dataesr/scientific-paragraphs-categorization huggingface dataset:
Branches with <70% bootstraps were collapsed. The scale bars represent a genetic distance of 0. 05. A classical shape tree can be examined in Figure  S4 ; Figure  S7 : The ML phylogenetic tree (radical shape) of the CP nucleotide sequences, conducted in MEGA X. The isolates are indicated by their accession number or isolate name, host and country of origin. The isolates are color-coded based on the country of origin: pink: Poland, blue: Greece, green: China, red: Brazil, and orange: rest of world. Branches with <70% bootstraps were collapsed. The scale bars represent a genetic distance of 0. 05. A classical shape tree can be examined in Figure  S4 ; Figure  S8 : The ML phylogenetic tree (radical shape) of the CP nucleotide sequences, conducted in MEGA X. The isolates are indicated by their accession number or isolate name, host and country of origin.
The isolates are color-coded based on the method of detection: green: HTS, red: PCR and specifically with square PCR amplified isolates by one primer pair, whereas with circle the full-length isolates by PCR primer walking. Branches with <70% bootstraps were collapsed. The scale bars represent a genetic distance of 0. 05. A classical shape tree can be examined in Figure  S4 ; Figure  S9. ML phylogenetic tree conducted in MEGA X using full-length ASPV CP amino acid sequences. The analysis involved 124 ASPV isolates, with a total of 438 positions in the final dataset. All GenBank isolates are reported with their accession numbers, isolate name, host type and country of origin. The isolates from this study are indicated by their isolate name, host and country of origin. In parenthesis is given the size of the CP gene.
, 2017; Lorrain et al. , 2019). Most genomes are collected in the JGI Mycocosm database, which allows to draw comparative studies  (Grigoriev et al. , 2014). Several tools are available to specifically explore the genomes annotations. So far, no global survey of TF genes was conducted in the order Pucciniales, despite reports of gene family expansions presenting typical DBDs  (Aime et al. , 2017; Cuomo et al. , 2017; Duplessis et al. , 2011a). Among rust fungi, Melampsora larici-populina (poplar rust fungus) is a pioneer model for rust genomics with transcriptomic resources that encompass the whole life-cycle -i. e. infection of the two host plants and the five spore typesmaking it possible to study specific gene categories based on life-cycle transcriptomics  (Duplessis et al. , 2021). In this study, we analyzed the repertoire of transcription factors in Pucciniales compared to other basidiomycetes and to ascomycetes.
Transcriptomic resources Transcriptomic profiling of M. larici-populina was established across its life cycle based on previously published studies transposed to the version 2 of the genome  (Guerillot et al. , 2021). Briefly, gene expression values were determined for dormant and germinated urediniospores, time course infection of poplar leaves and telia by oligoarrays  (Duplessis et al. , 2011b; Hacquard et al. , 2013)  and for basidia, as well as for spermogonia and aecia on larch needles by RNAseq  (Lorrain et al. , 2018a). Expression values for oligoarrays and RNAseq were normalized separately. For the present study, we extracted expression profiles of M. larici-populina TF genes from an open transcriptomic dataset (Supplementary Table  S1 ). The Morpheus tool from the broad institute website (https://software.
g. TFII domains) or were related to plant TF with a low representation in the overall dataset. PFAM with a very low representation among all fungi (less than 10 occurrences found among 874 genomes) were also discarded from the set. A total of 58 TF families were defined from their DBD similarity, corresponding to 480339 TF domains in 874 fungal genomes (Table  1 ). The order Pucciniales belongs to the subphylum Pucciniomycotina in the phylum Basidiomycota. In order to compare rust fungal TFs, we set taxonomical groups as follows: Pucciniales (n=12), Other_Pucciniomycotina (n=13), Ustilaginomycotina (n=28), Other_Basidiomycota (n=233) and Ascomycota (n=588). In average, the total number of genes is greater in Pucciniales compared with other fungal groups, i. e. 18280 ±1580 in Pucciniales versus 7788 genes ± 500 in Other_Pucciniomycotina, 6945 genes ± 628 in Ustilaginomycotina, 15137 genes ± 687 in Other_Basidiomycota and 10920 genes ± 163 in Ascomycota.
