dataesr/scientific-paragraphs-categorization huggingface dataset:
google. com/p/ea-utils  [62]. The alignment step is performed using Burrows Wheeler Aligner  [63]  and Pileup is performed using SAMtools  [64]. Once the pileup is done, an in-house script collects the data per-position and calculates the variance at each nucleotide position by root mean square deviation (RMSD) and determines the mean variance and standard error across the whole genome  [65].
This work was supported by  European Research Council   Starting Grant  # 242719 ,  French National  Grant  ANR-09-JCJC-0118-1 , and the  LabEx IBEID (Integrative Biology of Emerging Infectious Diseases) program. KRG was supported by a stipend from the  Pasteur-Paris University (PPU)  International PhD Program and by  Institut Carnot Pasteur Maladies Infectieuses. Work performed in the laboratory of MCS  is supported by  French National Grant  ( ANR-09-JCJC-0045-01 ), the  European Research Council  ( FP7/2007-2013 ERC 242703 ), and the  LabEx IBEID (Integrative Biology of Emerging Infectious Diseases) program. ABF's contribution was supported by the  European Commission   Framework Program Seven Award ''InfraVec''  (project number  228421 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Fungal protein sequences homologous to PTR2 transporters were identified by BLASTp searches within the Mycocosm database  (Grigoriev et al. , 2014). Prediction of trans-membrane domains was performed using TMHMM (http://www. cbs. dtu. dk/services/TMHMM-2. 0/), SOSUI (http://harrier. nagahama-i-bio. ac. jp/sosui/), HMMTOP (http://www. enzim. hu/hmmtop/) and TMpred (http://www. ch. embnet. org/software/TMPRED_form. html) programs. Amino acid sequences were aligned using MUSCLE  (Edgar, 2004)  and their phylogenetic links inferred by using the Maximum Likelihood method based on the JTT matrix-based model  (Jones et al. , 1992)  as implemented in MEGA5  (Tamura et al. , 2011).
C. 2. A. 17) family. For this reason, it was called RiPTR2. Recent releases of the complete genome sequence of R. irregularis  (Tisserant et al. , 2013; Lin et al. , 2014)  allowed us to assess that RiPTR2 is a single copy gene containing four introns (Figure  S1 ). Twelve transmembrane domains were predicted for RiPTR2 amino acid sequence (Figures  S1, S2 ) using HMMTOP, TMHMM, SOSUI, and TMPRED programs. This structure is similar to that of the well characterized PTR2 sequence from S. cerevisiae which shows 40. 8% identity at the amino acid level to RiPTR2. Two putative homologs were found within an extensive transcripts dataset of another AM fungus, Gigaspora margarita (Salvioli et al. , in preparation), which is phylogenetically distant from R. irregularis. The two deduced amino acid sequences, named GmarPTR2A and GmarPTR2B show 56.
2 and 51. 7% identity to RiPTR2, respectively. A phylogenetic analysis was carried out by Maximum Likelihood method using PTR2 sequences from a number of fungi representative of distinct taxonomic groups retrieved from the MycoCosm  (Grigoriev et al. , 2014)  genomic database (Figure  1 ; Table  S1 ). RiPTR2 grouped with the two G. margarita sequences to form a well-supported sequence cluster clearly separated from sequences from Ascomycota, Basidiomycota and Zygomycota species.
, 2012). We also performed a time course experiment in the sandwich system to determine RiPTR2 transcript abundance at different times (7, 14, 28, and 60 days) post-inoculation (dpi) of  M. truncatula plants. Morphological analyses of roots showed almost no fungal structures at 7 or 14 dpi, while mycorrhization frequency increased from 28 to 60 dpi. Arbuscules were visible starting from 28 dpi and were slightly less abundant at 60 dpi than at 28 dpi (Figure  S3 ). Since in the ERM RiPTR2 is expressed at negligible levels, gene expression was evaluated in whole mycorrhizal roots, without making a distinction between IRM and ERM. RiPTR2 mRNA abundance increased in parallel to the development of the intraradical phase as demonstrated by morphological data and the parallel mRNA accumulation of MtPT4, the M.
