dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods Frankia sp. phylogeny based on 50 housekeeping genes A set of 50 housekeeping genes was identified in Candidatus Frankia datiscae Dg1 and then used in a BLASTP search as the query. The corresponding BLAST searches were restricted to Frankia alni ACN14a, Frankia sp. Ccl3, Frankia sp. EAN1pec, Acidothermus cellulolyticus 11B, Geodermatophilus obscurus DSM 43160, Nakamurella multipartita DSM 44233, Stackebrandtia nassauensis DSM 44728 and Thermobifida fusca YX. All alignments were created using MUSCLE (multiple sequence comparison by log-expectation;  [34] ) at the EMBL-EBI website. Maximum parsimony analyses were performed using the software package PAUP Ã version 4. 0b10  [35]. All characters were weighted equally and gaps in the alignment were treated as missing. A heuristic search strategy with 10 random replicates, TBR branch-swapping and the MULTREES optimization was used.
Nod protein sequence phylogenies NodA, NodB, NodC, NodI and NodJ protein sequences were obtained from GenBank (NCBI)  [37]  by a multistep BLAST procedure using the protein sequence from Dg1 NodA, NodB, NodC, NodI and NodJ (GenBank accession numbers AEH09514, AEH10396, AEH10398 and AEH10399, respectively) as query. A BLASTP search of the non-redundant protein sequence (nr) database using default parameters (word size = 3, Expect threshold = 10, limiting to 100 target sequences, BLOSUM matrix, and an 11:1 gap cost/extension) generated 100 protein sequences of high similarity. To identify more distantly related potential homologs within the Actinobacteria and Proteobacteria, five iterations of PSI-BLAST were conducted using default parameters (word size = 3, Expect threshold = 10, limiting to 500 target sequences, BLOSUM matrix, and an 11:1 gap cost/extension and a threshold for inclusion of 0.
0001). Between 30 and 40 sequences were evaluated for analyses that maximized both sequence and taxonomic diversity. Some NodA sequences from diverse taxa in GenBank were eliminated due to short sequence length and/or origin in a nodule metagenome. For Dg1 NodA (AEH09513) homologs in actinobacteria, a BLASTP search for Actinobacterial sequences with similarity of 1e -10 or better in the NCBI and additionally in the JGI databases. Nineteen sequences were found in each database (not including Dg1). From these 38, sequences with query coverage of less than 75% were removed. Also, some of the remaining sequences were identical or a partial sequence of another. In such cases, only the longest sequence was kept. This way, altogether 18 unique actinobacterial sequences of NodA homologs were identified.
All alignments were created using MUSCLE  [34]  at the EMBL-EBI website, followed by manual adjustment. Selection of the appropriate amino acid substitution model was determined using PROTTEST 2. 4  [38]  through the server housed at the University of Vigo (http:// darwin. uvigo. es/software/prottest2_server. html). Maximum Likelihood analyses for all alignments (NodA, NodB, NodC, NodI, and NodJ) were performed using PhyML 3. 0  [39]  on the ATGC bioinformatics server. The settings for the NodA run included the JTT model and rate variation sampled from a gamma distribution (JTT+G). The settings for the NodB run included the WAG model, estimated invariant sites, and rate variation sampled from a gamma distribution (WAG+I+G). NodC settings included the LG model, estimated invariant sites, and rate variation sampled from a gamma distribution (LG+I+G).
Total RNA was sequenced in a single lane on an Illumina GAIIx system producing ca. 27 million reads (40 bp single reads). A total of 3,712,391 reads mapped to the complete genome of the Frankia strain Dg1 (GenBank: CP002801-CP002803) using Bowtie  [41]. Ribosomal RNA (16S, 23S and 5S) accounted for 96% of the total mapped reads (3,566,390 reads). The remaining 146,001 reads were filtered for exact duplicate sequences (likely PCR artifacts), sorted and converted to BAM files using the samtools package  [42] , leaving a final 66,962 reads which were analyzed. Data visualization and browsing were conducted with the Integrative Genomics Viewer software  [43]. Gene expression was calculated as the number of mapped reads per kilobase of gene sequence (rpkb) using custom R scripts  [44]. Standard database fields in NCBI were used for gene annotation.