Comparison of TF families distribution across fungi shows specific evolutionary trajectories in Pucciniales. To evaluate whether the difference observed in fungal TF domains distribution is related to specific contraction or expansion of particular TF families, we extracted the families most represented in the whole dataset (Table  2 ). The top 10 TF families (83% of the whole fungal TF domains collection) are shown in Figure  2. The Zn cluster (28. 1%), Fungal-trans (24. 1%), Zf-C2H2 (9. 8%), Zf-C3HC4 (6. 5%), and SET (3. 3%) families are predominant. Globally, a similar picture can be observed in all surveyed fungal groups. At the contrary, the order Pucciniales presents a different proportion and the four major families are in order Zf-C2H2, Zf-C3HC4, Zn cluster and SET (Figure  2A ). Moreover, we can see that whereas the Zn cluster TF dominates in all other fungi, the Zf-C2H2 is more represented in the Pucciniales (ratio of Zf-C2H2/Zn cluster: 1.
Transcriptomics of the poplar rust fungus highlights specific expression profiles of TFs during its life cycle. The poplar rust fungus M. larici-populina is the only rust fungus for which a survey of the complete life cycle has been established by transcriptomics, covering stages at which all spore forms are formed. Taking advantage of this dataset  (Guerillot et al. , 2021) , we extracted the expression profiles of 213 genes (among a total of 252 TF genes) belonging to TF families. The missing expression profiles correspond to genes for which expression details were not available from the oligoarrays defined on early versions of the genome. The Figure  5  presents the global expression profiling of the 213 M. larici-populina TF genes sorted by k-means clustering (see Supplementary Figure  S1  for the expanded view of the global profile and k-means clusters).
For instance, almost all genes of the FAR1 family show a specific expression at the earliest stages of poplar infection, whereas they are not detected in sporulating spores. At this stage, infection hyphae are colonizing the host mesophyll  (Hacquard et al. , 2011a ). An assumed role of FAR1 is mobilization of intracellular lipid reserves during plant infection by the phytopathogenic fungus Magnaporthe oryzae  (bin Yusof et al. , 2014). It would be interesting to decipher the exact role of FAR1 at this stage and whether regulation of lipid metabolism also plays a role at this stage of rust infection. M. larici-populina CSPs exhibit the most remarkable expression profile among TF genes, restricted to the basidia stage with the highest transcript levels across the global expression dataset. Basidia are produced from telia right after overwintering when favorable conditions are met  (Duplessis et al.
mobility group (HMG) box; PF09011: High mobility group (HMG)-box domain; PF04769: Mating-type protein MAT alpha 1 HMG-box Homeobox/Homeodom ain PF00046: Homeobox/Homeodomain; PF05920: Homeobox KN domain HSF DNA-bind PF00447: Heat shock factor (HSF)-type, DNA-binding HTH PF01381: Helix-turn-helix; PF13560: Helix-turn-helix domain KilA-N PF04383: KilA-N domain Myb PF00249: Myb-like, DNA-binding domain; PF13837: Myb/SANT-like, DNA-binding domain; PF00319: SRF-type transcription factor (DNA-binding and dimerisation domain).
Acknowledgements P. L, N. G and L. G received funding from the  European Community 's  Horizon 2020 Project  under grant agreement no. 821003  (4C). The  Surface Ocean CO₂ Atlas (SOCAT ) is an international effort, endorsed by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study   (SOLAS)  and the  Integrated Marine Biosphere Research (IMBeR) program , to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.
Background: Quaternary climate fluctuations have been acknowledged as major drivers of the geographical distribution of the extraordinary biodiversity observed in tropical biomes, including Madagascar. The main existing framework for Pleistocene Malagasy diversification assumes that forest cover was strongly shaped by warmer Interglacials (leading to forest expansion) and by cooler and arid glacials (leading to forest contraction), but predictions derived from this scenario for forest-dwelling animals have rarely been tested with genomic datasets.
Genomic resources Both species of mouse lemurs were trapped in two forest sites within the Ankaranfantsika National Park (ANP), Ravelobe and Ankomakoma. Similar to previous studies in the region, the capture data revealed that the two species were not evenly distributed within the two forest sites, even though the trapping effort was the same along all transects  [41]  (Fig. 1b, c  and Additional file 1: Table  S1  for details). Two genomic datasets were generated based on the RADseq data for complementary analyses. The Analysis of Next Generation Sequencing Data (ANGSD; dataset 1)  [62]  pipeline resulted in genotype likelihood information from a total of 324,608 variable sites for M. murinus (n = 22) and 601,571 variable sites for M. ravelobensis (n = 56). The number of SNPs in the called genotype dataset (dataset 2) yielded 122,053 SNPs for M.
