dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods accounting for phylogenetic uncertainty should be preferred whenever possible because they should provide more reliable parameter estimates and realistic confidence intervals around them. Based on a real comparative dataset, we ran simulations to investigate the effect of variation in the size of the random tree sets downloaded from BirdTree on the variability of parameter estimates from a bivariate relationship between mass-specific productivity and body mass. Irrespective of the method of analysis, using at least 1,000 trees allows obtaining parameter estimates with very small (< 0. 15%) coefficients of variation. We argue that BirdTree, due to the ease of use and the major advantages over previous 'traditional' methods to obtain phylogenetic hypotheses of bird species (e. g. supertrees or manual coding of published phylogenies), will become the standard reference in avian comparative studies for years to come [Current Zoology 61 (6): 959-965, 2015].
As is already occurring with a similar online tool providing primate as well as other mammal phylogenies, the 10kTrees project  (Arnold et al. , 2010 ; see http://10ktrees. fas. harvard. edu), we assume that phylogenetic trees downloaded from BirdTree will be used as a standard reference in avian comparative studies for years to come, and here we discuss methods that might be used to incorporate such trees in phylogenetic analyses of trait (co)evolution, such as phylogenetically-corrected multiple regression models (e. g. phylogenetic generalized least-squares (PGLS) models;  Pagel, 1997; Freckleton et al. , 2002). We then ran simulations, based on a real comparative dataset, to investigate the variability in parameter estimates according to the number of trees downloaded from BirdTree, in order to provide guidance on the number of trees required to obtain robust statistical inferences.
g. Barker et al. , 2004). All existing species were assigned to one of the major clades ('crown clades') of the backbone phylogeny. Genetic data from multiple loci, retrieved from GenBank, were used to reconstruct the phylogeny of crown clades and to combine these with the backbone phylogenies in a Bayesian framework, using a number of additional constraints and informed priors [thoroughly described in the supplemen-tary material of  Jetz et al. (2012) ]. Using BEAST  (Drummond and Rambaut, 2007) ,  Jetz et al. (2012)  obtained two sets of ultrametric, fully resolved (bifurcating) Markov chain Monte Carlo (MCMC) trees of all bird species, either based on the  Hackett et al. (2008)  or the  Ericson et al. (2006)  backbone phylogeny, and made 10,000 post-burn-in trees freely available to download from the BirdTree online platform.
Therefore, methods that incorporate phylogenetic uncertainty in regression models (or in any statistical method relying on a phylogenetic tree) are expected to provide more reliable parameter estimates and standard errors compared to methods ignoring phylogenetic uncertainty, and should be preferred whenever possible (e. g. Grafen, 1989; Martins, 1996; Martins and Hansen, 1997; Huelsenbeck and Rannala, 2003; de Villemereuil et al. , 2012, Garamszegi and Mundry, 2014 ; see also the popular software Bayes-Traits, http://www. evolution. rdg. ac. uk/BayesTraitsV2Beta. html). Particularly, incorporating uncertainties into the phylogenetic analyses puts an emphasis on the confidence interval around parameter estimates, which delineates how much a comparative result can be trusted. To account for phylogenetic uncertainty and to fully exploit the possibilities offered by downloading tree sets from BirdTree to obtain robust comparative inferences, we envisage several possibilities that rely on an entire set of phylogenetic trees instead of a single consensus tree.
Moreover, Bayesian methods, such as the one proposed by  de Villemereuil et al. (2012) , have yet to be tested extensively in a variety of real dataset conditions, including multiple regression settings, and may become computationally problematic when the number of species is very large (> 200)  (de Villemereuil et al. , 2012). Finally, Bayesian approaches may introduce unnecessary variance in posterior estimates whenever no informative priors are provided (i. e. in most cases). Therefore, albeit Bayesian approaches also provide mean parameter estimates and confidence intervals (in this case they are called credible intervals), the latter variance will reflect the effect of both the variance across the considered phylogenetic trees and the use of uninformative priors  (Garamszegi and Mundry, 2014).