ACKNOWLEDGMENTS The Authors express their thanks to  Cristina Calcagno  for her help in the microdissection experiments and  Ascensión Valderas  for technical assistance. Research was funded by the  University grant  ( 60% ) to  Luisa Lanfranco  and the Project  AGL2012-35611  from the  Spanish Ministry of Economy and Competitiveness  to  Nuria Ferrol. We thank  P. Bonfante  for access to an unpublished transcripts dataset of  Gigaspora margarita  ( BEG 34 ). We are grateful to  F. Martin  and the  Mycorrhizal Genomics Initiative consortium  for access to unpublished genome data. We also thank  S. Torres  and collaborators for the use of unpublished genome sequence data from Mucor circinelloides,  M. Berbee  and collaborators for those of Gonapodya prolifera and Conidiobolus coronatus, and  J.
Candidate Gene Sequencing and Sequence Analysis The E. urophylla genome has not been sequenced yet and at the time of the experiments, the E. grandis W. Hill ex Maiden sequence was not released. In that context, primer design was based on gene sequences from other species found in databases. Primers were designed in order to get the whole sequence of the candidate genes (Table  S1 ). Details of primer design for each gene are given in Supplementary Online Material. PCRs were performed in 10 µL reaction mixtures with 10 mM incubation buffer 1X CORE Kit Q-Biogene (Tampon CORE Kit Q-Biogene, MP Biomedical), 0. 30 µM forward and reverse primers, 1. 2 units of Taq DNA polymerase (Invitrogen) and 15 ng of template DNA. We used the following PCR program for the amplification: 94˚C for 4 min; then 35 cycles (40 cycles for CesAs) of 94˚C for 30 sec, an annealing step for 1 min at the primer's optimized annealing temperature for 1 min and 72˚C for 1 min (2 min for C4Hs), followed by a final extension at 72˚C for 5 min.
PCR products were loaded on agarose gels for electrophoresis and the quantity of PCR product was estimated by image analysis with the freeware Image J v1. 45b (Wayne Rasband, NIH). One hundred ng of primer product were sent to High-Throughput Genomics Unit (HTGU, Department of Genome Sciences, Univ. of Washington, Seattle) for direct sequencing. For each gene, the overlapping sequences obtained after direct PCR sequencing were assembled and aligned with Codon Code Aligner software (Licor. 2002-2007 ver. 3. 5. 2), and manually edited. For heterozygote sequences due the presence of INDELs, the sequences of the two alleles were either deducted manually or with INDELLIGENT software  [39].
Determining the Demographic History of E. urophylla in Timor Island Using Microsatellites Data We used the Approximate Bayesian Computation (ABC) approach as implemented in DIYABC software  [42] ,  [43]  to determine the demographic scenario that may have shaped E. urophylla diversity pattern observed on SSR data. ABC uses coalescence tool to generate thousands of simulated data having a similar configuration then observed one in terms of individual number, marker type and number and relies on summary statistics to summarize and compare simulated and observed data. Simulations are launched from a set of user-defined demographic scenarios with parameters sampled from prior distributions. DIYABC software provides a full set of tools that we used to simulate SSR data and to perform scenario choice, parameter evaluation and several steps of model validation.
We simulated then 1000 additional data sets using the parameter posteriors of the scenario with highest probability and compared their summary statistic with those of the observed data. The scenario with highest probability is considered as suitable if the summary statistics of the observed data are included in the 95% confidence interval of the summary statistic's posterior predictive distribution. Parameter posterior distributions were estimated only for the scenario with highest probability using the 10% closest simulated points with a local regression procedure and a logit transformation of the parameters. Confidence in scenario choice was finally evaluated by calculating power and alpha risk associated with choosing the scenario with highest probability. Alpha risk was evaluated by generating 1000 additional simulated sets for each of the four rejected scenario using their respective prior and by assessing the proportion wrongly attributed to the scenario with highest probability based on the reference table.
Power was calculated by generating 1000 additional simulated data sets for the scenario with highest probability and by calculating the proportion correctly assigned using the reference table. Confidence in parameter estimates was evaluated by comparing the known parameters of a set of 1000 newly simulated data set obtained for the most likely scenario to those estimated using the current reference table. As measure of parameter estimation error and bias, we calculated the average relative bias and the factor 2, i. e. the proportion of pseudo observed data set for which the point estimate is at least half and at most twice the true value  [42]. Events are considered backward in time, from present to past. Constraints on the parameters are indicated along with each scenario along with the detail of parameters used with DIYABC.
