dataesr/scientific-paragraphs-categorization huggingface dataset:
Construction of the Manually Curated TE (MCTE) library We used the REPET package (v. 2. 5)  (Flutre et al. 2011; Hoede et al. 2014; Quesneville et al. 2005)  for performing TE annotations using a manually curated TE (MCTE) library of consensus sequences. Briefly, REPET is composed of two main pipelines, TEdenovo dedicated to de novo detection of TE families and TEannot for the annotation and analysis of TEs in genomic sequences. For the creation of the MCTE library, we first run the TEdenovo pipeline (default parameters) on 13 genomes (representatives of the geographic distribution of the strains) (Table  1 ). The manual curation of the identified consensuses consisted in three main procedures: removal of redundant sequences, the manual identification of potentially artifactual sequences, and the classification of consensuses into families (Supplementary File S1.
pl script from FlyBase  (Gramates et al. 2017) , resulting in the following regions: 2L:530,000. 18,870,000; 2R:5,982,495. 24,972,477; 3L:750,000. 19,026,900; 3R:6,754,278. 31,614,278; X:1,325,967. 21,338,973. In order to determine the coordinates of the euchromatic regions in each scaffolded genome, we mapped scaffolds to the euchromatic region of the ISO1 genome using MUMmer (v3. 0)  (Kurtz et al. 2004 ). We then determined the coordinates in the scaffolded genomes by parsing MUMmer´s output and extracting the coordinates mapping at the boundaries of the euchromatic region of the ISO1 genome. After running the TEannot pipeline over the euchromatic regions of each genome, we performed a post-annotation filtering step consisting in the removal of TE copies <100bp, as REPET cannot accurately annotate these copies, and copies whose length overlapped >80% with satellite annotations.
Comparison with short-read-based TE annotations We compared REPET TE annotations on the de novo assembled genomes using the MCTE library with the annotations performed by two short-read-based TE annotation software: TEMP (v. 1. 05) (Zhuang et al. 2014)  and TIDAL (v. 1. 0) (Rahman et al. 2015). To make the comparison unbiased regarding the TE library, we also used the MCTE library for TEMP and TIDAL. We considered 11 strains representative of the geographic variability and with the best quality assembled genomes (Table  1 ). We used BEDtools (v. 2. 18)  (Quinlan & Hall 2010)  to find the overlapping TEs copies predicted by the three different methods (REPET, TIDAL and TEMP) in the 11 strains in a family-aware fashion. To estimate TEMP and TIDAL false negative rate and REPET false positive rate, manual inspection was performed for 300 of the 712 de novo insertions in the COR-014 genome.
To do this, we identified the region where each of these TEs was annotated according to REPET/TEMP/TIDAL and we aligned this region against the ISO1 reference genome to find out if a de novo insertion truly exists. We also used Blast to search for sequence similarities of such genomic region with (i) a database that contains all the individual TE copies identified in our genomes; and (ii) all Flybase´s 'Transposons -all annotated elements (NT)'. If REPET identified a TE not annotated by TEMP/TIDAL we considered it as TEMP/TIDAL false negative. If a TE was annotated by REPET but we could not find sequence similarities with any of the TE databases by Blast, we considered it as a REPET false positive. Additional 50 TEs annotated by TEMP/TIDAL but not by REPET were also manually curated following the same procedure.
Finally, once all the reliable TEs of each strain were transferred to the reference, the orthologous TEs were defined (Supplementary File S1. 11, Figures  S18-S20 ). To avoid false positives, we only used those TEs for which more than half of the orthologous TEs were larger than 120bp. All scripts used for the TE transfer are available at www. github. com/sradiouy/deNovoTEsDmel. After determining the presence/absence of TEs, we classified them in three frequency classes: rare (TEs present in <10% genomes), fixed (TEs present in >95% of the genomes) and common (TEs present in ≥10% and ≤95%). We then calculated the number of TEs for each frequency class considering different number of genomes, starting from 5 up to 47. We estimated the mean and standard deviation of the number of TEs in each frequency class by randomly choosing genomes (30 iterations).
