dataesr/scientific-paragraphs-categorization huggingface dataset:
We additionally quantified the strategy adopted by A. thaliana in response to the neighboring plants by calculating the ratio HD as the height of the first flower on the rosette diameter (i. e. , H1F/DIAM). The values of HD below and above 1 would correspond to an aggressive and escape strategy, respectively  (Baron et al. , 2015). Although all the plants bolted at the end of the experiment (i. e. , 87 days after sowing), the plants that had not flowered at this time (i. e. , 1. 15%) were assigned a flowering date value of 87. The values of H1F and HD were therefore not available for these plants. All the raw phenotypic data are available in Supplementary Dataset 1. For a given treatment, the mean H1F genotypic value among the accessions is represented by a red dot. h 2 : narrow-sense heritability estimates.
Genome-Wide Association Mapping Combined With a Local Score Approach The effects of population structure on phenotype-genotype associations have been demonstrated to be limited in the TOU-A population  (Brachi et al. , 2013; Baron et al. , 2015). Nevertheless, GWA mapping was run using a mixed-model approach implemented in the software genome-wide efficient mixed model association (GEMMA,  Zhou and Stephens, 2012). This model includes a genetic kinship matrix as a covariate to control for the effect of the demographic history of the TOU-A population. In this study, we discarded SNPs with more than 17 missing values across the 91 accessions. In addition, because rare alleles may lead to an inflation of low p-values  (Atwell et al. , 2010; Brachi et al. , 2010) , we only considered the SNPs with a minor allele relative frequency (MARF) > 10%, leaving us with 630,234 SNPs.
Identification of Candidate Genes Associated With Response to the Monospecific and Plurispecific Interactions To identify the candidate genes associated with natural variation of plant-plant interactions, we retrieved genes around the significant zones identified by the local score approach. Using a custom script, we selected all genes inside the zones as well as the first gene upstream and the first gene downstream of these zones. The TAIR 10 database 2 was used as our reference, leaving us with 4,524 unique candidate genes (N BIOMASS = 1,538; N DIAM = 1,008; N H1F = 1,559; and N HD = 1,572; Supplementary Figure  2 ). In order to identify significantly over-represented biological processes (P < 0. 05) in response to the different neighborhoods for the different traits, we submitted the candidate gene lists to the classification superviewer tool ( 3   Provart and Zhu, 2003)  using the MAPMAN classification.
From these QTLs, we retrieved 4,524 unique genes located within and in the vicinity of 1 kb of the significant QTL regions detected for each "phenotypic trait × treatment" (Supplementary Dataset 2). In agreement with the values of genetic correlations between these four traits (Supplementary Figure  3 ), a large fraction of candidate genes was specific to a given treatment (BIOMASS: 77. 4%, DIAM: 63. 4%, H1F: 77. 2%, and HD: 70. 6%), suggesting that the genetic bases are largely distinct among the 12 treatments (Figure  5A  and Supplementary Figure  4 ). Therefore, in this study, the genetic architecture underlying intraspecific variability of the competitive response largely depends on both the composition and assemblage of the neighborhood of A. thaliana.
Identification of Enriched Biological Processes Across the 48 "phenotypic trait × treatment" combinations, we identified 139 significantly over-represented gene ontology (GO, MapMan functional annotation) terms, which belong to 25 unique GO processes (Supplementary Dataset 3). Interestingly, the RNA related processes correspond to the most abundant GO term over-represented found in 28 "phenotypic trait × treatment" combinations, with eight treatments for BIOMASS, seven treatments for DIAM and HD, and six treatments for H1F (Figure  5 , Supplementary Figure  5 , and Supplementary Dataset 3). The second most represented GO terms are transport and minor CHO metabolism processes with 13 and 12 "phenotypic trait × treatment" combinations, respectively (Figure  5 , Supplementary Figure  5 , and Supplementary Dataset 3), but with different distribution across the traits.
On one hand, the transport processes were represented in the four traits, while minor CHO metabolism processes were not found for BIOMASS in any of the 12 treatments. Conversely, the C1-metabolism processes were mostly enriched for BIOMASS in six treatments out of the seven "phenotypic trait × treatment" combinations identified for these processes (Supplementary Figure  5 ). The remaining enriched MapMan processes are related to tetrapyrrole synthesis, miscellaneous, protein, redox, secondary metabolism, stress, lipid metabolism, hormone metabolism, cell, photosystem, signaling, and not assigned genes (Figure  5 , Supplementary Figure  5 , and Supplementary Dataset 3).
These transcription factors are known to regulate multiple developmental processes, such as stress and light response  (Bürglin and Affolter, 2016; Guo et al. , 2021) , and might exert a crucial role in promoting plant adaptation to diverse plant environments. The second major over-represented biological process is related to the transport functions. Interestingly, 14 out of the 79 transport related proteins, identified in the seven treatments for ratio HD corresponding to the height of the first flower divided by rosette diameter, correspond to ABC (ATP-binding cassette) transporter proteins (Supplementary Dataset 2). Although none of them have been functionally characterized, these transporters have been recently proposed as key players of plant adaptation to their abiotic or biotic environment  (Hwang et al.
