dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIALS AND METHODS All details regarding the experimental part of this work (protein preparation, NMR spectroscopy, and SAXS experiments) are reported in the Supporting Materials and Methods. We only present here the computational tools used for this study. Indeed, it is often emphasized that comparisons and validations of results from simulations against experimental data require robust and accurate methods to back-calculate experimental observables from conformational ensembles. This section particularly surveys several software for the back-calculation of SAXS intensities. It also describes the tested force fields and details of the MD simulations.
Back-calculation of NMR observables Proton and heavy atom chemical shifts (CSs) of a given protein structure can be calculated using various software, such as SHIFTS  (37, 38) , SHIFTX  (39) , SPARTA  (40) , or CAMSHIFT  (41). Then, structure-dependent CSs can be averaged over the protein conformational ensemble and directly compared with NMR measurements. All these software were shown to be Structures of Disordered N-WASP Domain V satisfactorily accurate and sensitive  (42) (43) (44) (45) (46). In this study, we chose the program SHIFTS  (37)  to compute CSs for each structure of N-WASP domain V generated by in silico methods. To identify transient secondary structures in IDPs, it is rather useful to analyze secondary CSs, which are defined as variations of experimental CSs relative to random coil values.
To make direct comparisons with experimental values, the program PALES  (51)  was applied to the sampled structures of N-WASP domain V to compute residue-specific 1 H- 15 N RDC theoretical values. It should be noted that RDCs from NMR measurements were scaled uniformly by a factor of 1/3 to account for an experimental 2H quadrupole splitting of 30 Hz instead of 10 Hz in the back-calculation program.
Back-calculation of SAXS intensities The scattering intensity of a protein, obtained as the scattering curve of the solution of protein minus the scattering of the buffer, not only accounts for the protein electrons but also for those of solvent molecules in excess or in deficiency at the protein surface with respect to the buffer electronic density. Thus, back-calculations of the intensity scattered by a protein first require correctly calculating the volume of solvent excluded by the protein, which is not straightforward. A second difficulty is to accurately estimate the scattering contribution from the protein hydration layer  (52) (53) (54). Two types of approaches were developed to calculate SAXS intensities from atomic structures: implicit-solvent and explicit-solvent methods (see Table  1  of (53) for a list of the main software).
Among the recent freely available explicit-solvent SAXS software, we chose the program WAXSiS  (56, 57)  as a reference, similarly to two other recent studies  (54, 58). This approach performs short MD simulations (15-250 ps) of explicit water molecules in the solute hydration layer (of 7 A ˚thickness). Protein scattering intensities are then calculated by subtracting the buffer scattering, which is determined from MD simulations of pure solvent. Regarding implicit-solvent approaches, we applied in this work the most used software and tested several parameters for the hydration layer density: 1) CRYSOL  (59). The scattering contribution of the solute hydration layer can be tuned using a parameter Dr, which represents the excess of electron density in the hydration layer compared to the bulk (whose density is fixed to 0.
By default, c1 ¼ c2 ¼ 1. 0 and c3 ¼ 0. 0. We tested several combinations of these parameters with 0. 0 # c1 # 1. 0, 0. 6 # c2 # 1. 0, and 0. 0 # c3 # 1. 0. It should be noted that CRYSOL3 also allows one to choose either spherical harmonics or a cubic method to estimate the solvent excluded volume  (63, 64). These two options were included in our benchmark. 4) Pepsi-SAXS  (55). In its new version (0. 8) (https://team. inria. fr/nano-d/ software/pepsi-saxs), Pepsi-SAXS allows us to adjust the hydration shell contrast in percentage of the bulk density (fixed to 0. 334 e/A ˚3). We assessed several values of this new parameter between 1. 0 and 5. 0% (default value), corresponding to Dr ranging from 0. 0033 to 0. 0167 e/A ˚3. Comparisons between WAXSiS and implicit-solvent approaches were performed by calculating and averaging SAXS intensities over a limited number of N-WASP domain V structures representative of its conformational ensemble.
013 e/A ˚3 instead of 0. 03 e/A ˚3. The FoXS program provides best results with the parameter c2 ¼ 0. 1 (Dr ¼ 0. 001 e/A ˚3) compared to the default value c2 ¼ 0. 0. The best fit observed with CRYSOL3 is obtained by using the cubic method with the parameters (c1, c2, c3) ¼ (1. 0, 0. 7, 0. 0). Finally, Pepsi-SAXS yielded the best agreement, with an excess density of 2. 2% (Dr ¼ 0. 0073 e/A ˚3) instead of 5%. To check whether the above values depend on the number of conformations  (65)  used in the hydration layer density optimization, we computed c 2 values between WAXSiS and the two software CRYSOL and Pepsi-SAXS on various pools of size ranging from 20 to 100 conformations. (For building the pool of 100 structures, we additionally selected the 20 frames at time t ¼ 20 ns of the 20 MD trajectories.