Demographic modelling The demographic history of M. murinus and M. ravelobensis was inferred using three complementary approaches (Stairway Plot, PSMC, IICR-simulations). The Stairway Plot  [55]  and the PSMC (Pairwise Sequentially Markovian Coalescent,  [56] ) were used to infer population size changes from the RADseq data and whole-genome sequences, respectively. The Stairway Plot analyses with the entire dataset for each forest site (Additional file 1: Fig. S4 ) and for the two sites together (Fig. 3b ) suggested the same overall demographic trend for M. murinus and M. ravelobensis. Both species exhibited an increase of population size < 100 kyr and unexpectedly reached their largest size between the LGM and the African Humid Period (N e ~ 210,000 for M. ravelobensis and ~ 60,000 for M.
murinus). These maxima were followed by a subsequent continuous decline in population size that started at the onset of the AHP and lasted until the present. Stairway Plot results consistently suggested a higher N e for M. ravelobensis than for M. murinus, even after standardizing the number of individuals in the analyses (n = 7 per forest site and species, Additional file 1: Fig. S5 ). Despite the sex-biased dispersal patterns suggested for our study species  [63] [64] [65] , the Stairway Plot analyses performed with the M. ravelobensis dataset (n = 22 males and 22 females) confirmed an identical demographic history when considering males and females separately (Additional file 1: Fig. S6 ).
Sub-libraries were randomly sheared  [105, 106]  using a Covaris M220 ultrasonicator, resulting in fragments with an average size of 550 bp. Sheared DNA fragments were ligated to the second adapter and all fragments with both adapters were amplified in 10 Polymerase Chain Reaction (PCR) cycles. DNA concentration and fragment sizes of the amplified libraries were verified using qPCR and Fragment Analyzer. The sublibraries were sequenced using 150 bp paired-end reads on an Illumina HiSeq3000 platform at the GenoToul-GeT-PlaGE Core Facility (Toulouse, France). Raw data was initially demultiplexed using the tool splitbc implemented on the FASTX-toolkit (http:// hanno nlab. cshl. edu/ fastx_ toolk it/), and the quality of the raw data was verified with FastQC v0. 11. 7 (http:// www. bioin forma tics.
bbsrc. ac. uk/ proje cts/ fastqc). Trimmomatic v0. 36  [107]  was used to remove Illumina adapters from the reads, remove reads with low quality bases, and to trim the reads to a minimum 4-base sliding window with quality score below 15. Additionally, reads with less than 60 bp length after the filtering steps were removed from the analyses. After quality filtering, BWA-MEM (http:// biobwa. sourc eforge. net/) was used to align the paired-end reads to a high quality genome assembly of Microcebus murinus (GCA_000165445. 3) [108]. Finally, the software SAMtools v1. 8  [109]  was used to remove PCR duplicates (i. e. , sequence reads that result from sequencing two or more copies of the exact same DNA fragment;  [110] ) and to convert the Sequence Alignment Map (SAM) format to the corresponding binary version (BAM).
To ensure that only autosomal data was used for the analyses, the M. murinus scaffold NC_033692. 1 (designated as the X-chromosome) as well as the M. murinus mitochondrial genome (NCBI Accession Number: KR911908. 1) were excluded from the aligned BAM files. The autosomal BAM files were then used as input files for all downstream genomic analyses. In addition to the RADseq dataset, one M. murinus (Ankomakoma) and two M. ravelobensis samples (Ravelobe and Ankomakoma) were selected for wholegenome sequencing. All samples were females which are the philopatric sex in both species  [65, 69]. Given that no female M. murinus was caught at Ravelobe, no wholegenome sequence was generated at this site for that species. Libraries were prepared at the Institute for Animal Breeding and Genetics of the University of Veterinary Medicine Hannover using the NEBNext Ultra DNA Library Prep Kit from Illumina (New England BioLabs, Ipswich, MA, USA).
DNA samples (~ 200 ng of DNA) were first sheared with an ultrasonicator (Covaris M220, Woburn, Massachusetts, USA) and the respective sizes were selected according to the manufacturer's recommendations. Whole-genome sequencing was performed on an Illumina NextSeq 500 (Illumina, San Diego, CA, USA) for 300 cycles in paired-end mode. Visual quality control of whole-genome sequencing data was performed using FastQC version 0. 11. 7. Reads were trimmed using PRINSEQ version 0. 20. 4 [111]  and mapped to the M. murinus reference genome ("Microcebus_murinus. Mmur_3. 0. dna. toplevel. fa. gz") using the BWA-MEM algorithm implemented in the BWA version 0. 7. 17  [112]. Similarly to the RADseq dataset, the reads that mapped against the M. murinus X-chromosome and mitochondrial genome were discarded from our analyses (see details above).