Determining Selective Status of Candidate Genes Accounting for Demographic History Demographic history strongly impact sequence polymorphism pattern and it is necessary to account for it while searching for selection imprint on candidate genes. To achieve that aim, we build the expected null distribution of several summary statistics for sequence data, conditional to the most likely demographic scenario inferred from the SSR data set, and used it to assess neutrality status of the candidate genes. We built this conditional distribution and detected outlier sequences using EGGLIB Python/C++ library and its ABC commands  [52]. This library provides tools to easily perform coalescent simulations from inferred parameter posteriors and determine outlier status of candidate loci. Since DYIABC and EGGLIB do not use the same scenario parameters and that SSR and SNPs have different mutation rate and pattern, it is not possible to use the parameter posterior distributions inferred from SSR with DIYABC in our candidate gene analysis.
Nucleotide Diversity of Candidate Genes Each gene was sequenced on 18 individuals (Table  1 ). Sequences are available from GenBank under accession nos. KF970937-KF971152. On the full sample, the number of sites without missing data and gaps varied from 1027 bp (EuC4H2) to 4615 (EuCAD2), with number of segregating sites S varying from 39 (EuC4H2) to 170 (EuCAD2). Taking all genes together, we had 19,798 sites without any gap or missing data, cumulating 661 segregating sites. The overall level of diversity was 0. 00651 for π and 0. 00744 for θw. However, at a gene level, the diversity varied on a twofold extends: for π, from 0. 00479 (EuCesA1) to 0. 00899 (EuC4H2) and for θw, from 0. 0051 (EuCesA3) to 0. 00916 (EuC4H2). For the six studied genes, d d N S ratio always stayed bellow one at the full length scale, varying on almost a ten fold extend, from 0.
2012 ) (Table  S2 ) was used for QTL mapping. Nine SSR markers that had been mapped on chromosome 3B in that map also showed polymorphism in SynDH2  (Luo et al. 2012). They were also used to construct a genetic map of chromosome 3B (Table  S3 ). QTL IciMapping v3. 1 software (http://www. isbreeding. net), which is based on an inclusive composite interval mapping (ICIM-ADD) model  (Li et al. 2007) , was used for map construction and QTL analysis  (Zhang et al. 2012 ). The Kosambi function was used to calculate genetic distances  (Kosambi 1943). The selfed seed set rates were used to identify QTL in the haploid and doubled haploid populations (Table  S2  and Table  S3 ). Threshold values were calculated using 1000 permutations with a 0. 0500 type I error. The proportion of phenotypic variation explained by each QTL was calculated using single factor regression (R 2 ).
At least eight clones were sequenced for each sample. Sequence alignments were performed using the DNAMAN 6. 0 Demo software (Lynnon Biosoft). RNA extraction, cDNA synthesis, and quantitative real-time PCR When flag leaves emerged to approximately 5 cm, individual spikelets were dissected from the rachis, and all three synchronized anthers within a spikelet were removed from the first floret. One anther was fixed in 1:3 (v/v) acetic acid:ethanol and stored at 4°. This anther was later squashed in 2% acetocarmine to determine the developmental stage under a light microscope. The two remaining anthers were collected in a 2-ml EP tube frozen in liquid nitrogen and stored at 280°until used. These two anthers at similar phases of the cell cycle constituted a sample as a biological replicate. After anthers in the EP tube that was placed on a EP tube plate with liquid nitrogen were carefully grinded into fine powder using a pre-cooled glass rod, the RNAprep Pure Plant Kit (TIANGEN, China) was used to isolate the total RNA according to the manufacturer's instructions.
QTL analysis of hexaploidization capacity in the T. turgidum×Ae. tauschii haploids To further investigate the genetic basis of the high capacity of hexaploidization in LDN, haploid (triploid) population SynH1 (LDN/ AS313//AS60) and its corresponding doubled haploid (hexaploid) population SynDH1 were used for QTL mapping (Figure  1 ). The molecular map constructed by the two populations should be the same, although the ploidy of the populations was different. The molecular data for the 113 SynDH1 lines were used to construct a linkage map containing 588 molecular markers that had been assigned to the 14 A-genome and B-genome chromosomes, covering a total genetic distance of 2,048. 79 cM, with a mean distance of 3. 48 cM between adjacent markers  (Zhang et al. 2012 ). The selfed seed set rates of the SynH1 plants were used as phenotypic data for QTL analysis.