Nevertheless, for both CRYSOL and Pepsi-SAXS, optimization of the hydration layer density reduces the disagreement with the explicit-solvent SAXS calculations. Comparing the four tested implicit-solvent approaches, our results on N-WASP domain V indicate that an excellent agreement with WAXSiS is obtained by using CRYSOL with Dr ¼ 0. 013 e/A ˚3, the recent software Pepsi-SAXS with a hydration layer contrast of 2. 2%, or CRYSOL3 with parameters c1 ¼ 1. 0, c2 ¼ 0. 7, and c3 ¼ 0. 0. Besides, we observed that Pepsi-SAXS was significantly faster than the other software, notably CRYSOL (0. 01 s for Pepsi-SAXS vs. 2 s for CRYSOL on a single structure of N-WASP domain V). More importantly, Pepsi-SAXS takes into account solvent densities in protein cavities and concave surfaces in a better way than the original version of CRYSOL  (55, 59).
Protein force fields and water models Historically, protein force fields were developed to simulate the conformational dynamics of folded proteins, which generally have their nonpolar residues buried in their core and protected from solvent. Recently, several force fields were improved to properly generate conformational ensembles of IDPs, in which nonpolar residues are frequently exposed to solvent. This is generally achieved by using a four-site water model, which better accounts for the electric properties of water molecules, and by accentuating the depth of the solute-solvent Lennard-Jones (LJ) potentials to better solvate nonpolar residues. This improvement was first reported by Best et al. , who proposed to rescale by a factor g ¼ 1. 1 the LJ parameters ε Oi between the water oxygen FIGURE  2  Comparison between SAXS intensity back-calculation software using implicit-solvent models versus WAXSiS, which uses an explicit-solvent approach.
To minimize the possible bias induced by our selection of initial conformations, we only kept the last 80 ns of each MD simulation and collected data every 40 ps, yielding ensembles of 40,000 structures for subsequent analyses. Most of the conformational analyses were performed using GROMACS tools. Nevertheless, it should be emphasized that protein radii of gyration were not calculated with the GROMACS tool gmx gyrate but from the SAXS intensities using the Guinier approximation Log½IðqÞ=Ið0Þ ¼ ÀR 2 g q 2 =3 when q / 0 (78). Finally, the software STRIDE (79) was used to assign secondary structure elements to each residue of each protein conformation, based on hydrogen bond criteria and backbone dihedral angle values. Then, outputs from STRIDE were used to compute the probabilities for each residue to be in a-helix or b-strand within each conformational ensemble.
FM In this study, we compared N-WASP domain V conformational ensembles derived from MD simulations against those generated by a much faster statistical coil generator, FM  (80) , which is very popular within the NMR and SAXS communities. From a primary sequence, FM constructs protein three-dimensional structures by linking consecutive residues with dihedral angles f/j randomly taken from a database of residues with only loop conformations  (81, 82). Residue-specific hard spheres located at Cb atoms (Ca for Gly)  (83)  are used to avoid steric clashes between amino acids (if a steric clash occurs between a newly added residue and the previously built polypeptide chain, another pair of dihedral angles f/j is randomly chosen). No attractive potentials are taken into account during the generation process.
Optionally, restrained conformational ensembles can be produced by specifying, in the FM inputs, fixed SSPs for given fragments of the sequence (for these residues, the dihedral angles f/j are constrained to adopt standard a or b values)  (80). It could be noted that FM only generates structures of the protein backbone. Thus, for each protein conformation, side chains were subsequently added using the SCWRL4 program  (84). Finally, the complete atomic structures were generated by adding the lacking hydrogen atoms with the GROMACS tools pdb2gmx  (66). In this work, we used FM to generate two different pools of 40,000 conformations of N-WASP domain V, one without constrained SSP and the other one by specifying two a-helical propensities of 13 and 31% for segments 10-19 and 39-47, respectively, as suggested by NMR data (see below Results).