TE eQTL analysis In order to identify polymorphic TEs significantly associated with the expression levels of nearby genes, we analyzed available whole-body RNA-Seq data from 12 European and 8 American strains (Table1, Table  S2C ). Briefly, RNA-Seq data was trimmed using the fastp package (v. 0. 20)  (Chen et al. 2018 ) with default parameters. Expression levels were quantified by applying the salmon package (v. 1. 0. 0)  (Patro et al. 2017 ) against the ENSEMBL (Dm. BDGP6. 22. 9) transcripts. Obtained transcripts per million (TPM) were summed up to gene level and rlog normalized using DESeq2 (v. 1. 28. 1)  (Love et al. 2014). eQTL analysis was performed using the QTLTools package (v. 1. 2)  (Delaneau et al. 2017)  taking into account the population structure (Figure  S21 , S22). Putative cis-eQTL were searched within a 1Kb window around each gene using the cis module in QTLTools.
Selection Analysis We looked for evidences of selection in genomic regions targeted by TEs insertions using selscan (v. 1. 2. 0a)  (Szpiech & Hernandez 2014)  and Single Nucleotide Polymorphisms (SNPs) as a proxy (Supplementary File S1. 12). We looked for evidences of incomplete   (Voight et al. 2006 ), iHH12 (Garud et al. 2015; Torres et al. 2018) and nSL (Ferrer-Admetlla et al. 2014). iHS and nSL statistics are both aimed to identify incomplete sweeps, where the selected allele is not fixed in the sample, and the main difference is that nSL is more robust to recombination rate variations, which increases the power to detect soft sweeps. iHH12 has been developed for the detection of both hard and soft sweeps, with more power than iHS to detect soft sweeps  (Szpiech & Hernandez 2014). After obtaining results from each statistic, we normalized them using the norm package in 10 frequency bins across each chromosome.
2019) , resulting in 902 TEs. From those, we also discarded TEs belonging to the INE-1 and the LARD families, since those represent very old TE families likely to have reach high frequencies neutrally, ending up with a set of 746 TEs. We considered TEs in this 746 dataset as likely to be enriched for candidate adaptive TEs  (Rech et al. 2019). We then looked whether any of these 746 TEs was nearby a SNP showing significant values at some of the haplotype-based selection test. Finally, for each SNP-TE pair we stablished criteria of 'co-occurrence' by requesting certain number of the strains containing both the SNP allele undergoing a selective sweep and the nearby TE: for TEs present in 5-6 strains we request at least 4 of the strains to contain both the allele undergoing a selective sweep and the nearby TE and for TEs present in ≥7 strains we request the majority of strains to contain both the significant SNP and the nearby TE.
Genomes were sequenced using ONT except MUN-016 and TOM-007 that were sequenced using PacBio. *Indicates the 13 strains used in the construction of the de novo MCTE library. + Indicates the 11 strains used in the comparison of TE annotations using REPET, TIDAL and TEMP. x Indicates the 20 strains used in the cis-eQTL analysis. ( S) Genome assembled using long-read sequencing data of the D. melanogaster reference genome provided in  Solares et al. (2018). Additional information on the strains can be found in Table  S1  and on the sequencing in Tables  S2  and S3.
Anatomy GeneRIF Predicted: list of genes with predicted GeneRIF terms involved in fly´s bodily structures (Gene Reference into Function: https://www. ncbi. nlm. nih. gov/gene/about-generif). 2) Allele LoF Phenotypes from FlyBase: FlyBase's allele phenotypic dataset. Loss of function phenotypes and gene sets with alleles producing those phenotypes. 3) Putative Regulatory miRNAs from DroID:.
Acknowledgments We would like to thank  DrosEU researchers  for sharing with us the strains sequenced in this manuscript (Table  S1 ). We also thank  Miriam Merenciano ,  Anna Ullastres , and  Lain Guio  for helping create the inbred strains. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( H2020-ERC-2014-CoG-647900 ). SR is funded by the  MICINN/FSE/AEI  ( PRE2018-084755 ) and VH was funded by the  Generalitat de Catalunya  ( FI2017_B00468 ). DrosEU is funded by an  ESEB   Special Topic Network award.
Data Availability All scaffolded assemblies and the raw data (long and short read sequencing) have been deposited in NCBI under the BioProject accession PRJNA559813. The VCF file containing SNP callings for 46 D. melanogaster genomes used for testing positive selection evidences is available at http://dx. doi. org/10. 20350/digitalCSIC/13708. Fasta sequences for the D. melanogaster Manually Curated Transposable Elements (MCTE) library are available at http://dx. doi. org/10. 20350/digitalCSIC/13765. Recombination rates according to Fiston-Lavier et al. (2010) and Comeron et al. (2012) for D. melanogaster genome release 6 are available at http://dx. doi. org/10. 20350/digitalCSIC/13766. BED files containing Transposable Element (TE) annotations for 47 Drosophila melanogaster genomes: http://dx.