Dynamic light scattering (DLS) Measurements of the mean hydrodynamic radius of the Plin4-oil droplets by dynamic light scattering were performed on a sample taken from the middle of the tube, avoiding any unreacted oil that remained at the top of the emulsion, at least 3 hr after vortexing to prevent the interference of gas bubbles with the measurement. Subsequent samples at later time points were removed in the same manner without any additional mixing. Emulsion samples were diluted 100-fold in freshly degassed HKM buffer with 1 mM DTT. Measurements were performed on a Zetasizer Nano ZS machine (Malvern) at 25˚C, and data were processed using the CONTIN method.
In this way, the flow in the main inlet channel could be stopped without significantly affecting the meniscus of oil, and the inlet vial with buffer could be exchanged by a vial with fluorescent protein solution (0. 1 mg/ml in HKM buffer, mixed at a ratio 10:1 for unlabeled vs Alexa488-labeled protein). The sample volume in the vial was approximately 400 ml. The flow in the main inlet channel was then resumed and the diffusion of the protein from the main inlet channel into the side channel and its adsorption onto oil meniscus was monitored by time-lapse confocal microscopy for up to 30 min at a rate of 1 frame every 3 s (ECLIPSE TE2000-E, Plan Fluor 40x objective, EZC1 software, Nikon). Finally, the flow in the main inlet channel was stopped again, the inlet vial exchanged by a vial with buffer, and the main inlet channel was rinsed while monitoring the diffusion of the protein from the side channel.
For competition experiments between Plin4 12mer and 4T > S mutant, proteins were mixed at a mass ratio 50:1 for unlabeled to labeled protein, Plin4 12mer: Plin4 12mer-Alex488 or 4T > S: Plin4 12mer-Alex488, with a total protein concentration of 0. 1 mg/ml. All experiments were conducted at room temperature. Between experiments, glass chips were regenerated by copious washing with 3% SDS at 50˚C, followed by distilled water, 3% TFD4 at 50˚C, distilled water and finally dried by air (except for the measurements of the surface tension, where inhomogeneous glass surface was needed to increase the contact angle hysteresis). Image analysis was performed with ImageJ/Fiji  (Schindelin et al. , 2012)  and Matlab.
When imaging emulsions, images were acquired in 10 to 15 z-sections of 0. 2 mm were taken. For imaging HeLa cells and quantification of LD-to-PM signal ratio in yeast, we used an LSM 780 confocal microscope (Zeiss) with a x63/1. 4 oil objective and a PMT GaAsP camera, driven by ZEN software. Images were processed with ImageJ and prepared for figures with Canvas Draw (canvas X).
Fluorescence recovery after photobleaching (FRAP) FRAP assays in vitro were performed on freshly prepared fluorescent emulsions with Alex488-labeled proteins on glass slides using the CSU-X1 spinning disc microscope and 100x objective, bleaching laser with a wavelength of 473 nm and iLas software controlled by Metamorph. Several circular areas of 25 Â 25 pixels were bleached in each field (828 Â 960 pixels), either on oil particles or in surrounding solution. The following FRAP time-course was used: six images pre-bleach, then bleach followed by 10 s of 1 image/s, 60 s of 1 image/10 s, and finally 600 s of 1 image/30 s (or until the loss of focus). Fluorescence of the bleached area at each time point was normalized to the average fluorescence before bleaching. Data was processed using Excel.
For FRAP assays in yeast cells, a circular area of 15 Â 15 pixels in a cell expressing a GFP-fusion protein was bleached, either on the LDs or on the plasma membrane. Five images were taken before bleaching, followed by a post-bleach time-course: 15 s of 1 image/s, 60 s of 1 image/5 s, and ~200 s of 1 image/20 s. Background fluorescence outside the cell was subtracted from the bleached area and the signal was normalized to the whole cell signal for each time-point. Data was processed with Excel and plotted using SigmaPlot (Systat Software). FRAP assays in Drosophila S2 cells expressing Plin4 12mer-GFP were performed as for yeast, except that three circular areas of 15 Â 15 pixels containing isolated LDs were selected per cell. The following FRAP time-course was used: five images pre-bleach, then bleach, followed by 30 s of 1 image/s, 60 s of 1 image/5 s, and finally ~200 s of 1 image/20 s.
Data were analysed in Excel and plotted with KaleidaGraph (Synergy software). To determine the fraction of LDs in HeLa cells that were positive for transfected fluorescent protein, a single z-section that contained the most LDs in a cell was first selected. All LDs in the selected cell section were identified in the green (Bodipy dye) channel using the 'Analyze particle' plug-in. LDs positive for fluorescent protein were then identified by determining a threshold value for the red fluorescent signal (mCherry-protein fusion), 1. 4x above average cellular fluorescence, and counting all LDs with fluorescence above this threshold. This number was divided by the total LD number to calculate the fraction of LDs in one cell section positive for protein. Data was processed in Microsoft Excel and plotted using SigmaPlot.
