dataesr/scientific-paragraphs-categorization huggingface dataset:
Lastly, for each individual i, an estimate of marker data point x ij was done using the weighted average of the k markers that were closest to the nonmissing values at the i th individual. The weight of each SNP marker was computed as 1 / d 2 , where d refers to the Euclidean distance between the marker to be weighted and marker j. Imputation with kNNI was performed using the function raw. data in the R package snpReady  [50]. We used a sweep. sample threshold of 0. 6, a call rate of 0. 4 and a MAF threshold of 0. 15 to avoid losing any data.
Expectation maximization imputation (EMI) We included EMI in this study since it was designed for use with GBS markers and gave the highest GS accuracy for fusarium head blight incidence and kernel quality index among five map-independent imputation methods in wheat  [51]. The multivariate normal -expectation maximization (EM) algorithm  [24]  is a general approach for computing maximum likelihood estimates of unknown parameters when there is missing data. Imputation of missing data points using the EM algorithm involves two steps which are the expectation step and the maximization step. The EM imputation algorithm is based on the multivariate normal distribution and it imputes missing markers based on realized relationship averaged over all the markers. We implemented the EMI using the R package rrBLUP  [52].
Probabilistic PCA imputation (PPCAI) PPCAI was chosen for this study because it gave the best imputation accuracy on a comparison involving three map-independent imputation methods with GBS data from maize and wheat  [54]. PPCAI  [27]  is a PCA based imputation approach. Missing data-points were first set to marker averages, and singular value decomposition (SVD) was then used to construct orthogonal principal components. The algorithm finishes by using the principal components, which correspond to the largest eigenvalues, to infer the missing genotypes in the SNP matrix. PPCAI was implemented using the R package pcaMethods  [27]. We used the function KEstimate to identify the optimal number of principal components, which was chosen as the one that gave the highest prediction accuracy.
Singular value decomposition imputation (SVDI) SVDI was chosen among map-independent imputation methods as it gave the best prediction accuracy for fusarium damaged kernels in wheat with GBS data  [51]. For the SVDI  [25]  algorithm, singular value decomposition of marker matrix M was used to obtain the k most significant eigengenes (different numbers of principal components), i. e. with the highest eigengene value. Missing value j was then estimated in SNP i by first regressing SNP i against the k eigengenes. The regression coefficients were then used to reconstruct missing value j using a linear combination of k eigengenes. SVDI was implemented using the R package pcaMethods  [27].
FImpute 2. 2 We included FImpute version 2. 2  [29]  in the comparison of imputation methods as it outperformed Beagle imputation with SNP array data in beef cattle  [55]. In addition, FImpute gave a similar accuracy as Beagle 4. 0 on GBS data in dairy cows  [56]. FImpute makes use of pedigree information to impute missing genotypes and if pedigree information is not available, the algorithm uses population information to construct haplotypes using an overlapping sliding window approach. Like Beagle, FImpute requires SNP physical positions expressed in base pairs and assumes that 1 cM corresponds to 1 Mbp. Consequently, we multiplied the genetic positions by 1000,000 to convert them into physical positions. We used the default settings of FImpute (shrink factor = 0. 15 and overlap = 0. 65 of sliding windows) for this study and for both dataset 1 and dataset 2.
Impact of marker density on prediction accuracy The effect of marker density on GS accuracy was assessed using dataset 1 by making genomic predictions using marker subsets of varying sizes from the mapped and non-mapped markers. From the nonmapped marker data set, marker subsets of 25, 250, 500, 1000, 1769 and N. Munyengwa et al. 3420 SNPs were created, and from the mapped marker dataset, marker subsets of 25, 250, 500, 1000 and 1769 SNPs were created. Marker subsets were made by random sampling, with 45 replicates by level of SNP number.
Model for genomic predictions A purely additive GS model and the random regression best linear unbiased prediction (rr-BLUP) method  [1]  were used to obtain the genomic estimated genetic values (GEGVs) of the validation rubber clones. The additive model was chosen as Cros et al. [21]  did not find any difference in accuracy when using this approach and other approaches modeling non-additive effects in dataset 1. Marker effects were estimated using the following linear mixed model: y = Xβ + Zm + 8 Where y is the vector of clone genetic values (i. e. adjusted phenotypic values), β is the vector of fixed effects (the mean phenotype) with incidence matrix X, m is the vector of the random marker effects with incidence matrix Z, i. e. the matrix of genotypes, and ԑ is the vector of residual effects.