The rpkb data were fitted with a negative binomial distribution (S1 Fig) using the fitdistrplus package in R  [45]. Expression quantiles (99%, 95%, 90%, 80%, 70%, and 60%) were computed. Expression of complete or nearly complete metabolic pathways was determined by importing the genome and transcriptome into BioCyc  [46] , and using Pathway Tools  [47]. Pathways, gene and protein annotations, and functional gene clusters were cross-checked in the KEGG Pathway Database supplemented with other sequence databases. A subset of total pathways was selected for further analysis based on high relative expression values, and a minimum of three genes per pathway. Statistically significant overrepresentation of key biosynthetic pathways in the active transcriptome was assessed for genes in each of the quantile bins using a hypergeometric test.
Primer sequences are given in S1 Table. Assay performance was evaluated with a standard curve. The Eco Real-Time PCR system (Illumina, San Diego, CA, USA) was used for data collection and standard curve generation. PCR products were validated by dissociation curve analysis and agarose gel electrophoresis. Assays were analyzed in triplicate with the standard curve method  [48]. PCR efficiency was calculated with the Eco software with data obtained from the exponential phase of each amplification plot. Primers were designed using Beacon Designer software (PREMIER Biosoft, Palo Alto, USA; S1 Table ). Gene expression data was normalized against expression of the translation initiation factor gene IF-3. Data analysis including data pre-processing and normalization was performed with GenEx (version 5.
The Phyloprofile function of the MAGE platform  [52]  was used to extract those genes present in the ACN14a, CcI3 and EAN1pec but absent in Dg1 at a sequence similarity of 30% over a length of 80% of the shortest sequence. Palindromic Repeats were analyzed with the palindrome tool from EMBOSS (http://bips. u-strasbg. fr/EMBOSS/) with no mismatches and the following parameters: 1. Repeat units between 8 and 11 bases with up to a 3 base gap. 2. Repeat units between 12 and 19 bases with up to a 7 base gap. 3. Repeat units between 20 and 90 bases with up to a 20 base gap. 4. Repeat units of less than 12 bases must occur at least 10 times in the genome. 5. Repeat units of less than 20 bases must occur twice in the genome. Tandem repeats was analyzed with the MUMmer 3. 13 package (http://www. tigr.
Finding and identifying repeats To identify ISs in the investigated organisms, a database of potential ISs was assembled from two sources: First, the program RepeatScout  [53]  was used to identify all repeated nucleotide sequences with >500 nt length in the investigated organisms. Repeats associated to non-mobile repeated genes, e. g. rRNA, photosynthesis genes and other regular genes present in multiple copies in the genome, were removed. This yielded 173 putative IS elements. Second, 4512 likely ISs identified in a previous investigation  [54]  were added to the database. This database included 3377 repeats from the ISfinder site  [55] , a web repository of known ISs, as well as 1135 repeats from cyanobacteria, Frankia genomes available in 2012 and other known symbiotic bacteria. The repeats found by RepeatScout were named with an abbreviation of the name of the originating organism, the letter 'R' and a number, e.
This was performed by using the footprints as queries against the database of repeats. The repeat sequence that received the highest score with the footprint as query was chosen as the most likely IS to occupy the footprint. In some cases, the best scoring repeat didn't cover the entire footprint. The search was then repeated with the remaining footprint as query. The process was repeated until all parts of the footprints had been identified. The whole process yielded a GenBank file for each of the investigated genomes, where the verified and putative ISs are described with position, kind and quality of identification (S2 Table ).
of Frankia OTUs together with seven v1-2 region sequences and two v2 region sequences (Cn endophyte; Dc endophyte) of Cluster II Frankia strains and three v1-2 region sequences of Cluster I or III Frankia strains available in GenBank  [37] ; S3 Table). Among the added Cluster II Frankia sequences, three originated in Pakistan (Dg1, Cn endophyte, Dc endophyte), one originated in New Zealand (FE37), and all others originated in North America. Sequence alignment was done using MAFFT  [60] ; the length of the aligned sequences was 321bp. A neighborjoining tree was constructed based on a pairwise distance matrix of percent nucleotide difference with 1000 boot strap replicates using PAUP Ã version 4. 0b10  [61]. All positions were weighted equally and all gaps were treated as missing data.