How Many Trees? The reliability of phylogenetically-corrected analyses may vary according to the number and specific sets of species (taxonomic bias,  Arnold and Nunn, 2010)  and intra-specific variance in traits  (Garamszegi and Møller, 2010). Morevover, it may vary according to the number of phylogenetic trees used to draw inferences, since fitting models using a variable number of trees that differ to some extent between each other in their phylogenetic relationships among species (either in terms of topology or branch lengths) will result in variation of estimated model parameters. Here we addressed two of these issues by evaluating the effect of variation in the number of species and in the number of phylogenetic trees on the precision of parameter estimates from PGLS models based on a real comparative dataset.
As phylogenetic information, we used random draws of either 250, 500, 1,000 or 2,500 trees (tree-size classes hereafter) [we choose 2,500 as the higher limit because downloading tree sets > 2,000 tress from BirdTree can be problematic, as download often fails (D. Rubolini pers. obs. )]. Our simple simulation was based on the large-scale comparative analysis of the relationship between mass-specific productivity and life-history traits of 980 bird species carried out by  Sibly et al. (2012). We assessed variation in parameter estimates of the (negative) relationship between mass-specific productivity and body mass (both log 10 -transformed; see  Sibly et al. , 2012)  in two random samples of 50 and 100 species (a typical sample size in most comparative analyses of bird species) from the original 980 species set (50 and 100 species test datasets hereafter).
For each test dataset, we downloaded from BirdTree ('Hackett All Species') 250, 500, 1,000 and 2,500 randomly sampled trees. We repeated downloading 10 times for each tree size class, thus obtaining 10 random replicate tree sets for each tree size class. For each replicate set of trees, we built a MRC tree. We then ran a PGLS model for each test dataset and each replicate tree-size using as phylogenetic information either the MRC tree (MRC models) or all the trees in each replicate tree set using the model-averaging method (MA models). PGLS models were ran with the caper R package  (Orme et al. , 2012) , with λ estimated from the data by maximum likelihood, using R (2. 15. 2)  (R Core Team, 2013). For each run, we noted the estimated slope and standard error of MRC models, and the model-averaged slope and standard error from the MA models.
We obtained a total of 80 slopes and standard errors for each method (10 replicates × 2 test datasets × 4 tree-size classes). From each pair of slope and standard error, we calculated a z-value as the ratio between the two (absolute value; this was done also for model-averaged slopes and standard errors). To evaluate precision of model parameters, we calculated the coefficient of variation (CV out of the 10 replicate analyses) of each model parameter (slope, standard error, z) for each of the tree-size classes and method of analysis. The variability of CVs of z-values according to the test dataset, tree size class and method of analysis is illustrated in Fig. 1. CVs were below 0. 40% even for analyses based on 250 trees, and were consistently lower for the 100 than for the 50 species test dataset, especially for estimates obtained with the MA method.
CVs of z-values dropped to values varying between 0. 05% and 0. 12% for models based on at least 1,000 trees (Fig. 1 ). A pairwise comparison of z-values for each tree set revealed that the MA method returned consistently smaller z-values than the MRC method for the 50 species test dataset (4. 61 ± 0. 01 SE vs. 4. 65 ± 0. 01 SE, t 39 = 23. 1, P < 0. 001), while the opposite was the case for the 100 species test dataset (11. 56 ± 0. 01 SE vs. 11. 51 ± 0. 01 SE, t 39 = 27. 2, P < 0. 001). Trends of CVs of other model parameters according to tree size-class were similar as those shown in Fig. 1  and were of the same magnitude as those of z-values (minmax, slope: 0. 01%-0. 32%; standard error: 0. 04%-0. 17%) (details not shown). Although there is no generally recommended threshold for precision estimates, we are confident that CVs of parameter estimates < 0.
5% will not strongly affect the conclusions of phylogenetically corrected analyses. However, downloading from BirdTree and including in the analyses at least 1,000 trees should result in very precise model parameters, regardless of the method of analysis. Clearly, our simulations on a real dataset suggest that inclusion in PGLS models of > 1,000 trees does not improve the precision of parameter estimates. Moreover, we suggest to run the analyses either using trees based on the 'Hackett' and 'Ericson' backbones, to check for consistency of results, although it is likely that for most comparative datasets the 'Hackett' and 'Ericson' backbones will give consistent results. Indeed, as it is also clear from our simulations, comparative regression models appear relatively robust to some degree of misspecifications in the underlying trees, either in terms of branch lengths or topology  (Purvis et al.