Conformational subensemble selection When experimental data on the studied IDP are available, it is possible to filter a given conformational ensemble and select a subensemble that better agrees with experiments  (29). In this work, we used the program GAJOE from the suite EOM  (85, 86)  for selecting conformational subensembles of N-WASP domain V that better fit the SAXS intensities. It should be noted that GAJOE was not employed here in the conventional way: we did not search for a subensemble with a minimal number of conformations, as GAJOE usually does (maximal parsimony principle). We rather wanted to have a large subset of structures to account for the diversity of the disordered protein conformations (maximal entropy approach). Thus, we asked GAJOE to perform 100 runs of genetic algorithm optimization of the fit with experiments and to save a subset of 50 optimal structures at the end of each run, yielding a final subensemble of 5000 selected conformations.
NMR information on local secondary structures We first compared residue-specific secondary propensity scores computed by the SSP program  (49) , which combines CSs of Ca, Cb, CO, and backbone N atoms either measured by NMR or calculated by the program SHIFTS  (37). This score indicates protein regions having a propensity to form a-helix (SSP score >0) or b-strand/coil (SSP score <0). This information on protein local structures can also be retrieved from residue-specific N-H either measured by NMR or calculated by PALES  (51). As shown in Fig. 4 , A and C, SSP score and RDC profiles averaged over the FM_ssp ensemble are in excellent agreement with NMR data, which is expected because this conformational ensemble was generated with appropriate input restraints to best fit the NMR observations.
These shorter helices could represent intermediate states toward the more extended helices indicated by NMR data and FM_ssp ensemble but that were not sampled by MD simulations because of their arguably limited duration. This could explain the discrepancies between NMR measurements and MD estimations of SSP scores and RDCs. Our simulations also detected two segments  (31-34 and 46-51)  flanking the helical region 37-43 with significant propensities for a-helix higher than 10% (Fig. 4 F ). These two additional helical segments were not found around the a-helix of the first WH2 motif, which could contribute to differentiate the conformation and binding affinity for actin of the two N-WASP domain V WH2 motifs. Regarding local extended structures, the two ensembles generated by FM have nearly no conformations with residues in b-strand (Fig.
SAXS information on global shape Primary data collected by SAXS experiments are plotted in Fig. 5, A  and B. Kratky curves present typical profiles of a fully disordered protein (Fig. 5 , C and D)  (87, 88). From experimental data, the average radius of gyration R g of a conformational ensemble can be estimated by using the Guinier approximation, Log½IðqÞ=Ið0Þ ¼ À R 2 g q 2 =3, which is valid for very small angles  (78). A linear fit of the Log I(q) curve for qR g below 0. 8, using the Primus package  (89) , yielded an experimental average radius of gyration R g ¼ 24. 30 5 0. 24 A ˚for N-WASP domain V. It could be noted that this value is quite close to the value expected from Flory's theory, which relates the average radius of gyration of polymer chains to their number of monomeric units: R g ¼ R 0 N v (90).
Conformational subensemble selection with GAJOE Previous comparisons between experimental and in silico conformations of N-WASP domain V showed that both FM_ssp and MD_A03ws approaches generated ensembles in fairly good agreement with both NMR and SAXS data. This agreement can be improved by selecting conformational subensembles that minimize the c 2 values between back-calculated and experimental data, particularly SAXS intensities. To this aim, we performed 100 runs of genetic algorithm optimization with the program GAJOE  (85, 86) , yielding 5000 conformations of N-WASP domain V that were subsequently analyzed. It should be noted first that conformational subensemble selections on the criterion of SAXS intensity marginally changed the SSP profiles and their agreement with NMRderived data (Fig.
Reduced residuals D/s ¼ [I calc (q) À I exp (q)]/s exp (q). Probability of the N-WASP domain V radius of gyration (E and F) computed for the F_nossp, FM_ssp, MD_C36mm, and MD_A03ws conformational ensembles is shown. Vertical black solid and colored dashed lines indicate radius of gyration measured by experiments and mean values found in simulations, respectively. To see this figure in color, go online. Structures of Disordered N-WASP Domain V helicity observed after GAJOE selection is probably an artifact of the slightly imperfect prediction of SAXS profiles by the conformational ensembles. This could be due to either a limited accuracy of the SAXS calculation software for both compact and extended structures or to the non-fully exhaustive conformational samplings, highlighting the difficulties to provide a conformational ensemble in perfect agreement with both RDC measurements and SAXS intensities.