Dg1 contains two operons of canonical nod genes Two operons of homologs of the canonical nod genes were found in the Dg1 genome: 1) an operon containing nodA'B1A, coding for: NodA', GenBank accession number AEH09515 (a truncated NodA, only 36 amino acids); NodB1, AEH09514; and a full-length NodA, AEH09513; and 2) a nodB2CIJ operon, coding for NodB2, AEH10396; and NodC, AEH10397 (Fig  1 ). The proteins encoded by these genes display very high amino acid sequence similarity to the canonical nodABC genes (Figs 2, 3 and 4; S5 Fig;  S6  Table ), and their arrangement in the Dg1 chromosome suggests that they are expressed as operons, similar to their counterparts in rhizobia. Based on these findings of amino acid sequence similarity and synteny, they are referred to as nod genes throughout this manuscript.
Both Dg1 nod operons are expressed in symbiosis The expression of the two nod operons in Dg1 in nodules of D. glomerata was examined by quantitative RT-PCR, using the Dg1 translation initiation factor gene IF-3 (GenBank accession  number AEH10595) as constitutive control (see  [84] ). Transcription of nodA, nodB1, nodB2 and nodC in nodules (Table  2 ) was confirmed through transcriptome analysis (see below).
Nodule occupancy Since the microsymbionts of D. glomerata cannot be cultured, it cannot easily be ascertained how many strains occupy an individual nodule, whether nodules house Frankia exclusively or in combination with other bacteria. In addition, it was important to detect any major contribution to nodule occupancy by a non-Frankia taxon that could carry accessory nodulation genes (e. g. nodABC). To shed light on these questions, D. glomerata nodules induced by Frankia inoculant originating in Pakistan  [21]  and grown at Stockholm University (SU), and D. glomerata nodules induced by a California source of Frankia, and grown at the University of California in Davis (UCD), respectively, were used for DNA isolation and analysis of operational taxonomic units (OTUs) via PCR amplification with universal 16S rDNA primers 27F and 388R and 454 sequencing (SRA BioProject PRJNA258479).
This phytogeographic context does not represent the situation of Cluster II strains which have a broad host range, and a broader geographic distribution. Dg1 has more than 800 genes not present in any other Frankia genome available in the JGI database. Most strikingly, only Dg1 among sequenced Frankia genomes contains a copy (AEH09479-AEH09484) of the mammalian cell entry (mce) gene cluster from Mycobacterium tuberculosis, assumed to be involved in the uptake of sterols, which in Streptomyces coelicolor is required for plant root colonization  [103]. So we speculate that the ancestors of all Frankia strain were rhizosphere bacteria with large genomes and broad saprotrophic potential when one subgroup-the progenitors of Cluster II-acquired the gene set for nitrogen fixation and evolved symbiotic capabilities.
Acknowledgments This research was funded by grants from the  Swedish Research Councils Formas  ( 229-2005-679 ) and VR (2007- 17840-52674-16 ) to KP; by  CA Experiment Station  project  PLS-2173-H  to AMB; by a grant from the  French ANR (BugsInaCell   ANR-13-BSV7-0013-03 ) to PN; and  UC Mexus-Conacyt  grant to AMB and AMH. KB was supported by  UCDavis Plant Sciences Graduate Fellowship ; AO'B was supported by  NSF   GRFP DGE-1148897 ; EGW was supported by  NSF   EF-0949453  to MTF. Thanks go to  Y-Y Guo  for technical assistance and to  A Jumpponen ,  Kansas State University , for guidance in 454 sequencing. We appreciate the sponsorship of the  Joint Genome Institute for the Genome Jamboree of the Frankia_symbiont of Datisca glomerata genome ,  Walnut Creek , CA on July 27-28, 2009, especially the guidance of  N.