Validation approaches for genomic predictions 2. 8. 1. Across site validation within dataset 1 Across-site genomic predictions were performed with dataset 1, leading to two different validation scenarios (Site 1 towards Site 2 and Site 2 towards Site 1). In the first scenario, 179 individual clones from Site 1 were used to train the GS model to predict the GEGVs of the 123 clones in Site 2, that were used as the validation population. In the second scenario, the 125 individual clones in Site 2 were used to train the GS model and the 177 clones in Site 1 were used for the validation. The two common clones in the two sites were only used to train the GS model and excluded from the validation sets.
Prediction accuracy Prediction accuracy was obtained for each validation set as the Pearson correlation between the GEGV (ĝ) and the genetic value (y) of the clones composing the set. The GS accuracy was calculated by dividing the prediction accuracy by the square root of clonal mean heritability (H 2 ). For dataset 2, accuracies of the five cross-validations were averaged to give aggregated accuracies. Pairwise comparisons of prediction accuracies among imputation methods were made for each trait using the Hotelling-Williams t-test  [59]. This test compares two dependent Pearson correlation coefficients, i. e. with one variable in common (which, in our case, is the genetic value of clones used for validation). The test was implemented using the function r. test in the R package psych  [60].
Percentage of correctly imputed genotypes Among the genotypes (i. e. datapoints) with high read depth, some genotypes were set as missing (validation genotypes), imputed with the different methods and used to compute the percentage of correct imputations. This was done in dataset 1 and dataset 2 considering the mapped SNPs, in order to compare the nine imputation methods. There were 17,503 and 6291 validation genotypes in dataset 1 and in dataset 2, respectively, defined as genotypes with read depth ≥ 250 and ≤ 550 in dataset 1 and with read depth ≥ 105 and ≤ 125 in dataset 2. In each dataset, three replicates were defined by setting as missing one third of the validation genotypes, with the validation genotypes set as missing in a given replicate chosen randomly. For LinkImputeR, as this software makes use of the read depth information, the actual read depth of the validation genotypes was replaced by read depth randomly sampled among the read depths observed in the genotypes that were originally missing.
Effect of imputation method on percentage of correctly imputed genotypes Large differences among imputation methods were found in terms of percentage of correctly imputed genotypes (Fig. 4 ). LinkImputeR gave the best results, with 92% and 97% of correct imputations in datasets 1 and 2, respectively. It was significantly better than all the other methods, and was followed in terms of imputation accuracy by FImpute using pedigree information (88% on average over the two datasets) and Beagle (86%). MNI was the worst method in both datasets, with a mean percentage of correctly imputed genotypes of 42%.
Effect of imputation method on GS accuracy 3. 3. 1. Mapped SNPs When considering the subsets of mapped SNPs (i. e. 1769 SNPs on dataset 1 and 3111 on dataset 2), LinkImputeR, Beagle and FImpute without pedigree information were the best imputation methods in terms of GS accuracy, on average over the three traits, although there were variations according to trait (Table  3 ). Thus, for genomic predictions made for site 1 of dataset 1, considering rubber yield, the GS accuracy varied from 0. 53 for LinkImputeR, Beagle and SVDI to 0. 48 for MNI, corresponding to a 10. 8% increase between the worst and the best methods (Table  3 ). For rubber yield in site 2, LinkImputeR was the best imputation method with a GS accuracy of 0. 5, closely followed by Beagle and FImpute without pedigree information (0.
49). The method giving the smallest GS accuracy was again MNI (0. 45), corresponding to an increase between the worst and the best methods similar to what was found in site 1 (+11. 1%). For sucrose content, kNNI (0. 16) was the best imputation method for site 1, closely followed by Beagle, LinkImputeR and FImpute without pedigree information (0. 15 for the three of them). The lowest GS accuracy was recorded with MNI and PPCAI, both of them with a GS accuracy of 0. 13. For site 2, FImpute without pedigree information gave the highest GS accuracy (0. 25) followed by Beagle (0. 24), FImpute with pedigree information (0. 23), LinkImputeR and SVDI (0. 22). The worst method was MNI (0. 17), making GS accuracies obtained with FImpute without pedigree information 47. 1% higher. When we performed cross-validations for GS prediction of circumference within dataset 2, LinkImputeR was the imputation method which led to the highest GS accuracy (0.