RADseq datasets: genotype likelihoods & SNP calling Next-Generation Sequencing platforms can generate large amounts of sequencing data but are prone to sequencing errors  [113] [114] [115]. It is therefore advisable to keep the data in form of genotype likelihoods during downstream analyses, because genotype likelihoods retain information about uncertainty in base calling, which enable to control for some problems commonly associated with RADseq datasets (e. g. , unevenness in sequencing depth and allelic dropout)  [114, 116, 117]. Consequently, most of our analyses were carried out on genotype likelihoods estimated with ANGSD  [115] , considering the following filters: a minimum base quality of 20, a minimum mapping quality of 30, minimum Minor Allele Frequency below 0. 5, a minimum mean depth of coverage of 4X  [115, 117, 118] , and sites present in at least 75% of the individuals (dataset 1: genotype likelihoods).
In addition to the genotype likelihoods, genotypes were called for each mouse lemur species for complementary analyses. Genotypes were called with SAMtools v1. 8  [109]  using the same quality filters previously used in ANGSD (see above), but considering a minimum depth of coverage of 10× to ensure high-confidence genotype calls (dataset 2: genotype calls)  [119]. Knowing that population genetics analyses can be biased by social structure and relatedness between individuals (e. g. ,  [120, 121] ), a relatedness analyses was performed next. Relatedness between two individuals is usually described by the concept of identity-by-descent (IBD), where two alleles are considered identical by descent if they recently descended from a common ancestral allele  [117, 122]. NGSrelate  [117]  was used to calculate the IBD coefficients (k 0 , k 1 and k 2 ; probability of two individuals sharing 0, 1 or 2 alleles from a single ancestor at any locus, respectively) between pairs of individuals using dataset 1  [122].
First-degree relatives were then inferred based on the comparison of the obtained IBD coefficients with the expected IBD probabilities (i. e. , parent-offspring: k 0 = 0, k 1 = 1 and k 2 = 0; full sibs: k 0 = 0. 25, k 1 = 0. 50 and k 2 = 0. 25)  [122]. Only one individual of each dyad of closely related individuals was retained in our dataset for downstream analyses (see Additional file 1: Text S3).
Population-genomic structure & isolation-by-distance Population-genomic structure patterns were investigated in M. murinus (n = 22) and M. ravelobensis (n = 56) using three distinct approaches. First, genetic differentiation between both sites was estimated per species using Wright's F-statistics F ST  [123]. The analyses were performed with ARLEQUIN  [124]  and significance was tested using 10,000 permutations (dataset 2). Second, signals of population genetic structure were evaluated by Principal Component Analysis (PCA) computed using PCAngsd (dataset 1)  [125]. The PCA Eigenvalues that explained most of the genetic variation between individuals (PC1 and PC2) were extracted and individuals were plotted using R (R CoreTeam 2014). Third, NGSadmix  [114] , a maximum-likelihood clustering method, was used to assign individuals to a specific number of clusters (K) using dataset 1.
Geographic distance was measured as the linear geographical distance in km separating each pair of individuals. Both Rousset's genetic distance and geographic distance were computed with SPAGeDi  [130]  based on dataset 2. The occurrence of isolation-by-distance was finally investigated with a Mantel test  [131]  using the VEGAN package  [132]  available in R (R Development Core Team 2005). Significance was determined via 10,000 permutations. In addition, genetic summary statistics were calculated for each species and forest site (see Additional file 1: Text S4 and Table  S5  for details). Inbreeding coefficients per individual (F) were also estimated using dataset 2 to detect deviations from Hardy-Weinberg equilibrium (Additional file 1: Tables  S6  and S7 )  [133]. One M. ravelobensis individual captured at Ravelobe showed a highly negative F value and was therefore excluded from the demographic analyses (Additional file 1: Table  S6 )  [133].
Stairway plot The Stairway Plot  [55]  method uses the Site Frequency Spectrum (SFS; i. e. , the distribution of the allele frequencies of a given set of SNPs in a population)  [136]  from population genomic sequence data to estimate a series of population mutation rates (θ = 4N e µ) following a multi-epoch demographic model, where epochs coincide with coalescent events  [55]. The realSFS tool implemented in ANGSD  [115]  was used to estimate a folded one-dimensional SFS for each mouse lemur species (i. e. , considering the two forest sites together) based on dataset 1, because we lack a suitable outgroup to determine the ancestral state of each allele  [136]. The folded 1d-SFS of each species was used as input data to generate 199 additional SFS by bootstrap, following the software guidelines  [55].