Conclusions To date, most comparative analyses of birds have been carried out by combining available trees using a variety of approaches, often obtained using incomparable genetic methods (e. g. DNA-DNA hybridization and gene-based phylogenies). To our knowledge, the Bird-Tree project constitutes the first attempt to produce a complete phylogenetic tree of avian taxa based on a robust Bayesian phylogenetic framework. We believe that, if ad hoc phylogenetic analyses based on novel, original molecular data are not available (as is the case with the vast majority of evolutionary comparative studies of birds carried out to date), phylogenetic trees obtained from BirdTree will provide evolutionary ecologists with a unique opportunity to standardize the presentation of their phylogenetically-corrected statistical analyses using a robust and validated phylogenetic background.
Acknowledgements We are grateful to  D. Horner  and  C. Gissi  for assistance with Python language, and to  G. Thomas  and two anonymous referees for useful comments on previous drafts of the paper. LZG was supported by funds from the  Spanish government  within the frame of the ' Plan Nacional' program  (ref. no. CGL2012-38262 ,  CGL2012-40026-C02-01 ).
, 2008). For the conserved miRNAs, the same miRNA/family names as in miRBase were assigned, but with new serial numbers (such as b, c, d) in some cases. For the novel miR-NAs, the names bna_novel_miRX1 to bna_novel_miRX214 were given  (Meyers et al. , 2008). The targets of miRNAs in B. napus were predicted via the online sever psRNATarget  (Dai and Zhao, 2011). Using the function 'Usersubmitted small RNAs/ user-submitted transcripts', the query of transcripts was generated from the B. napus genome. Default parameters were used to filter candidates. The software PatMan was used to map the degradomic reads to the targets, and custom perl scripts were employed to identify candidate degradative targets. According to previous results (Jones-Rhoades and  Bartel, 2004; Shen et al. , 2014; Song et al.
Lipid biosynthesis-related genes As reference, the Arabidopsis lipid-related gene database was downloaded at http://aralip. plantbiology. msu. edu/ (Li-Beisson et al. , 2013). The annotation of lipid-related genes in B. napus was carried out using the method of  Wang et al. (2014)  with minor modifications, i. e. BLASTp under the E-value <1e-5 and sequence identify >50% for the search of orthologous genes.
Out of 645 miRNAs, 275 and 364 originated from the A n and C n subgenomes, respectively (six miRNAs remained unassigned). Besides the known B. napus miRNAs deposited in the current international miRNA database miR-Base (release 21) and reported in recent studies  (Huang et al. , 2013; Lukasik et al. , 2013; Wang et al. , 2013; Shen et al. , 2014) , 351 miRNAs including 47 conserved from 19 families and 304 novel miRNAs from 187 families were newly identified by this study. To confirm the expression of the predicted miRNAs, eight conserved and 14 novel miRNAs were chosen for stem-loop reverse transcription PCR analysis. The results confirmed the expected expression of all eight conserved and 13 of the 14 novel miRNAs, except for a non-specific amplification of miRX132 in B. napus seedlings (Supplementary Fig.
Differential expression was confirmed for 10 selected miRNAs (four conserved and six novel miRNAs) by stemloop qPCR (Supplementary Fig. S3 , available at JXB online). Among these, several miRNAs (such as miR159 and miR319) have a pronounced role in regulating plant leaf/root development  (Khraiwesh et al. , 2012). The expression profiles of four of these miRNAs and their targets in roots and leaves were further investigated using RT-qPCR. As shown in Fig. 2 , in all four cases (miR319, miR6032, miRX63, and miR148), a reciprocal change in the expression levels between the miRNA and its target was evident. Thus, these data suggested a direct miRNA-target expression modulation in regulation of developmental and/ or physiological processes in the root and leaf.
These data strongly suggested a partitioned expression of miRNA between the two subgenomes after the hybridization of speciation in B. napus. To investigate the heredity of miRNA expression, small RNA populations from seedlings of the same developmental stage (six leaves) of the two parental lines (Tapidor and Ningyou7) and their four DH lines were analysed. After normalization of small RNA reads, the expression levels of miR-NAs in Tapidor and Ningyou7 were compared with those in the four DH lines. Using a 2-fold change of the average expression level of a miRNA in the four DH lines relative to their parental lines as the threshold value  (Stupar et al. , 2007) , the additive genetic effect of miRNAs in B. napus was estimated. Significantly, a higher number of miRNAs with a non-additive rather than with an additive genetic effect (2-to 3-fold) were observed in the four DH lines (Supplementary Table  S12 , available at JXB online), suggesting the potential dominance of non-additive genetics of miRNAs in B.