2008)  anchored to deletion bin 3BS1-0. 33-0. 55. Similarity search against the draft sequence of the A genome of T. urartu  (Ling et al. 2013)  revealed the presence of a homeologous copy in scaffold 25600, which belongs to deletion bin 3AS4-0. 45-1. 00. We failed to detect a homeologous copy on the D-genome draft sequence of Ae. tauschii  (Jia et al. 2013). Based on the scaffold sequence of ctg1014, a pair of primers was designed to clone the full Ttam CDS from T. turgidum LDN, AS313, AS2255, AS308, and PI14892, and common hexaploid wheat Chinese Spring. The F 1 hybrids of these T. turgidum lines with Ae. tauschii showed a different abilities to produce functional gametes  (Zhang et al. 2010). However, all the analyzed lines had the same Ttam-3B CDS sequence (GenBank no. KJ863558). This suggested that this gene has been relatively well-conserved.
Similarly, the homeologous gene on 3A was the same among analyzed lines (GenBank no. KJ863557). Although there were some variations in the DNA sequences among Ttam-3A, Ttam-3B, and Ttam-3D (GenBank no. KJ863559) (Figure  S1 ), their two cyclin domains were the same and were conserved among species, including Arabidopsis  (Marchler-Bauer and Bryant 2004; Marchler-Bauer et al. 2009 , 2011) , rice, and Brachypodium (Figure  7 ). We further checked the expression levels of Ttam at five meiotic stages, including leptotene to pachytene (LP), diplotene to diakinesis (DD), metaphase I, restitution nucleus, and dyads in T. turgidum•Ae. tauschii hybrids, with qRT-PCR. Overall, LDN-AS60 hybrids showed significantly lower expression levels than the AS2255•AS60 hybrids at all stages (Figure  8 ). Although the LDN-AS60 hybrids showed consistent expression among biological replicates in all stages, the AS2255-AS60 hybrids showed high variations in the first four stages, except at the dyad stage (Figure  8 ).
ACKNOWLEDGMENTS We thank  Professor Robert A. McIntosh , at  University of Sydney , for reviewing and revising this manuscript. We thank anonymous reviewers for their valuable critiques of this manuscript. This research was supported by the  National Natural Science Foundation of China  ( 31271723 ), the  863 Program  ( 2011AA100103 ) and  Sichuan Provincial Youth Fund  ( 2011JQ0016 ), and  South Dakota Experiment Station. Scaffold sequence of chromosome 3B BAC contig ctg1014 was obtained in the framework of  3BSEQ  project with the support of  ANR and France Agrimer  (grant # ANR-09-GENM-025-001 ).
Pattern of Population Recombination Rate To evaluate the historical pattern of recombination, we estimated the population-scale recombination rate () across the genome. The value for was calculated between neighboring pairs of SNPs using the program LDhat  (McVean et al. 2004 ). The genome-wide average estimate of was found to be 3. 17 Morgans/kb, which is lower than the estimate for S. cerevisiae ( = 5. 06 Morgans/kb) we determined using recently published sequence data  (Skelly et al. 2013 ). To assess recombination rate variability across the genome, we averaged these estimates in nonoverlapping 50-kb windows to obtain a finer-scale genetic map for each of the eight chromosomes (fig. 2c ). The results show heterogeneity in recombination rate along the genome, particularly on Sakl0C-left, which has an increased rate.
Recombination in this 1-Mb region is about 2. 7Â higher than that determined for the rest of the genome ( = 8. 5 vs. 3. 2 Morgans/kb). Therefore, Sakl0C-left is characterized by a higher GC-content and increased genetic diversity as well as a higher ancestral recombination rate as compared with the rest of the genome (fig. 2 ). All of these observations support the hypothesis that there is a stronger effect of GCbiased gene conversion on Sakl0C-left compared with the rest of the genome. We also scanned the genome for hotspots of recombination using stringent criteria to avoid the detection of false positives (see Materials and Methods). Basically, we tested for statistically significant increases in compared with flanking regions, using the program SequenceLDhot  (Fearnhead 2006). A total of 98 recombination hotspots, with an average length of 2 kb, were found in the whole population and half were located in the Sakl0C-left region (supplementary fig.
Estimation of the Timing of the Introgression Event Finally, we used the population genomic data obtained to estimate the minimum number of generations that have passed since the last common ancestor, allowing for us to estimate the length of time necessary for the evolution of the introgressed region to reach its current state. Following the same strategy as previously used for S. cerevisiae  (Ruderfer et al. 2006) , our data lead to an estimation of 55. 5 Â 10 6 generations since the last common ancestor (see Materials and Methods). If we consider that cell division of yeast in the wild ranges from 1 to 8 generations per day  (Fay and Benavides 2005) , the number estimated above corresponds to approximately 19,000-150,000 years since the most recent common ancestor. In addition, we estimated that approximately 500 generations of outcrossing occurred during the evolutionary history of L.
