dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, in agreement with the correspondence between clustering and regions or countries of origin of the germplasm, the geographical proximity appears to align with the patterning observed in the admixed accessions. In cases demonstrating the presence of a significant hierarchical population structure as this study suggests, this method preferentially detects the uppermost level of structure  [26, [48] [49] [50]. As a consequence, when large datasets in species with a complex background are analyzed, it is possible for an underlying substructure to remain undetected within the major divisions of the germplasm. In this context, the "nested (or two-steps) Structure" clustering method has been shown to be an efficient tool to delineate further levels of substructure in both apple and other plant species  [10, 24, 26, [49] [50] [51] [52].
Relatedness and family relationships The previously reported parentage of 10 recent cultivars was correctly inferred in all cases. These results served as a control and validated the parentage assignment obtained with the CERVUS software  [53]  indicating that the number and informativeness of SSR markers were sufficient at least for these cultivars. The 16 SSR markers were nevertheless limited in their ability to infer parentages, and additional cases might have been detected with a larger number of SSR markers. In a recent paper  [54] , it was suggested that the number of 27 SSR loci used in that study was a minimum to be utilized for full parentage reconstruction. Basically, the LOD score tests used in the CERVUS software are computed according to the SSR allelic frequencies, and thus, parentages involving common alleles are more difficult to detect.
By contrast, parentages involving low frequency and rare alleles are more easily detected. On that basis, it is worthy to note that the more frequently detected parents (i. e. , 'Reine des Reinettes' = 'King of the Pippins' , and 'Transparente de Croncels') are possibly representing a biased view of the frequently involved parents, as they most probably carry rare or low frequency alleles in at least some SSR loci. Putative parents present in the dataset but carrying more common alleles may have been hidden because of the statistical limits of their detection with 16 SSR markers. A similar situation was observed by  [23]  with the frequent appearance of 'Reine des Reinettes' as a parent of four old cultivars out of 28, using 21 SSR markers. In the near future, medium and high density SNP arrays  [55] [56] [57]  will provide much more power to infer parentages.
), and Kyrgyzstan (KNAU, Kyrgyz National Agrarian University, 12 acc. ). In all countries, the accessions were mostly chosen as old local/national dessert cultivars (registered or at least known before 1950), but 12 standard dessert cultivars were also included to strengthen comparisons between collections, namely 'Golden Delicious' , 'Red Delicious' , 'McIntosh' , 'Rome Beauty' , 'Granny Smith' , 'Jonathan' , 'Winter Banana' , 'Ingrid Marie' , ' Ananas Reinette' , 'Reinette de Champagne' , 'Discovery' and ' Alkmene'. Moreover, 40 old cider apple cultivars and 40 recently-bred dessert cultivars were sampled in the INRA collection in order to investigate particular patterns. Altogether, 2,446 accessions were thus considered (Additional file 1). Available collections were somewhat heterogeneous in nature as some of them corresponded to already established core collections (INRA and UNIBO) whereas others were selected for the present study thanks to available SSR marker data (UK-NFC and FRUCTUS, see below), or were chosen as a subset of mainly local cultivars (CRA-W, RBIPH, SLU, MTT, UPNA, UDL, EEAD, NCRRIHV, VNIISPK and KNAU).
Fully new SSR datasets were generated for collections from CRA-W, RBIPH, SLU, MTT, NCRRIHV, VNIISPK, and KNAU. Forward primers were labeled with four different fluorescent dyes (6-FAM, VIC, NED, or PET) in order to be combined into four different multiplexed reactions (Additional file 3). Polymerase chain reactions (PCR) for the four multiplex PCRs were performed in a final volume of 11 μL using 10 ng of DNA template, 0. 18 μM of each primer (with the exception of some markers as described in Additional file 3), and 1× PCR Master mix of QIAGEN kit multiplex PCR (Qiagen, Hilden, Germany). PCR cycling conditions were as follows: pre-incubation for 15 min at 94°C, followed by 4 cycles using a touchdown amplification program with an annealing temperature reduced by 1°C per cycle from 60°C to 55°C, followed by 34 cycles, each consisting of 30 s denaturing at 94°C, 90 s annealing at 55°C, and 60 s elongation at 72°C, the last cycle ending with a final 15-min extension at 72°C.