All SNPs When considering all the SNPs, with or without known genetic positions (i. e. 3420 SNPs on dataset 1 and 9088 on dataset 2), LinkImputeR was on average the best imputation method, although there were again variations according to trait (Table  3 ). When making GS predictions for rubber production, LinkImputeR gave the highest GS accuracy in the two sites of dataset 1, reaching 0. 53 in site 1 (10. 4% higher than the worst method, MNI) and 0. 5 in site 2 (13. 6% higher than the worst method, MNI). For sucrose content, kNNI (0. 16) was the best imputation method in site 1 followed by EMI (0. 15), PPCAI (0. 15), SVDI (0. 15), LinkImputeR (0. 14), RFI (0. 14) and lastly MNI (0. 14). In site 2, PPCAI and SVDI were equally the best imputation methods (0. 19) followed by RFI (0. 18), EMI (0.
18), LinkImputeR (0. 17), MNI (0. 16) and lastly kNNI (0. 15). For circumference in dataset 2, the highest GS accuracy was obtained using LinkImputeR (0. 22) followed by kNNI and MNI (GS accuracy of 0. 2 for both of them). As with markers from the genetic map, the lowest GS accuracies were obtained after using markers imputed with RFI, EMI, PPCAI and SVDI. Over these cases, LinkImputeR was the most efficient method for GS predictions, as it had the best average rank and mean GS accuracy (0. 31, against 0. 28 to 0. 30 for the other methods). However, many differences were not significant (see Supplementary Table  S.
Impact of marker density on GS accuracy Fig. 5  and Fig. 6  summarize the effect of marker density on GS accuracy in dataset 1 for the two traits under study (rubber yield and sucrose content). When marker density became higher, there was for the two traits a strong increase in GS accuracy, before a plateau was reached at around 1000 markers with markers from the genetic map and around 1600 markers with unordered markers. Only marginal increases in GS accuracy were observed after the plateau was reached. Rubber yield showed a more pronounced increase in GS accuracy with marker density as compared to sucrose content. Also, from the results of the map-independent imputation methods, we can see in Fig. 5  and Fig. 6  that using only the markers with known position on the genetic maps increased GS accuracies compared to when using all the markers.
Overall, LinkImputeR was the best imputation method because of its consistent performance over the traits and SNP datasets (i. e. the subsets of SNPs located on the genetic maps and all the markers), and its high percentage of correctly imputed genotypes. LinkImputeR has a unique Fig. 5. Effect of imputation approach and marker density on GS accuracy in site 1 (left) and site 2 (right) (dataset 1) for rubber yield. When not all markers were used, values are means over 45 replicates. FIPed/FIPnoP: FImpute using / not using pedigree information. feature compared to the other imputation approaches presented here, i. e. the ability to define a subset of SNPs imputed optimally: the user specifies a range of values that will be used as filters for different parameters, in particular minimum minor allele frequency, maximum percentage of missing genotype per SNP and sample, and minimum read depth per genotype, and LinkImputeR measures the imputation accuracy for every combination of the different filters.
This allows the users to easily identify the optimal quality filters to apply to their SNP dataset to achieve the best imputation. To better understand the performances of LinkImputeR, we computed the percentage of correctly imputed genotypes when no filtering was made by the software, i. e. forcing it to keep all the SNPs. The mean percentage of correctly imputed genotypes over the two datasets remained high (0. 86), but was slightly below FImpute with pedigree information (0. 88) and identical to Beagle. LinkImputeR therefore performed best as a result of its high imputation accuracy and its ability to define a subset of SNPs imputed optimally. This also suggests that FImpute and Beagle could perform as well as, or even outperform, LinkImputeR, if their use was preceded by a preliminary analysis to identify quality filters to apply to the SNP dataset.
This should be further investigated. In addition, a possible reason for the consistent high performance of LinkImputeR, in particular compared to the other map-independent imputation methods, is that it makes use of LD between markers, which is a key information regarding the correspondence between the genotypes at two different SNPs. For future studies, it would be interesting to evaluate the performances of Link-ImputeR in populations with a higher degree of complexity, and therefore a contrasting pattern of LD compared to the biparental populations used here. The differences among methods in terms of GS accuracy were relatively small, with a coefficient of variation of 3%, on average over combinations of traits, sites and SNP datasets, while the percentage of correctly imputed genotypes varied more widely among methods, with an average coefficient of variation of 22%.