Anatomy GeneRIF Predicted: list of genes with predicted GeneRIF terms involved in fly´s bodily structures (Gene Reference into Function: https://www. ncbi. nlm. nih. gov/gene/about-generif). 2) Allele LoF Phenotypes from FlyBase: FlyBase's allele phenotypic dataset. Loss of function phenotypes and gene sets with alleles producing those phenotypes. 3) Putative Regulatory miRNAs from DroID: DroID's (http://www. droidb. org/) putative miRNA targets dataset and 4) Transcription Factors from DroID: DroID's (http://www. droidb. org/) transcription factor-gene interactions datasets. We report only terms with an adjusted p-value <0. 05. to analyze enrichments regarding four gene-set libraries: 1).
Thermal treatment and denaturation kinetics The IMFs (800 μL) in glass capillary tubes (8 mm inner diameter and 40 mm length, Waters, USA), were heated in a thermally controlled water bath (Fisherbrand™ Isotemp™ water bath, Thermo Fisher Scientific, Newington, USA) set at different temperatures ranging from 67. 3 • C to 79. 6 • C by intervals of about 2. 5 • C. Model IMFs were not heated above 80 • C because the mechanism of β-LG denaturation change leading to a change of bβ-LG activation energy  (Hillier et al. , 1979; Petit et al. , 2011). For each temperature, the heating ramp (i. e. the time to reach the target temperature) was controlled with a YC-747UD Data Logger Thermometer inserted in a reference tube. The heating ramp was converted into a heating time at target temperature  (Halabi et al.
The NLS regression was performed in Python 3. 7 using the curve_fit function with least-squares method from scipy library  (Jones et al. , 2001). As described by  Bates and Watts (1988) ,  Fogler (1999) , and by  Edgar et al. (2001) , a nonlinear least-squares regression consists in, starting from an initial guess, applying optimization algorithms to estimate parameters that minimize the sum of squared residuals from a given model, represented in equation (  9 ). SSe = ∑ no i=1 (y i -ŷi ) 2 (9) In which, SSe is the sum of squared errors (residuals), i is the index for each observation or estimation, no is the number of experimental points, y i is the experimental observation of index i, and ŷi is the y value calculated by the model for the observation of index i, using estimated parameters.
Moreover, comparing the one-step linearized method to the NLS method it is possible to assess effects of the linearization with respect to time (both estimations being performed in one step). ( C t C 0 ) (1-n) = 1 + (n -1) ⋅ k n1 ⋅ e Ea R ⋅ ( 1 T 1 -1 T ) ⋅ C (n-1) 0 ⋅t (15) Although being linear according to time, the linearized method presented in this study was applied to a dataset considering different temperatures, and since the dependent variable has a nonlinear relationship with temperature, there isn't algebraic solution and so it isn't possible to apply standard linear regression methods. Once again, the solution was obtained using Python 3. 7, but this time applying the minimize function using nonlinear default optimization algorithms from scipy. optimize module  (Jones et al. , 2001).
(2018)  use the inverse of the observation values squared as weights. In this study, weights were proposed by equations (  19 ) and (20). w i = 1 y i ⋅ŷ i ( 19 ) w i = (t i + 0. 25⋅t(T)) s(t(T)) ⋅ no no(T)⋅len(Temps) (20) In which w i is the weight for observation of index i, t i is the time of observation of index i, t(T) is the average time of heating at temperature T, s(t(T)) is the standard deviation of the times of observations at temperature T, no is the total number of observations, no(T) is the number of observations at the given temperature, and len(Temps) is the number of unique temperatures evaluated in the experimental dataset. The weights of equation (  19 ) were chosen to avoid remarkably high relative residuals at low concentrations. The rate of denaturation is lower at lower concentrations, because of the first derivative (equation (  1 )).
So, it is expected that the concentration is more stable at lower values and there's lower local variance. It is important to remark that the WLS regression with this weights function is of easier convergence than using the inverse of squared model values, described by  Kohberger (1980). Weights of equation (  20 ) were chosen after analyzing the samples distribution to avoid sampling bias of having more observations at initial times than at ending times and of having more observations at some temperatures. To perform the WLS, experimental data was fitted to equation (  5 ), applying least_squares function from scipy. optimize module  (Jones et al. , 2001) , to minimize the output of a weighted squared residuals function (equation (  18 )). The parameters variance matrix was calculated by modifying equation (21) for weighted linear regression  (DuMouchel and Duncan, 1983).