SSR amplification products were analyzed with an ABI3730 XL sequencing system (Applied Biosystems, Foster City, CA, USA). Fragment analysis and sizing were carried out using GeneMapper v. 4. 0 software (Applied Biosystems, Foster City, CA, USA); chromatograms were independently read by two operators. When SSR marker data were already available and obtained at different sites, SSR allele sizes were carefully adjusted between collections, both by use of reference accessions known to be in common between collections and by re-genotyping a subset of each collection with the full set of 16 SSR markers to confirm the allele adjustment.
Diversity assessments The multilocus SSR profiles were compared pairwise in order to establish the genetic uniqueness of each accession. Accessions were considered as duplicates if they had identical SSR fingerprints, or if they had one allelic difference for a maximum of two SSR loci thus making room for some genotyping errors and/or spontaneous SSR mutations. On this basis, redundant profiles were removed from the dataset to avoid bias in genetic analyses and duplicate groups were labeled with unique group ID codes (FBUNQ codes). An accession was declared as a putative triploid when at least three of the 16 SSR loci exhibited three distinct alleles. Analyses of descriptive diversity statistics were conducted at locus level. For each SSR marker, SPAGeDi v. 1. 3 software  [65]  was used to estimate the number of alleles (N A ), the number of alleles with a frequency below 5 % (N B ), the number of effective alleles (N E ), and the observed (Ho) and expected (He) heterozygosity.
Determination of the geographical regions of origin of the unique genotypes Using passport data along with reviewing published records with a focus on old literature (national compilations/varietal catalogues/reports) and specialized websites we were able to discern the geographical regions of origin for a large part of the unique genotypes analyzed. This was further helped by the resolution of identified duplicates and comparison of accessions against additional SSR data of the whole UK-NFC apple collection kindly made available from the UK-NFC database  [64]  and of the whole FRUCTUS collection kindly made available by Agroscope  [14]. We first decided to define three broad historical European regions of origin of the germplasm according to geographical proximity and traditional agricultural relations between them: North + East (Sweden, Norway, Finland, Denmark, Baltic countries, plus Russia, Ukraine and Kyrgyzstan), West (Ireland, United Kingdom, France, Belgium, the Netherlands, Switzerland, Germany, Czech Republic) and South (Spain and Italy).
Analysis of the genetic structure The software Structure v. 2. 3. 4  [67]  was used to estimate the number of hypothetical subpopulations (K) and to quantify the proportion of ancestry of each genotype to the inferred subpopulations. No prior information about the geographical origin of the accessions was considered in the analysis. Ten independent runs were carried out for K values ranging from two to 10 using 500,000 Markov Chain Monte Carlo (MCMC) iterations after a burn-in of 200,000 steps assuming an admixture model and allelic frequencies correlated. In order to assess the best K value supported for our dataset, the ΔK method  [68]  was used through the Structure harvester v. 0. 6. 93 website  [69]  to examine the rate of change in successive posterior probabilities over the range of K values.
Parentage reconstruction and relatedness between the accessions On the basis of the SSR profiles of the unique genotypes, accessions were analyzed to infer possible parent-offspring relationships using Cervus v. 3. 0 software  [53]. In order to reveal only robust parentages, we limited the study to the inferences of 'two-parents offspring' relationships and did not consider inferences of 'one-parent offspring' relationships where the lacking parent offers more flexibility but more speculative assignments as well, especially with only 16 SSR markers. Two criteria were considered to establish strict parentage relationships: i) a confidence level of the LOD score and ii) the Delta LOD value (defined as the difference in LOD scores between the first and second most likely two-candidate parents inferred) both higher than 95 %.
Finally, an additional constraint was added to strengthen the results by limiting the maximum number of tolerated locus mismatches to only one in any inferred twoparents offspring trio, assuming that such a slight difference may be attributable to possible scoring errors, occurrence of null alleles or occasional mutational events  [54, 78]. MIGALE bioinformatic platform is also greatly acknowledged for having given support to intense data analyses. Natural Resources Institute, Finland, is gratefully acknowledged for providing access to 50 Finnish apple cultivars in their germplasm collections. UDL, University of Lleida, and EEAD, Aula Dei Experimental Station, are gratefully acknowledged for providing access to apple cultivars in their germplasm collections. Acknowledgements to Slepkov S. from Maykop experimental station of Vavilov's N.