Discussion This study carried out the first genome-wide investigation of miRNAs in B. napus using the newly sequenced B. napus genome. In addition to known B. napus miRNAs, identified mainly based on the two progenitor genomes of B. napus  (Zhou et al. , 2012; Korbes et al. , 2012; Xu et al. , 2012; Zhao et al. , 2012 , Shen et al. , 2014) , a new set of B. napus miR-NAs was identified, including both conserved and novel ones. As expected, the number of B. napus miRNAs identified so far is significantly higher than that in the model dicot species Arabidopsis thaliana and monocot species rice (Oryza sativa). To the best of our knowledge, this represents the largest miRNA population ever found in a single plant species up to now, thus offering a unique dataset for the study of the origin, genomic structure, and evolution of miRNAs in allopolyploid plant genomes.
Acknowledgements This work was supported by the  National Basic Research Program of China  ( 2011CB109306/2015CB150200 ), the  Ministry of Science and Technology of China  ( 2013IM030700 ), the  National Science Foundation of China  ( 31100876 ), and the  FNR (Fachagentur Nachwachsende Rohstoffe) ,  Germany  ( 22410312 ). The authors thank DAAD and the  China Scholarship Council  for travel grants and the  Jiangsu Collaborative Innovation Center for Modern Crop Production (JCIC-MCP ).
Our general approach is to project the bipartite host-parasite networks to unipartite networks by connecting two host individuals if they share at least one parasite species (Fig. 1C, D )  [23, 24]. Although using networks based on parasite sharing can yield important insights into the relationship between network structure and possible transmission patterns  [23, 24] , a complete picture of the system requires an analysis of both the host-parasite and the (projected) transmission networks, because the structure of the latter is a direct result of the structure of the former. In addition, the effect of the inclusion of multiple hosts in such networks on parasite dynamics has not been investigated. We used data on south-east Asian rodents and their gastrointestinal helminth parasites. To assess the value of including multiple species, we compared multi-versus single-species individual-based networks (Fig.
g. body mass, sex, etc). For continuous characteristics, the difference was calculated as an absolute difference; for discrete characteristics, 1 was assigned if the two individuals had the same value (e. g. both were males), and 0 if they differed in the characteristic. In the multi-species networks, we included a continuous explanatory variable matrix that contained patristic distances (sum of phylogenetic branch lengths) as a measure of phylogenetic distance between a pair of rodents. The output of the analysis is similar to that of a 'classic' logistic regression and includes a list of coefficients and their statistical significance (see  [46, 47]  for details on how statistical significance is calculated in MRM). We only analyzed networks with >10 individuals and omitted RSM from analyses in Mondokiri because data were missing for seven individuals.
This work was supported by the  French National Research Agency bio diversity Community Ecology of Rodent  and their  Pathogens  project  ANR 07 BDIV 012  CERoPath. (http://www. agencenationale-recherche. fr/). SP was funded by the  Kreitman Foundation Fellowship, Ben-Gurion University of the Negev. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Code and data Analyses were performed using R (version 3. 1. 1; [50] ) within the Linux environment with aid of the 'bipartite' package (version 2. 04;  [51] ). We calculated EC with the 'evcent' function from the igraph package (version 0. 7. 1;  [52] ). Multi-model inference was done with package MuMIn (version 1. 10. 5) in R  [53]. Modularity analyses were done with software bipartmod (http:// seeslab. info/downloads/bipartite-modularity/). MRM analysis was done with a modified version of the 'MRM' function from the 'ecodist' package (version 1. 2. 9) in R  [54]. We provide the R code and data in S1-S4 Data. Matrix data and original files and code to assemble the phylogenetic tree are also available at Figshare http://dx. doi. org/10. 6084/m9. figshare. 1210724.