Conclusions The assumption of residuals with constant variance in which the nonlinear least-squares method is based proved not to be valid to this dataset, because the variance of residuals was observed to be constant neither versus time nor versus concentration and mean of the residuals was biased not equally distributed between positive and negative values. The linearized method was less effective than NLS and WLS in making good predictions because of uncontrolled distortion of residuals due to the linearization, and, although being performed in one step, it did not result in more accurate estimations when compared to the two-step method. The WLS with the proposed weight functions has been the best method for estimating kinetic parameters, avoiding bias observed in the NLS estimations. Analyzing SSe and confidence intervals of parameters we can infer that the second weight function produced even more accurate results than the first.
However, defining the weight function requires previous knowledge of sampling biases and residuals variance, that are particular to each dataset. This can make it harder to implement the WLS without previously performing another regression method. Moreover, to perform the WLS method (in nonlinear problems) it is necessary to provide initial guesses for the parameters, as for any nonlinear regression. Probably a good approach when performing estimations of kinetic parameters in future studies could be to start by the two-step method, use obtained estimations as initial guesses in the NLS method, analyze residuals distribution, and then define weights and apply WLS.
001, Poisson regression model) (Supplementary Fig. S1 ). Hence, these data imply that transcriptional changes are indeed important for the effect provoked by exogenous rac-GR24 in roots. To discover the transcriptional targets controlling rac-GR24-dependent responses in the root, we carried out an RNA-sequence (RNA-seq) experiment on Col-0 and max2 mutant plants treated with either 1 µM rac-GR24 or 0. 01% (v/v) acetone (mock) for 6 h. This 6-h time point had been selected based on previous studies and marker genes  (Walton et al. 2016). To enrich for statistically and biologically significant differentially expressed genes, we applied two selection criteria, namely a corrected P value (False Discovery Rate, FDR) < 0. 05 and a fold change (FC) cutoff of either 1. 2 or 0. 83. For rac-GR24-treated WT plants, 146 differentially expressed genes were identified, of which 63 were induced and 83 were repressed (Supplementary Dataset S1).
The comparison between max2 and WT seedlings grown under mock conditions resulted in 2,011 differentially expressed genes, of which 882 were upregulated and 1,129 were downregulated (Supplementary Dataset S2). After rac-GR24 treatment of max2 roots, 107 differentially expressed genes were detected, of which 87 were upregulated and 20 downregulated (Supplementary Dataset S3). All unique and overlapping differentially expressed genes (Supplementary Datasets S1-S3) were determined and are presented as UpSet plot (Fig. 1 , Supplementary Datasets S4). The qRT-PCR analysis confirmed differential expression of three out of seven selected candidate genes (AT1G60750, AT3G54530 and AT4G28850), indicating that some rac-GR24-induced transcriptional changes might be independent of MAX2 (Supplementary Fig.
S2 ). When the dataset of genes differentially expressed between max2 and WT under mock conditions was compared with the root proteome analysis carried out for the same genotypes, 57 common candidates were found (Supplementary Table  S1 ). With the exception of three genes, the proteomic profile was similar to the transcriptional one, indicating that the transcriptional changes might contribute to the observed modifications in the proteome  (Walton et al. 2016). Recently, the SL analog, GR24 4DO has been shown to specifically act through D14 to activate the SL signaling in Arabidopsis seedlings and treatment with this compound allowed the identification of 401 SL-responsive genes  (Wang et al. 2020b). As rac-GR24 used here stimulates both the SL and KAR signaling  (Scaffidi et al. 2014 ) and both pathways are known to be involved in root development  (Villaécija-Aguilar et al.
2019) , we compared both datasets to search for SL-specific transcriptional responses in the root. The comparison of genes differentially expressed after rac-GR24 treatment in Col-0, but not in max2 root tissue, and GR24 4DO -treated Col-0 seedlings  (Wang et al. 2020b)   S2 ), suggesting that these genes might be specific for SL responses in the root. Finally, in search for genes that might act in the root downstream of the KAI2 pathway, we compared our data with a recently published transcriptome of red light-grown smax1 smxl2 seedlings  (Bursch et al. 2021). We found 95 genes that were downregulated in max2 while upregulated in smax1 smxl2 seedlings when compared to the wild type (Supplementary Dataset S5). Although very distinct experimental conditions had been used to generate the transcriptomics datasets, the results might give a hint which genes are exclusively involved in SL or KAR/KL pathways in the root.