[64] parentages of 46 apple cultivars inferred within the set of the 1859 apple unique accessions using 16 SSR markers with their accession codes, accession names (AcceNumber), their duplicate codes according to the SSR profile (FBUNQ) and their putative country of origin (OriginHist) (Continued) based on 11 SSR[64]the accession CZ_GS_0478 'Ušlechtilé žluté' duplicate with 'Golden Noble'.
Acknowledgments The staff at the ANAN genotyping platform of the SFR 149 QUASAV (Angers, France), at the GENTYANE genotyping platform ( INRA, Clermont-Ferrand, France ) and at the  Unité Expérimentale Horticole  ( INRA, Angers, France ) are warmly acknowledged for their help in producing the genotyping data and for maintaining the apple germplasm collection, respectively. The  INRA.
High-throughput sequencing (HTS) approaches require no prior assumptions about the bacterial communities present in samples of diverse nature, including noncultivable bacteria. Such HTS microbial identification approaches are based on the sequencing of all (WGS: whole-genome sequencing) or some (RNAseq or 16S rRNA amplicon sequencing) of the bacterial DNA or RNA in a sample, followed by comparison to a reference sequence database  [4]. HTS has made major contributions to the generation of comprehensive inventories of the bacteria, including pathogens, present in humans  [5]. Such approaches are now being extended to the characterization of bacteria in wildlife  [6] [7] [8] [9] [10] [11] [12] [13]. However, improvements in the estimation of infection risks will require more than just the detection of bacterial pathogens.
Indeed, we will also need to estimate the prevalence of these pathogens by host taxon and/or environmental features, together with coinfection rates  [14, 15]  and pathogen interactions  [16, 17]. Razzauti et al. [8]  recently used 16S rRNA amplicon sequencing with the dual-index sequencing strategy of Kozich et al. [18]  to detect bacterial pathogens in very large numbers of rodent samples (up to several hundred samples in a single run) on the Illumina MiSeq sequencing platform. The 16S rRNA amplicon sequencing technique is based on the amplification of small fragments of one or two hypervariable regions of the 16S rRNA gene. The sequences of these fragments are then obtained and compared with reference sequences in curated databases for taxonomic identification  [4, 19]. Multiplexed approaches of this kind include short indices (or tags) linked to the PCR products and specific to a given sample.
This makes it possible to assign the sequences generated by the HTS run to a particular sample following bioinformatic analysis of the dataset  [18]. Razzauti et al. [8]  demonstrated the considerable potential of this approach for determining the prevalence of bacteria within populations and for analyzing bacterial interactions within hosts and vectors, based on the accurate characterization of bacterial diversity within each individual samples it provides. However, various sources of error during the generation and processing of HTS data  [20]  may make it difficult to determine which samples are really positive or negative for a given bacterium. The detection of one or a few sequences assigned to a given taxon in a sample does not necessarily mean that the bacterium is actually present in that sample.
We used these bias estimates to control and filter out potential false-positive and false-negative samples during screening for bacterial pathogens. We applied this strategy to 711 commensal rodents collected from 24 villages in Senegal, Western Africa: 208 Mus musculus domesticus, 189 Rattus rattus, 93 Mastomys natalensis and 221 Mastomys erythroleucus. Pathogenic bacteria associated with the rodents were analysed using a protocol based on Illumina MiSeq sequencing of the V4 hypervariable region of the 16S rRNA gene  [18]. We considered the common pitfalls listed in Table  1  during the various stages of the experiment (see details in the workflow procedure, Figure  1 ). Biases in assessments of the presence or absence of bacteria in rodents were estimated directly from the dataset, by including and analysing negative controls (NC) and positive controls (PC) at various stages of the experiment (see Box 1), and systematically using sample replicates.
Removal of low-quality reads Errors during processing (sequence trimming, alignment)  [18, 37, 38]  False-positive and negative samples Use of standardized protocols and reproducible workflows Incomplete reference sequence databases  [39]  False-negative samples Selection of an appropriate database for the selected target region and testing of the database for bacteria of particular interest Error of taxonomic classification  [40]  False-positive samples Positive controls for PCRs (extraction from infected tissue and/or bacterial isolates and/or mock communities) Checking of taxonomic assignments by other methods (e. g. , Blast analyses on different databases).
