of 175 juvenile oysters randomly chosen from these families was conducted by AquaTechnics Inc.
All standards and samples were run in duplicate. The concentration of DNA in ng/µL was determined for all positive samples using a Quant-IT ™ PicoGreen dsDNA Assay Kit (Molecular Probes) with fluorescent intensity (485 nm excitation, 535 nm emission) measured using a Molecular Devices FilterMax F5 microplate reader with SoftMax Pro 64 software (Molecular Devices, San Jose, CA, USA). Oyster tissue samples that were negative for OsHV-1 DNA were run in a second qPCR to ensure the presence of amplifiable DNA by amplifying the 18S gene of C. gigas using the primers CG 18S F (5 -CAG CGA AAG CAT TTG CCA AG-3 ) and CG 18S R (5 CAC CCA CCG AAT CAA GAA AGA G 3-')  [50]. The master mix concentrations are identical to those mentioned above, with a thermocycler protocol as follows: 95 • C for 20 s followed by 35 cycles of 95 • C for 3 s and 55 • C for 30 s, and a final melt curve analysis.
Statistical Analysis All statistical analyses were performed using R version 4. 0. 2 (The R Foundation, Vienna, Austria)  [72]. An alpha of 0. 05 was set for all significance tests. All figures were generated using the package ggplot2  [73] , except Figure  3 , which was generated using the package pheatmap  [74]. A survival analysis was conducted using the survival  [75]  and survminer  [76]  packages to generate Kaplan-Meier curves with log-rank chi-square tests and Cox proportional hazard ratios to investigate differences in survival probability of oysters exposed to each virus treatment. We also investigated the survival probability of the five (family 60 was excluded, see 2. 2) high-or low-surviving families (averaged across all viruses), and the survival probability of the individual five high-or low-surviving families among viruses and controls.
To investigate the effects of treatment on concentrations of OsHV-1 in tissues and water, we used linear models. All combinations of linear models were run to determine which factors contributed to accumulation of qPCR copy number in live oysters or water, with fixed effects for virus, oyster family, day, and their interaction. All qPCR data were log 10 (x + 1) transformed to normalize them prior to analysis. The package MuMIn  [77]  was used to fit all combinations of models from a global model. The best fit model was chosen based on minimizing the Akaike Information Criterion with a correction for the small sample size (AICc). The diagnostics of model fit were examined for each model and satisfied, except for the linear model to test the effects of treatment on concentrations of OsHV-1 in tissues.
Differences among treatments and families for tissue concentrations were thus tested individually with a Kruskal-Wallis test (p < 0. 05) followed by a multiple comparison using the Wilcoxon Rank Sum Test with a "holm" probability adjustment  [78]. Correlations were run between survival data in the present study and the corresponding oyster families exposed to the FRA µvar via bath exposure and OsHV-1 TB in the field by  Divilov et al. [47]. Correlations were run with the cor. test function using the Spearman method due to non-normality in the data. All data and statistical code are available on figshare  [79].
, 2014; Martinez-Urtaza et al. , 2004). Reptiles are often infected by Salmonella  (Corrente et al. , 2017; Kanagarajah et al. , 2018; Mukherjee et al. , 2019; Pulford et al. , 2019) , and S. enterica strains can invade plant cells, and survive in soil  (Dyda et al. , 2020; Jechalke et al. , 2019; Schikora et al. , 2012). The degree of overlap between bacterial populations from those sources and those that infect humans and animals has not yet been adequately addressed. These uncertainties raise the following specific questions. Does the natural diversity and broad population structure of S. enterica differ between continents, or by source? Are S. enterica populations uniform across smaller geographic entities with multiple legal entities but continuous contact, such as the island of Ireland? Do isolates from water and reptiles cause gastroenteritis in humans? A broad sampling of Salmonella from diverse geographical sources and multiple hosts is needed to answer these questions, and to counteract the current extreme bias in the public databases of Salmonella genomes.
EnteroBase contains >275,000 Salmonella genomes, but most of them are from common serovars infecting humans in the US and the UK. The 10K genomes project has added numerous additional details to the global genetic and genomic diversity of Salmonella. In turn, that additional diversity warrants an extensive investigation of the entire dataset. However, such an ambitious project would exceed the capabilities of a small group of scientists, including the authors of this report on their own. We therefore heartily invite the entire global Salmonella community to join in this investigation.
Methods Bacterial isolates. S. enterica isolates from multiple sources were collected at University College Cork by MA from 2008-2012, and their metadata were stored in a BioNumerics (Biomerieux) database. The metadata included country, year, and source of isolation, but none of the details that might allow identification of individual farms or people from whom they were isolated. No ethical permissions are required for transfer of such bacterial samples. Microbiological cultivation was performed as described in detail elsewhere  (O'Farrell et al. , 2012). Isolated single bacterial colonies were used to inoculate 1. 4 ml growth/freezing medium in 2-D bar-coded, screw-capped FluidX tubes  (O'Farrell et al. , 2012)  whose physical locations were stored in an ItemTracker database. These tubes were grown overnight with shaking at 37°C, and stored at -80°C.
All subsequent operations were performed with automated microbiology as described  (O'Farrell et al. , 2012). Cross-contamination from other tubes with these automated methods is not detectable in the sub-cultures, but can occur at a frequency of 1/500 in the parental tubes. Therefore, whenever the stock tubes were used for DNA isolation of a particular isolate, the most recently frozen serial sub-culture was used to inoculate one new subculture for freezing and storage as well as a second subculture for DNA isolation. DNA was isolated from many of these strains, and subjected to classical 7-gene multilocus sequence typing (MLST)  (Achtman et al. , 2012; Achtman et al. , 2013; O'Farrell et al. , 2012). The strain collection, robotic equipment and databases accompanied MA to the University of Warwick in 2013, where the same procedures were implemented, except that DNA isolation was performed with a Qiagen QiaCube.