GR24 Further analysis of possible changes in the gene expression of known components involved in rac-GR24 signaling did not reveal any expression modifications in either D14, KAI2, or MAX2 in any comparison (Supplementary Datasets S1-S3). In contrast, two of the SMAX1/SMXL clade, SMXL2 and SMXL7, were upregulated in the WT after the rac-GR24 treatment and the expression of SMAX1, SMXL2, and SMXL7 under mock conditions was lower in the roots of the max2 mutant than that of WT (Fig. 2A ). To confirm these observations, we evaluated the expression of these genes by qRT-PCR in WT roots after mock treatment and with rac-GR24 for 6 and 24 h. The expression of the three genes was significantly induced after 24 h of rac-GR24 treatment, but no difference was detected after 6 h (Fig. 2B ). Analysis of SMXL2 and SMXL7 GUS reporter lines revealed increased expression after treatment with rac-GR24 for both constructs.
For SMXL7, the blue staining was intensified in the vascular tissue of the hypocotyl and in the older root parts, whereas for SMXL2 in the vascular tissue of the root elongation zone and at the basis of newly formed lateral roots (Supplementary Figs  S3  and S4 ). Next, the expression levels of SMAX1, SMXL2, and SMXL7 were analyzed in the roots of d14, kai2, d14;kai2, and max2 mutants. The expression of SMAX1 and SMXL2 was significantly reduced in kai2, d14;kai2, and max2 in agreement with the signaling pathway in which they act. The transcript level of SMXL7 was slightly, but not significantly reduced in either mutant (Fig. 2C ). Altogether, by means of qRT-PCR and GUS reporter lines, we confirmed the data obtained by RNA-seq, demonstrating that treatment with rac-GR24 increased the expression of SMAX1, SMXL2, and SMXL7.
4B ). The rac-GR24 treatment did not induce levels of quercetin derivatives in the roots of myb12 mutants, whereas the response in the myb11 and myb111 mutants was similar to that of WT plants. No quercetin derivatives accumulated in the myb12 seedlings and other mutants containing the myb12 allele grown under mock conditions (Fig. 4C ). In contrast, the accumulation of kaempferol derivatives (blue fluorescence; Fig. 4 ) in response to rac-GR24 did not obviously differ. Together these data indicate that of the three MYB transcription factors at least MYB12 is required for the rac-GR24-dependent root flavonol response represented by accumulation of quercetin derivatives. Next, the CHS, FLS1, and PAL1 levels upon the rac-GR24 treatment were examined by qRT-PCR in the roots of the myb12. In contrast to WT plants, the expression of CHS and FLS1 was not significantly induced in the mutant.
The involvement of HY5 in rac-GR24-induced flavonol accumulation, LRD reduction and root hair elongation The flavonol production has been described as one of the processes controlled by the key transcription factor HY5  (Stracke et al. 2010b ). In our RNA-seq analysis HY5, but also its homolog HY5 HOMOLOG (HYH) were downregulated in the max2 mutant when compared to the WT (Fig. 6A ). However, the expression of HY5 in the d14, kai2, d14;kai2, and max2 mutants analyzed by qRT-PCR did not significantly differ from the WT (Fig. 6B ), possibly because the changes in RNA-seq dataset were too small. Nonetheless, we tested the hy5-215 (hy5) mutant for various known rac-GR24 root responses. Whereas WT seedlings displayed a significant (P ≤ 0. 001, Poisson regression model) decrease of 25% in LRD upon treatment, this difference in the hy5 mutant was not statistically significant (7,9%; Fig.
Possible role of TMO5/TMO5L1 as downstream transcription factors in the SL pathway in the root Additionally, we searched our RNA-seq dataset for the transcription factors that might be involved in regulation of rac-GR24-mediated changes in the root architecture. To date, no candidate genes further downstream of the SMXL repressors have been identified that control these responses. Interestingly, we found the transcription factor TARGET OF MONOPTEROS5 (TMO5)-LIKE1 (TMO5L1) as the most significantly downregulated gene in the WT upon treatment with rac-GR24 (Supplementary Dataset S1). Furthermore, its homolog, TMO5, also had a reduced expression under the same conditions and both were upregulated in max2 when compared to the WT in mock, hinting at a potential role for both as negative regulators of the rac-GR24 signaling (Fig.