Inferences were then made based on the 200 1d-SFS for each species with Stairway Plot v2. 0  [55]. Given the observed isolation-by-distance pattern in both M. murinus and M. ravelobensis, the Stairway Plots were also repeated considering the two forest sites separately. Knowing that changes in N e through time are dependent on the number of possible coalescent events, this method is sensitive to the number of individuals and SNPs in the dataset  [59, 60, 137]. Therefore, analyses were first performed considering the entire dataset of each forest site and species (n ranging between 7 and 30), and also repeated with an equal sample size (n = 7) per site and species. For this analysis individuals of each site were randomly selected using R (R Development Core Team 2005). Finally, to evaluate the effect of sex-biased dispersal in the demographic inferences of mouse lemurs, we reran the Stairway Plot for the M.
This is the best scenario under the piecewise stationary n-island model that (i) explains the data and (ii) is of reasonable complexity and (iii) can be inferred and thus trusted to some extent. In order to produce a well estimated IICR curve we simulated 100,000 T2 values for the M. murinus and the M. ravelobensis individuals sampled in Ankomakoma using the ms software  [139]. The IICR was plotted with the observed PSMC using a python script available at: https:// github. com/ willy rv/ IICRE stima tor  [61]. The SNIF program and its documentation can be found in github. com/arredondos/snif. The ms commands used to generate the best-fitting IICR plots can be found in the Additional file 1: Text S6.
Impact of repeat regions in the demographic modelling It has been recently shown that genomic repeat regions may affect demographic inferences using diverse methods, including the Stairway Plot and PSMC  [140]. To evaluate the impact of repeat regions in our demographic inferences, we reran the Stairway Plot for the entire M. ravelobensis dataset (n = 55) and the PSMC analyses for the three whole-genome sequences without the repeat regions. We firstly removed all repeat regions from our BAM files following the RepeatMasker output file kindly provided by J. Rogers for the Microcebus murinus reference genome (GenBank Assembly Accession Number: GCA_000165445. 3  [108] ). We then reran the Stairway plot and PSMC using the same command options aforementioned. Results are presented in the Additional file 1: Text S7 and Fig.
Acknowledgements We thank  Madagascar National Parks  and the  Direction du Système des Aires Protégées (DSAP) , the  Direction Générale du Ministère de l'Environnement et des Forêts de Madagascar, Madagascar's Ad Hoc Committee for Fauna and Flora , and the  Committee for Environmental Research (CAFF/CORE)  for the permission to conduct the field work in the  Ankarafantsika National Park. We thank  Madame Jacqueline ,  Planet Madagascar  and  Durrell Wildlife Conservation Trust  for their help concerning field work logistics. The field work was possible thanks to the help of  Adolphe Rakotomanantena ,  Augustin Rakotonandrianina ,  Hauke Henkel , the guides from  ANP  and thanks to the welcoming local community. We thank  Ariel Rodriguez ,  Jordan Silvester  and  Jörn Wrede  for their help with computational issues and  Felix Pellerin  for his help and guidance with custom scripts.
Funding Open Access funding enabled and organized by Projekt DEAL. This study was supported by the  Deutsche Forschungsgemeinschaft  ( DFG Ra 502/20-1, Ra 502/20-3 ,  ME 4517/2-1 ) and  H. Teixeira  was additionally funded by a scholarship of the  Hannover Graduate School for Veterinary Pathobiology, Neuroinfectiology and Translational Medicine (HGNI). The work also benefited from several meetings with collaborators organized under the  ERA-NET Biodi-vERsA initiative  (No. 2015-138 ), project:  INFRAGECO (Inference, Fragmentation, Genomics , and Conservation), financed on the German side by the  German Bundesministerium für Bildung und Forschung  (# 01LC1617A ).
Availability of data and materials The RADseq sequences generated under this study are publicly available at Sequence Read Archive (NCBI) in the BioProject PRJNA560399 (Number Accession: SAMN16955525-SAMN16955602). Whole-genome sequences are available in the BioProject PRJNA632451. Accession Numbers: SAMN15237929 (M. ravelobensis, Ravelobe), SAMN15246264 (M. ravelobensis, Ankomakoma), SAMN15237931 (M. murinus, Ankomakoma). Scripts used for all analyses are available upon reasonable request.