ACKNOWLEDGMENTS We thank the staff of the Swing beamline at the SOLEIL synchrotron for assistance during SAXS experiments. We are grateful to  J. Hub  and collaborators for fruitful explanations about WAXSiS software. This work is supported by the '' IDI 2016 '' project funded by the  IDEX Paris-Saclay  ( ANR-11-IDEX-0003-02 ). MD simulations were performed using HPC resources from  GENCI-CINES  (Grant  A0040710415 ).
The corresponding ITAG4. 0 annotation has 4,794 novel genes along with 29,281 genes preserved from ITAG2. 4. Most of the updated genes have extensions in the 5' and 3' UTRs resulting in doubling of annotated UTRs per gene. The genome and annotation can be accessed using SGN through BLAST database, Pathway database (SolCyc), Apollo, JBrowse genome browser and FTP available at https://solgenomics. net.
The PacBio based SL4. 0 genome described here contains novel sequence information with fewer gaps compared to earlier versions. The availability of new and comprehensive expression profiles of tomato from a range of tissues provides us a rich resource for annotating the new genome. We collected publicly available RNA-Seq datasets generated from a variety of tissues and conditions of wild-type tomato plants (Supp Table  3 ). These RNA-Seq datasets were enriched in fruit datasets, as fruit development is a major focus of tomato research. RNA-Seq data were selected to represent root, leaf, flower and callus along with various fruit developmental stages. Other important datasets included NBS-LRR gene enriched RNA-Seq and 5' and 3' enriched RNA-Seq data. Migrating legacy annotation from ITAG2. 4 ITAG2.
Ab-initio gene predictors Augustus  (Hoff and Stanke 2019)  and SNAP  (Li et al. 2007 ) enabled discovery of the novel genes. These gene predictors were trained on the reference genome assembly with all available evidence sources including expression and orthology data. The trained gene predictors helped to identify novel genes along with minor changes to the legacy gene models based on RNA-Seq support. The downstream analysis of annotations involved removing newly identified repeat genes based on similarity with the known repeats and incorporating manually curated genes from Apollo annotation editor (http://apollo. sgn. cornell. edu/apollo/) (Lee et al. 2013). This resulted in 34,075 protein coding gene annotation set ITAG4. 0. Automated Assignment of Human Readable Descriptions (AHRD) (https://github.
com/groupschoof/AHRD) using various plant databases predicted functions of 29,532 ITAG4. 0 genes. ITAG4. 0 has 4,794 novel genes (Supp Table  4 ) along with 29,281 genes preserved from ITAG2. 4. Many of the preserved genes between ITAG2. 4 and ITAG4. 0 have been updated (21,962 out of 29,281) in the current annotation (Supp Table  5 ). Most of the updated genes have extensions in the 5' and 3' UTRs resulting in doubling of UTRs per gene in ITAG4. 0 compared to ITAG2. 40. The extended UTRs in ITAG4. 0 will allow better mapping of short read RNA-Seq data, especially from the cost-effective 3' RNA-Seq protocol  (Tandonnet and Torres 2017) , giving a more accurate expression quantification.
Long non-coding RNA identification Long non-coding RNAs or lncRNA are relatively newly described class of RNAs and thought to be involved in the regulation of many biological processes. LncRNA are defined as transcripts with very low coding potential compared with known protein-coding genes and with length longer than 200 nucleotides. During annotation of protein-coding genes, we have used diverse expression data from Iso-Seq and RNA-seq. The diverse expression data enabled identification of lncRNA's. Mikado transcriptome discussed above was filtered for known protein-coding genes. The remaining transcripts were the basis for identification of lncRNAs using FEELnc pipeline  (Wucher et al. 2017). In total 5,874 lncRNA genes were predicted with 6,694 alternately spliced isoforms.
Conclusion Assembly of long read PacBio sequences and scaffolding with Hi-C chromatin capture has yielded a high-quality reference genome assembly of tomato, with major improvement in the contiguity of the genome compared to previous genome builds that were based primarily on 454 sequences. Characterization of repetitive structure of the genome shows that we have identified 10% more repeats compared to previous assemblies. There are numerous improvements in the structure of protein-coding genes in ITAG4. 0 annotation Full-length cDNA sequencing using PacBio Iso-Seq has contributed to the accuracy of the current genome annotation. But manual genome curation is still the gold standard for the genome annotation. We provide Apollo annotation editor as a tool to curate the structure and function of genes and the locus editor to add publications and metadata for genes.