This work was supported by  Swedish Research Council Formas  ( 229-2005-679 ) to KP;  Swedish Research Council VR  ( 2007-17840-52674-16 ) to KP;  CA Experiment Station  project  PLS-2173-H  to AMB; UC  Davis Plant Sciences Graduate Fellowship  to KB; UC Mexus-Conacyt  05-24  to AMB Actinobacteria, but among Proteobacteria only in nodulators. Taken together, our evidence indicates an Actinobacterial origin of rhizobial Nod factors. and AMH;  NSF   GRFP DGE-1148897  to A'OB;  NSF   EF-0949453  to MTF; Sesam  ANR-10-BLAN-1708  to PN; and BugsInACell  ANR-13-BSV7-0013-03  to PN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Furthermore, there was no correlation between the mean CO number in an inbred and the mean telomere-to-nearest-chiasma distance when bivalents with the same chiasma numbers were considered (R 2 = 0. 06 for bivalents with single chiasmata and R 2 = 0. 09 for bivalents with two chiasmata). We also measured distances between adjacent chiasmata and found a similar pattern. Overall, these data show that bivalents with the same chiasma number exhibit similar chiasma distribution in all inbreds. Consequently, distribution of recombination patterns is likely governed by the same principle(s) in all inbreds. Furthermore, the fact that chiasmata in all six inbreds showed similar spatial distribution patterns relative to the telomere suggests that the inbreds did not exhibit major differences in chromosome condensation patterns or other structure-related properties that affect CO distribution  (31, 38).
In inbreds with high DSB numbers (Right), the distribution of the initially formed DSBs is already sufficient for proper pairing. generated using the Volume Viewer tool to help visualize bivalents that were oriented along the z-y plane and to distinguish bona fide chiasmata from chromosome twists (Fig. 1 ). Chiasma numbers were counted manually by carefully analyzing each bivalent. Blind tests were performed to validate the results. ANOVA analyses were performed using StatPlus (AnalystSoft Inc. ). Immunofluorescence Microscopy. Immunolocalization experiments were performed as previously described (19) using the following antibodies: rabbit anti-HsRAD51 (49) diluted 1:500, rabbit anti-HsH3K4me3 (ab8580; Abcam) diluted 1:500, and guinea pig anti-ZmZYP1 (50) diluted 1:50. For quantification of RAD51 and H3K4me3 foci, softWoRx software (Applied Precision) was used.
The rate parameter was set to such a value that the mean density of COs was 2 per morgan. The shape parameter was fitted to the experimental data using the maximum likelihood framework. To do this analysis, we first constructed a list of all CO positions after normalizing to the total length of bivalents. This list was used to infer genetic distances. Then, the likelihood of each bivalent was calculated, taking into account the positions of the COs. Finally, we searched for the value of ν for which the likelihood of the whole dataset (equal to the product of the likelihoods of each bivalent) was maximized. The search was performed using hill climbing  (52). Confidence intervals were obtained through resimulation. Specifically, the gamma model was used to simulate 1,000 independent datasets with the inferred best value of ν.
Acknowledgments This study was partly funded by the  French "Agence Nationale pour la Recherche" (Systerra program , PerfCom  ANR-08-STRA-11  and  Pépites ,  ANR-08-STRA-10-04  projects). We thank  Siobhan Staunton  for editing the English language. The authors would like to dedicate this article to the memory of their highly respected and much loved colleague,  Elvire Legname  ( INRA, UMR Eco&Sols ). MethodsX thanks the reviewers of this article for taking the time to provide valuable feedback.
Estimating the origin and diversification of plant groups using the fossil record The plant fossil record has been primarily explored using rangebased counts  (Cleal, 1993; Anderson et al. , 2007) , rather than occurrence-based data. Age ranges include the first and last appearance of a taxon, whereas taxonomic occurrence data include finer detail of fossil individuals, such as abundance and geographic distribution. Range-based approaches are commonly applied with standard datasets above the generic level to infer biodiversity and diversification trends across broad time intervals (e. g. for Phanerozoic trends;  Sepkoski et al. , 1981). By contrast, targeting finer-scale diversification events (e. g. the Late Permian biotic crisis and its corresponding recovery period) requires the analytical implementation of sampling parameters from fossil occurrence data (e.
g. Alroy et al. , 2001). Both approaches to exploring the fossil record are valid, but the use of occurrence data opens the door to a battery of methods to analyze data and test hypotheses in a more robust statistical framework. A new method, PyRate, was developed to infer the rates of speciation (or origination for higher taxonomic ranks) and extinction and their variation through time, within a Bayesian probabilistic framework  (Silvestro et al. , 2014b). The method is capable of handling fossil occurrence data (i. e. not only first and last appearances) and dealing with the inherent incompleteness of the fossil record. PyRate simultaneously estimates rates of origination, extinction and preservation, as well as the lifespan of each taxa in the dataset. The method thus takes into account the uncertainties associated with the fossil preservation process.