For FImpute, it was better to use pedigree information in terms of imputation accuracy but better not to use it in terms of GS accuracy. Thus, with both datasets, FImpute imputed significantly better when using pedigree information. By contrast, for GS accuracy, FImpute without pedigree information consistently gave higher GS accuracies for the two traits (yield and sucrose) and across the two sites as compared to FImpute with pedigree information, except for yield in site 1 and trunk circumference where the two imputation methods gave the same GS accuracy. This discrepancy remains unclear, in particular as the legitimacy of the clones in dataset 1 was controlled using SSRs (not shown), and because the SNPs with a segregation pattern that significantly differed from the segregation expected from the parental genotypes were discarded.
Effect of marker type on GS accuracy The results obtained in this study confirm that GBS is an efficient genotyping method for the application of GS in rubber breeding, as indicated by the results obtained by Souza et al. [20]. Indeed, for rubber Fig. 6. Effect of imputation approach and marker density on GS accuracy in site 1 (left) and site 2 (right) (dataset 1) for sucrose content. When not all markers were used, values are means over 45 replicates. FIPed/FIPnoP: FImpute using / not using pedigree information. yield, the mean between site GS accuracy that we obtained for rubber yield with dataset 1 using markers imputed with LinkImputeR was 0. 52, which is, for the cross considered here, sufficient to increase the rate of genetic progress by the inclusion of a stage of genomic selection before the conventional small scale clonal trials in Cros et al.
[21]. In addition, this value is similar to the GS accuracy of Cros et al. [21] , where a mean between site GS accuracy of 0. 53 was obtained with the same dataset genotyped with 332 SSRs (instead of GBS), and using a larger number of clones (330 against 302 here, due to technical difficulties with the GBS analysis). We can even assume that, if data had been available here for the same number of clones as in Cros et al. [21] , higher GS accuracies could have been achieved, as GS accuracy increases with the size of the training population  [62]. It shows that SNPs from GBS, despite the fact that they have a higher percentage of missing data and higher error rate than SSRs, can be used for GS predictions. This opens the way to the practical application of GS, as Cros et al. [21]  showed that, to increase the rate of genetic gain, GS required to be applied on a large number of selection candidates (>1000), which would not be cost-effective with SSRs but is with GBS.
High-density rubber genetic linkage maps We obtained, for dataset 1 and 2, respectively, genetic linkage maps that contained 1769 and 3111 SNP markers located at non-redundant positions and spread across  19  LGs. The reason for an extra LG could be due to a large interval without a marker, making the SNP alleles on the two chromosome segments appear as statistically unrelated. This could also be because of a recombination hotspot for which it is difficult to prove the genetic proximity between physically close markers. The two genetic maps spanned a total length of 2600. 9 cM and 2215. 1 cM, respectively, which is comparable with the lengths of previously published maps in rubber (2144 cM in Lespinasse et al. [43] , 2041 cM in Pootakham et al. [44]  and 2441 cM in Le Guen et al. [63] ). The two GBS-based linkage maps, with a marker density of one SNP in every 1.
47 cM and 0. 71 cM in dataset 1 and 2, respectively, showed a much denser genome coverage as compared to previously published microsatellite-based linkage maps of Hevea (one marker in every 3. 5 cM in Conson et al. [36] , in every 8 cM in Le Guen et al. [63] , in every 10 cM in Souza et al. [37] ). Their marker density is comparable to what was achieved with GBS in the composite map obtained by Pootakham et al. [44] , where SNP density was one SNP in every 0. 89 cM.
Acknowledgments We thank the  Institut Francais du Caoutchouc (IFC) , and the companies Michelin, SIPH and  SOCFIN  for financial support and for providing the data used for this research. We also thank the companies SOGB (Société Grand Bereby) and  SAPH (Société Africaine de Plantations d'Hévéa)  for their logistical assistance in the field experiments in Cote d'Ivoire. The authors are grateful to the  Michelin group (Brazil)  for allowing access to phenotypic and molecular data from their breeding trials in Itiquira (Mato Grosso, Brazil). We acknowledge the  GENES program  of the  Intra-Africa Academic Mobility Scheme  of the  European Union  and the  Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP)  for financial support. We thank the UMR AGAP genotyping technology platform (CIRAD, Montpellier) and the  CIRAD-UMR AGAP HPC data center of the South Green bioinformatics platform  (http:// www.