Results & Discussion Raw sequencing results. S1  and complete information by sample and run in Table  S2 ). Overall, 99% of PCRs generated more than 3,000 raw reads (mean: 11,908 reads; standard deviation: 6,062). The raw sequence files are available in FASTQ format in the Dryad Digital Repository http://dx. doi. org/10. 5061/dryad. m3p7d [42]. Using mothur v1. 34  [43]  and the MiSeq standard operating procedure (http://www. mothur. org/wiki/MiSeq_SOP), we removed 20. 1% of paired-end reads because they were misassembled, 1. 5% of sequences because they were misaligned, 2. 6% because they were chimeric and 0. 2% because they were non- Recent research has highlighted different biases occurring at different steps of high-throughput sequencing. These biases can be estimated directly from the data by including several controls together with samples in the experiment.
Box 1. Guideline for experimental controls to include within high-throughput amplicon sequencing experiments to mi;gate false posi;ve results sequences) of the total sequence dataset (Table  S3 ). The accuracy of taxonomic assignment (to genus level) was assessed with positive controls for PCR, corresponding to DNA extracts from laboratory isolates of Bartonella taylorii, Borrelia burgdorferi and Mycoplasma mycoides (PC Bartonella_t , PC Borrelia_b and PC Mycoplasma_m , respectively), which were correctly assigned to a single OTU corresponding to the appropriate reference sequences (Table  2 ). Note that the sequences of PC Mycoplasma_m were assigned to Entomoplasmataceae rather than Mycoplasmataceae because of a frequent taxonomic error reflected in most databases, including Silva  [45]. This problem might also affect other taxa.
We therefore recommend systematically carrying out a blast analysis against the sequences of taxa of interest in GenBank to confirm the taxonomic assignment obtained with the 16S databases. Finally, we assumed that the small number of sequences per sample might limit the completeness of bacterial detection  [36]. For this reason, we discarded seven rodent samples (2 M. erythroleucus and 5 M. domesticus) yielding fewer than 500 sequences for at least one of the two PCR replicates (1% of the samples). on the maximum number of sequences observed in a negative or positive control for a particular OTU in each run **: Threshold TFA is based to the false assignment rate (0. 02%) weighted by the total number of sequences of each OTU in each run ***: Mycoplasma mycoides and Borrelia burgdorferi bacterial isolates added as positive controls for PCR and indexing (i.
These contaminants accounted for 29% of the sequences in the dataset (Figure  2 ). They also differed between MiSeq runs: Pseudomonas, Pelomonas and Herbaspirillum predominated in run 1, whereas Brevibacterium, Brachybacterium and Dietzia predominated in run 2 (Table  S4 , Figure  S1 ). This difference probably reflects the  These contaminant genera are also present (to a lesser extent) in the rodent samples. The inserts represent the proportion of sequences from rodent samples, which were incorrectly assigned to the controls. See Figure  S1  for separate histograms for both MiSeq runs.
Pathogenic genera Contaminant genera 0% 0,3% 0,2% 0,1% Genera identified as contaminants were then simply removed from the sample dataset. It is important to note, however, that the exclusion of these results does not rule out the possibility that our samples contained true rodent infections (at least for some of them like Streptococcus which contains both saprophytic and pathogenic species). However, as mentioned by Razzauti et al. [8]  distinguishing between those two possibilities seems difficult, if not impossible. Faced with this lack of certainty, it is most prudent to simply remove these taxa from the sample dataset. These results highlight the importance of carrying out systematic negative controls to filter the taxa concerned in order to prevent inappropriate data interpretation, particularly for the Streptococcus genus, which contains a number of important pathogenic species.