After removing shorter sequences, the final version of the genome assembly consisted of 309 Mb including 164. 2 Mb in 7 chromosome-length sequences or pseudo-molecules, plus 75. 2 Mb in 542 scaffolds longer than 50 Kb. The seven pseudo-molecules ranged from 13. 02 to 28. 17 Mb (Supplementary Table  1 ). Genome annotation of the red clover genome. We annotated 40,868 genes and 42,223 transcripts. Fig. 1  illustrates the spatial density of the 22,042 genes in the chromosomes (see Supplementary Fig. 5  for the individual chromosomes). A homologous protein in the UniprotKB database was found for 39,516 transcripts (93. 6%), and 1,580 of the remaining transcripts (3. 7%) had a novel ORF. A total of 31,576 transcripts (74. 8%) was annotated with at least one GO term (Supplementary Table  3 ). The number of genes in red clover is lower than in M.
We found 347 duplicated gene pairs in red clover (Supplementary Fig. 8 ), approximately three times less than the 963 gene pairs found in M. truncatula  3. The pairs in red clover originated from duplication events around 12. 7 MYA (Supplementary Fig. 9 ), thus after the divergence from M. truncatula, but we did not find a closer relation to repeat elements Gypsy or Copia in these duplicated gene pairs relative to all genes (Supplementary Fig 10). The repeat content was 41. 82% (Supplementary Table  4 ), which is slightly lower than previously reported  15. We re-annotated the repeat elements in several legumes with recent versions of the databases and tools, and revised the fraction of repeats in M. truncatula to 48. 7%, which is closer to the red clover values than the original annotation (30.
All three BAC libraries are available to the public from the Arizona Genomics Institute Resource Center (http://www. genome. arizona. edu/orders/). Physical map and BAC end sequencing. Using methods previously described, the three red clover BAC libraries were subjected to BAC clone end sequencing and BAC clone SNaPshot fingerprinting (FP)  29. Specifically, we used 18432, 9216 and 9216 BAC clones from the libraries TP_MBa, TP_ABa, and TP_ABb, respectively, which together represented nearly 10x genome coverage. The output data provided the raw inputs of the genome frame to allow physical map construction, anchoring of genetic and physical maps to the M. truncatula reference sequence  3  , and for comparative analysis to other genome data sets. A de novo BAC clone physical map was assembled with the FP data using FPC software with the settings and parameters as previously described  29.
The two pair-end and two single-end shotgun libraries were assembled, and later scaffolded using mate-pairs libraries with four different insert lengths, using Platanus v1. 2. 1  17  , which is optimized for heterozygous genomes. Scaffolds shorter than 500 bp were filtered out. We used Kmer spectra analysis to compare the assemblies produced by different pipelines, as well as our final assembly with the previously published assembly. A K-mer spectrum is a representation of how many fixed-length words or K-mers (y-axis) appear a certain number of times or coverage (x-axis). We used 31mers in our plots. The K-mer counting was performed with Jellyfish  33  and the comparison and plotting was performed with KAT, a tool developed at TGAC (https://github. com/TGAC/KAT). Further information can be found in the manual (https://documentation.
Most of the scaffolds with accepted links had been previously placed. In order to place a contig into a contiguous position we looked for any unplaced scaffold linking to the same contig as any previously placed scaffold. We used the EnsEMBL database and pipelines to construct the pseudo-molecules and reassign the coordinates of the features (genes, transcripts, exons, etc) to them. Each pseudo-molecule join was padded with a 10 Kbp gap.
Genome annotation and comparative analysis. The annotation pipeline is represented in the Supplementary Fig. 23. Repetitive and low complexity regions of the scaffolds were masked using RepeatMasker 36 based on self-alignments and homology with the RepBase public database and specific databases built with RepeatModeler 37. LTR retrotransposons were detected by LTRharvest  38. Repeat elements were classified with TEclass 39 and RepeatClassifier  37. The 5′ and 3′ ends of each LTR identified by LTRharvest were aligned with MUSCLE and used to calculate the nucleotide divergence rate with the Kimura-2 parameter using MEGA5  40. The insertion time was estimated by assuming an average substitution rate of 1. 3 × 10e-8, as in the common bean  5  analysis. De novo and genome guided ab initio transcripts were assembled from RNA-Seq reads 10 using Trinity 41 and Tophat/Cufflinks 42 , respectively.