Data compilation This study is based on plant fossil occurrences downloaded from the Paleobiology Database (PBDB; http://paleobiodb. org/cgibin/bridge. pl) on 6 March 2014. We performed multiple search queries in order to obtain the most comprehensive dataset of the plant fossils, on a global level. The approach that captured the maximum number of records involved querying the database for all described plant classes and orders individually. With this approach we obtained 35 666 entries, which spanned most of the Phanerozoic (the oldest occurrences dating back to c. 424 million yr ago (Ma)). We included all occurrences that were identified to the species level as well as those identified only at the genus level but without specific assignation (e. g. 'Psilophyton sp. '). We did not revise fossil identifications provided by the PBDB.
Several culling procedures are normally used to avoid including incompletely identified taxa, such as searching and excluding entries identified with aff. , cf. , sp. or other modifications to binomial   (Janevski & Baumiller, 2009). We initially explored the effect of excluding such records from the analyses, but because we found no differences in genus-level diversity patterns we opted for using all available records. The dataset thus includes records from all lithologies, environments and continents that were freely available from the PBDB, including their estimated ages in millions of years  (Myr). Raw data are available as supplementary materials (Supporting Information Tables  S1, S2 ). It is well known that the plant fossil record is highly fragmentary, not least for plants  (Cleal & Thomas, 2010).
, 2012). This might have the effect of overestimating plant diversity from some time intervals (e. g. the Carboniferous). Although our dataset includes several different types of plant remains (e. g. leaves, fertile and sterile axes, seed, fruits and roots), leaf samples represent > 70% of all the occurrences. Therefore, we considered the discrepancies between genera and fossil-genera as unlikely to alter the major fluctuations of plant diversity. There are several practical reasons for avoiding species-level analyses. First, excluding all fossils without assignation to the species level would reduce the full dataset by 47%. Second, > 50% of the species we compiled are known from a single occurrence, which makes it difficult to estimate accurate preservation rates regardless of the methodological approach used.
Lastly, specieslevel data should arguably be more sensitive to floristic variation due to regional endemism and fossil preservation biases. The interpretation and analytical incorporation of regional variations of species diversity is often problematic, as it could either derive from true biodiversity differences or reflect the fact that regional morphological differences are often described as new species in the absence of more thorough evidence. Thus, we consider that genus-level analyses constitute a robust way of exploring the diversification dynamics of the plant fossil record. Plant macrofossils (e. g. whole flowers, fruits, seeds, leaves) are commonly assumed to be the most reliable source of data to study plant evolution, because they usually contain many taxonomically useful characters and can therefore be more easily and reliably assigned to different plant groups  (Wang et al.
, 2010). By contrast, microfossils (pollen and spores) often lack a sufficient amount of diagnostic synapomorphies that facilitate taxonomic identification at the generic level. We therefore subsampled our original dataset to include only macrofossils (i. e. > 60% of the original dataset) in the analyses. Analyses of diversification should ideally be performed on monophyletic groups, that is, clades in a phylogenetic sense. However, although monophyly assessment is straightforward in phylogenetic analyses based on molecular data, many questions remain on the phylogenetic placement of several fossil taxa. In addition, restricting the analyses to clades would require splitting the already fragmentary fossil data into smaller subsets, reducing analytical power and precision. We therefore categorized vascular plant fossil-genera into three major groups, distinguished by their functional ecology, morphology and relationships (following  Kenrick & Crane, 1997; Tomescu, 2009; Friis et al.
, 2011; Hao & Xue, 2013) : (1) spore-bearing plants (i. e. early tracheophytes, rhyniophytes, lycophytes, sphenophytes, early euphyllophytes, progymnosperms, ferns and related plants, but excluding bryophytes); (2) nonflowering seed plants (i. e. 'gymnosperms' including Cycadidae, Ginkgoidae, Gnetidae, Pinidae, Bennettitales, Caytonia and seed ferns); and (3) flowering seed plants (i. e. angiosperms). Fossil occurrences were assigned to the different categories based on the supra-generic taxonomic information provided by the PBDB and, when available, using the Index Nominum Genericorum (Smithsonian Institution, http://botany. si. edu/ing/). We matched the names of fossil-genera in our dataset with those listed at The Plant List (http://www. theplantlist. org/) to identify which taxa are extant.