Filtering for false-positive results. Mothur analysis produced a table of abundance, giving the number of sequences for each OTU in each PCR product (data available in the Dryad Digital Repository http://dx. doi. org/10. 5061/dryad. m3p7d [42]. The multiple biases during experimental steps and data processing listed in Table  1  made it impossible to infer prevalence and co-occurrence directly from the table of sequence presence/absence in the PCR products. We suggest filtering the data with estimates of the different biases calculated from the multiple controls introduced during the process. This strategy involves calculating sequence number thresholds from our bias estimates. Two different thresholds were set for each of the 12 OTUs and two MiSeq runs. We then discarded positive results associated with sequence counts below the threshold (Figure  1 ).
[75]  for the Bartonella genus, in parallel with the 16S rRNA-V4 region. This strategy could also easily be adapted for other microbes, such as protists, fungi and even viruses, provided that universal primers are available for their detection (see  [76, 77]  for protists and fungi, and  [78]  for degenerate virus family-level primers for viruses). Finally, our filtering method could also be translated to any other postsequencing dataset of indexed or tagged amplicons in the framework of environmental studies (e. g. metabarcoding for diet analysis and biodiversity monitoring  [79] , the detection of rare somatic mutations  [80]  or the genotyping of highly polymorphic genes (e. g. MHC or HLA typing,  [81, 82] ).
The information concerning the rodent collection (sample ID, locality and species) is provided in the Table  S2. Cross-contamination during dissection was prevented by washing the tools used successively in bleach, water and alcohol between rodents. We used the spleen for bacterial detection, because this organ is a crucial site of early exposure to bacteria  [90]. Spleens were placed in RNAlater (Sigma) and stored at 4°C for 24 hours and then at -20°C until their use for genetic analyses. Target DNA region and primer design. We used primers with sequences slightly modified from those of the universal primers of Kozich et al. [18]  to amplify a 251-bp portion of the V4 region of the 16S rRNA gene (16S-V4F: GTGCCAGCMGCCGCGGTAA; 16S-V4R: GGACTACHVGGGTWTCTAATCC). The ability of these primers to hybridize to the DNA of bacterial zoonotic pathogens was assessed by checking that there were low numbers of mismatched bases over an alignment of 41,113 sequences from 79 zoonotic genera inventoried by Taylor et al  [1] , extracted from the Silva SSU database v119  [44].
The FASTA file is available in the Dryad Digital Repository http://dx. doi. org/10. 5061/dryad. m3p7d [42]. We used a slightly modified version of the dual-index method of Kozich et al. [18]  to multiplex our samples. The V4 primers included different 8-bp indices (called i5 index in the forward and i7 index in the reverse) and Illumina adapters (called P5 adapter in the forward and P7 adapter in the reverse) in the 5' position. The combinations of 24 i5-indexed primers and 36 i7-indexed primers made it possible to identify 864 different PCR products loaded onto the same MiSeq flowcell. Each index sequence differed from the others by at least two nucleotides, and each nucleotide position in the sets of indices contained approximately 25% of each base, to prevent problems due to Illumina low-diversity libraries (Table  1 ).
S1  and details by sample Table  S2 ). Bioinformatic and taxonomic classification. MiSeq datasets were processed with mothur v1. 34  [43]  and with the MiSeq standard operating procedure (SOP)  [18]. Briefly, the MiSeq SOP (http://www. mothur. org/wiki/MiSeq_SOP) allowed us to: 1) construct contigs of paired-end read 1 and read 2 using the make. contig command; 2) remove the reads with poor quality of assembly (> 275 bp); 3) align unique sequences on the SILVA SSU Reference alignment v119  [44] ; 4) remove the misaligned, non-specific (eukaryotic) and chimeric reads (uchime program); 5) regroup the reads into Operational Taxonomic Units (OTUs) with a 3% divergence threshold; and 6) classify the OTUs using the Bayesian classifier included in mothur (bootstrap cutoff = 80%) and the Silva taxonomic file.
At the end of the process, we obtained a table giving the number of reads for each OTU in line and each PCR product in column. For each OTU, the taxonomic classification (up to genus level) was provided. The abundance table generated by mothur for each PCR product and each OTU was filtered as described in the Results section. The most abundant sequence for each OTU in each sample was extracted from the sequence dataset with a custom-written Perl script (available in the Dryad Digital Repository http://dx. doi. org/10. 5061/dryad. m3p7d [42] ). The most abundant sequences for the 12 OTUs are available from GenBank (Accession Number KU697337 to KU697350).