Homopolymer correction Sequences produced by 454 pyrosequencing technology are prone to homopolymer errors  (Huse et al. 2007; Gilles et al. 2011). We used the Snippy haploid variant calling tool (https://github. com/tseemann/snippy) to iteratively correct insertion and deletion errors caused by runs of three or more homopolymers. The Illumina short reads provided by Syngenta were used evidence. Bionano Optical Maps Genome maps were generated for Solanum lycopersicum Heinz var 1706 by purifying highmolecular weight DNA by flow cytometry embedded in agarose plugs, nicked and labelled and finally loaded in the Irys system. The DNA backbone was stained using YOYO-1 and locations of fluorescent labels at restriction sites for Nb. BbvC1 and Nt. BspQI along each molecule were detected by image software in the Irys system.
The minimum length was 12,753 bp, maximum length was 28,741,570 bp and the median length was 96,901 bp. The contigs were error corrected with 63X PacBio reads iteratively with the Arrow pipeline (https://github. com/PacificBiosciences/GenomicConsensus) for two rounds. The PacBio error corrected contigs were then further error corrected with accurate Illumina short reads with 100X coverage of the genome using Pilon  (Walker et al. 2014). Pilon was run iteratively for two rounds with the following parameters: --threads 32 --changes --fix bases --diploid --mindepth 0. 6. We used the minimum depth of coverage required to correct a base as 0. 6 to capture the major allele at that locus in case it was heterozygous for this diploid genome. Hi-C scaffolding Young seedlings were sent to Dovetail Genomics (USA) who generated a Hi-C library and sequenced it with Illumina.
Iso-Seq sequencing and data analysis Breaker and mature green stages of tomato fruit were harvested and RNA was extracted for sequencing with PacBio SMRT technology. High quality full-length transcripts obtained from SMRT analysis pipeline were mapped to SL4. 0 genome assembly using GMAP  (Wu and Watanabe 2005)  and collapsed redundant isoforms using Cupcake ToFU pipeline. Genome mapping based clustering of full-length transcripts resulted in 17,112 genes and 110,577 isoforms.
RNA-Seq expression data Publicly available RNA-Seq data was obtained from NCBI SRA using SRA tools (version 2. 8. 2-1). RNA-Seq libraries were filtered for transgenic or mutant line expression data. Data sets with less than 80% mapping rate were not used. Other RNA-Seq data sources include RenSeq enriched for NB-LRR  (Andolfo et al. 2014 ) and untranslated region (UTR) enriched expression data  (Zhong et al. 2013; Tzfadia et al. 2018). All the RNA-Seq data sets were mapped on the repeat masked SL4. 0 genome and a transcriptome was assembled. In total, we mapped more than 12 billion RNA-Seq reads to generate the genome guided transcriptome with a mapping rate of approximately 85%. The mapping was performed using HISAT2  (Kim, Langmead, and Salzberg 2015)  and the transcriptome was generated using StringTie  (Pertea et al.
Transcriptome assembly Iso-Seq and RNA-Seq based transcriptomes were combined and picked transcripts using Mikado  (Venturini et al. 2018). Above described Iso-Seq transcriptome and StringTie transcriptomes were provided to Mikado along with reliable splice junctions obtained by Portcullis  (Mapleson et al. 2018). Blast alignments to SwissProt plant proteins ("SwissProt Database" 2008) and Pfam domains (El-Gebali et al. 2019) identified using hmmscan  (Eddy 1998)  were also provided to Mikado pipeline. Based on homology and identification of protein domains, transcripts were classified as protein-coding and non-coding genes. Overall, Mikado generated 32,998 proteincoding genes and 41,708 transcripts. Protein-coding transcripts identified based on expression data through Mikado were integrated in MAKER annotation pipeline.
ITAG4. 0 protein-coding gene annotation Gene identification was performed on the SL4. 0 genome after masking repeats. Annotation pipeline MAKER (version 3. 1)  (Campbell, Holt, et al. 2014)  was employed iteratively to improve legacy gene models and to predict novel gene models. For protein evidence, curated proteins from SwissProt were provided to MAKER annotation pipeline. For transcript evidence, Mikado generated transcriptome was provided to MAKER which comprised of Iso-Seq data and publically available RNA-Seq data. ITAG2. 40 transcripts were mapped to the new SL4. 0 reference genome assembly using GMAP (version: gmap-2017-06-20)  (Wu and Watanabe 2005). Mapped genes models were updated (parameter: pred_gff) using available expression data including Iso-Seq and RNA-Seq based transcriptome before models from other gene prediction methods were added.