Acknowledgments The authors thank  Joshua Shapiro ,  Gwenael Piganeau ,  Guillaume Achaz , and  Kelle Freel  for their invaluable advice and their colleagues from the GB-3 G project for helpful discussions. They also thank  Sophie Siguenza  for bioinformatics assistance. They are most grateful to the  GeneCore sequencing team (EMBL, Heidelberg, Germany ). This work was supported by an  ANR  grant ( 2010-BLAN-1606 ), a grant from  CNRS  and  R egion Alsace  to C. R. , an  ATIP/avenir Plus grant  from the  CNRS  to G. F. , and an  ANR  grant ( 2011-JSV6-004-01 ) to J. S.
Methods Sequencing strategy. All sequencing libraries were constructed from the genomic DNA of a single Adriatic sea bass individual (meiogynogenetic male 57)  44, 45  (kindly provided by the late A. Libertini, CNR, Venice, Italy through J. B. Taggart, University of Stirling, Stirling, UK). The BAC library  44  was obtained from the German Resources Center for Genome Research (RZPD, Berlin, Germany). Plasmid libraries were constructed as previously described  7. Template preparation was performed automatically at the Max Planck Institute for Molecular Genetics (MPIMG, Berlin, Germany). Purification was based on size-selective precipitation in polyethylene-glycol 6000/2-propanol mixtures  46. Template DNA was sequenced on ABI3730xl capillary sequencers and raw sequencing data were processed using the PHRED basecaller  47  and LUCY  48  for quality clipping and vector clipping.
Duplicate reads and residual sequencing adapters were removed. All, 454 reads from 20-kb mate-pair libraries were filtered for duplicates and chimeric mates by mapping them to older assembly versions of the D. labrax genome using gsMapper v2. 3 (Roche) and confirming collinearity with G. aculeatus and O. latipes. Sanger reads and 454 single-end reads were directly used for assembly using Celera Assembler v6. 1 (CA6. 1)  49. Several softwares for WGS assembly were tested, among which SOAPdenovo (for Illumina data), Newbler (Roche) and CA6. 1. CA6. 1 was the best-performing one, although the first assemblies needed editing because of inconsistencies in the long-range mate-pair data. These issues were solved by removing duplicates and screening for potential chimeric mate pairs by making use of the strong collinearity of sea bass with the stickleback  12.
PCR products were then purified with AMPure beads (Agencourt). Size selection of B250-350 bp fragments was performed by electrophoresis on a 2% agarose gel. Libraries were sequenced on four lanes of an Illumina GAIIx using 2 Â 100 pb paired-end reads. After library cleaning with Trimmomatic  52  , reads from each of the four lanes were mapped independently on the draft sequence of the sea bass genome with Tophat and then assembled with Cufflinks  53  , and a reference transcriptome was built by merging results of the four lanes with Cuffmerge. Genome annotation. The ab initio gene prediction was carried out with GEN-ESCAN  54. For homology-based predictions, we first mapped known teleost proteins from Ensembl and GenBank databases on the sea bass genome using SPALN aligner v2. 12 (ref. 55).
Custom scripts were used to choose the best-scoring protein match in a cluster of matches defined by exact exon-exon matches in a first iteration and overlapping exons in a second iteration. Second, the CDS models from SPALN were combined with assembled RNAseq data by splitting the gtf files of all predictions and transcripts into overlapping exon-intron-exon fragments and merging them using Cuffmerge  53. This resulted in a high number of possible transcript models. We used Transdecoder (http://transdecoder. sourceforge. net) to assign coding sequences to these models and used the length of the CDS as a score for each model. We weighted the scores of these different models based on their origin (highest rank: RNAseq only, lowest rank: SPALN only). Afterwards, the best-scoring transcript from a cluster of redundant CDS was selected as the reference gene model.
Reference genes resulting from our combined annotation were functionally annotated using Blast2GO v2. 6. 4 (ref. 56) and a custom-blast database consisting of vertebrate proteins from GenBank. In additional, the gene model proteins were submitted to InterProScan to identify shared signatures with proteins of known function. Functional annotation of RNAseq data was performed using blastx against Ensembl fish species proteomes and the NCBI nr database. Results were contrasted with annotation based on the direct homology-based method, and permitted annotation of new genes and correction of some gene predictions. Whole-genome alignments. Reference genome sequences of seven teleost fishes (G. aculeatus, O. niloticus, T. nigroviridis, T. rubripes, O. latipes, G. morhua and D. rerio) were downloaded from the Ensembl website.