Funding This work was partly funded by the  Institut Francais du Caoutchouc (IFC)  and the companies Michelin, SIPH and SOCFIN in the framework of the  IFC  project " Hevea varietal creation in West-Africa ". NM received a master scholarship from the  GENES program  of the  Intra-Africa Academic Mobility Scheme of the European Union. LS received a postdoctoral fellowship from the  FAPESP  ( 2012/05473-1  and  2012/ 50491-8 ). KeyGene N. V. owns patents and patent applications protecting its Sequence Based Genotyping technologies.
Data availability statement The data from dataset 1 can be found at the following link: https://www. ncbi. nlm. nih. gov/sra/PRJNA645262 Dataset 2 is publicly available and can be found at the following links: https://www. ncbi. nlm. nih. gov/bioproject/PRJNA540286 and. https://www. ncbi. nlm. nih. gov/bioproject/PRJNA541308.
18 0. 18 0. 17 0. 17 0. 18 0. 19 0. 15 0. 19 0. 2 0. 18 All markers (3420): Yield / site 1 0. 53 0. 5 0. 51 0. 5 0. 5 0. 5 0. 48 - - - Yield / site 2 0. 5 0. 45 0. 46 0. 45 0. 45 0. 45 0. 44 - - - Yield (mean) 0. 5 0. 48 0. 48 0. 48 0. 48 0. 47 0. 46 - - - Sucrose / site 1 0. 14 0. 14 0. 15 0. 15 0. 15 0. 16 0. 14 - - - Sucrose / site 2 0. 17 0. 18 0. 18 0. 19 0. 19 0. 15 0. 16 - - - Sucrose (mean) 0. 16 0. 16 0. 17 0. 17 0. 17 0. 16 0. 15 - - - Trait LIR RFI EMI PPCAI SVDI kNNI MNI Beagle FImpute FImpute (pedigree) Dataset 2 Mapped markers (3111): Circumference 0. 21 0. 19 0. 19 0. 19 0. 19 0. 2 0. 2 0. 2 0. 2 0. 2 All markers (9088): Circumference 0. 22 0. 18 0. 18 0. 18 0. 18 0. 2 0. 2 - - -.
Conveniently, we noticed that the different members of the RRNPP group share a common signature of 5 criteria (Fig. 1 ): i) the pro-peptide is a small protein (10-70aa), ii) the pro-peptide is secreted, except in rare exceptions (e. g. Shp and PrgQ  (7) ), via the SEC-translocon and further matured by exopeptidases into a communication peptide, iii) the receptor has a length comprised between 250 and 500aa, iv) the receptor harbors tetratricopeptide repeats (TPR), which are structural motifs involved in the binding of small peptides (in this case, the cognate communication peptide), v) the genes encoding the pro-peptide and the receptors are directly adjacent to each other. Advantageously, a large amount of reference Hidden-Markov-Models (HMMs) from the Cath-Gene3D  (30) , Superfamily  (31) , SMART  (32) , Pfam  (33)  and TIGRFAM  (34)  databases are already available to detect TPRs in protein sequences.
Moreover, a tool called SignalP specifically computes the likelihood that proteins harbor a signal sequence for the SEC-translocon (35) (Fig. 1 ). Consequently, the generic, yet specific signature of RRNPP QSSs is detectable in silico, without requiring homology searches that would limit the output to representatives of already known QSSs. On this basis, we have developed RRNPP_detector, a python software that detects the RRNPP signature in chromosomes and MGEs of gram-positive bacteria. We present its workflow, its usage and showcase practical examples of analyses against the complete genomes of viruses and of Firmicutes available on the NCBI.
These potential receptors next undergo a computational characterization. First, a BLASTp search  (37)  of reference RRNPP receptors (Rap, Rgg, NprR, PlcR, PrgX, TraA, AimR of Bacillus phage phi3T, AimR of Bacillus phage Waukesha, QsrB and AloR13) against these potential receptors are launched to identify the homologs of already known QSSs (by default: Evalue <= 1E-5, identity >= 20%, and alignment coverage >= 60% of the length of both the query and the target sequences). Then, an HMMsearch of DNA-binding domains found in reference receptors (PFAM: PF01381; Gene3D: 1. 10. 260. 40; Superfamily: 47413; SMART: SM00530) is launched against the potential receptors to identify those that may be transcription factors. After this characterization step, the 10-70aa proteins are further reduced to those with a CDS adjacent to the one of a potential receptor.