Acknowledgements We thank  C La Torre Garay ,  M Tuljak ,  V Countremoulins  and  N Joly  for technical help, and  R Schneiter  and  S Le  ´on for plasmids. We acknowledge the  IJM ImagoSeine facility , member of  IBiSA  and the  France-BioImaging infrastructure  ( ANR-10-INBS-04 ), and  L Bousset ,  V Albane  `se,  R Gautier ,  J Snoj ,  N Mejhert ,  C Jackson  and  Jackson-Verbavatz  team for helpful discussions and comments on the manuscript. This work was supported by the  CNRS , including a  CNRS PICS  grant (# 214454 ), the  Slovenian Research Agency  (research core funding No. P1-0055 ), and  ERC Synergy  grant # 856404. MG-A was supported by PhD fellowships from the  French 'Ministere de l'Education National, de l'Enseignement Superieur de la Recherche' and Fondation ARC pour la recherche sur le cancer  ( DOC20190509052 ).
The bio. tools registry is a main catalogue of computational tools in the life sciences. More than 17 000 tools have been registered by the international bioinformatics community. The bio. tools metadata schema includes semantic annotations of tool functions, that is, formal descriptions of tools' data types, formats, and operations with terms from the EDAM bioinformatics ontology. Such annotations enable the automated composition of tools into multistep pipelines or workflows. In this Technical Note, we revisit a previous case study on the automated composition of proteomics workflows. We use the same four workflow scenarios but instead of using a small set of tools with carefully handcrafted annotations, we explore workflows directly on bio. tools. We use the Automated Pipeline Explorer (APE), a reimplementation and extension of the workflow composition method previously used.
Automated Pipeline Explorer (APE) APE  19, 20  is a command line tool and Java API for the automated exploration of possible computational pipelines (scientific workflows) from large collections of computational tools. We use it as a standalone tool in this study, but in its capacity as an API, it could also be used as a component for workflow exploration within a workflow management system such as KNIME  21  or Galaxy. 22 Furthermore, APE can export the obtained workflows in Common Workflow Language (CWL, https://w3id. org/cwl/) format to facilitate integration with other systems. APE is open-source software and is available on GitHub (https://github. com/sanctuuary/APE). The workflow exploration algorithm used by APE is a variation and extension of the Semantic Linear Time Logic (SLTL)-based process synthesis approach originally introduced by Steffen et al.
tools). The domain setup process is described in further detail as follows. The workflow specifications we provided to APE comprised the available inputs and intended outputs (data type and format, again in EDAM terms) and, in some cases, additional constraints (provided through open text templates that internally translate to SLTL formulas). On this basis, APE computes possible workflows. Intuitively, this is a two-step process: 1. The tools' input/output annotations imply a network of technically possible tool sequences, which constitutes the search space for the workflow exploration. 2. In this search space, APE looks for paths that match the concrete workflow specification (from workflow inputs to outputs, while respecting additional constraints). Technically, this process is implemented as temporal logicbased program synthesis with iterative deepening; that is, the algorithm explores the workflows requiring the least amount of steps to solve the given problem first.
Domain Models In our previous study,  11  the domain model comprised 26 tools (mostly but not exclusively from the ms-utils. org collection of mass-spectrometry data analysis tools) that we annotated in a CSV file with input and output data type and formats as well as operations using terms from the EDAM ontology. In this study, we fetched the corresponding tool annotations directly from the bio. tools registry via its REST API. The bio. tools annotation schema also uses the EDAM ontology as reference vocabulary, but in contrast with our previous tabular annotation format, it allows for the annotation of multiple inputs and outputs per tool. Thus we now work with significantly more comprehensive descriptions of the available tools' functionality. For this study, we worked with three different domain models: 1.
The quality of the domain model determines the quality of the workflows obtained through automated exploration. bio. tools contains thousands of tools, annotated by a diverse group of contributors. Not surprisingly, this leads to mixed levels of annotation quality. In particular, many of the tool annotations lack input or output definitions, specify them vaguely or incompletely, or use outdated EDAM references. Therefore, we discarded annotations that: • do not specify an input, as these tools can be used at any step of the workflow and typically introduce unnecessary new data • do not specify an output (typically these are interactive tools), as they do not contribute to solving a data analysis problem • miss a data type or format specification, as these are incomplete annotations • reference deprecated EDAM data type or format terms, as they are not part of the domain taxonomy anymore Note that there are also many tool annotations that reference deprecated EDAM operation terms; however, we classify those as noncritical annotation errors and allow such tools.
The identification and localization of post-translational modifications in a phosphoproteomic study. Table  2  summarizes the corresponding workflow specifications, which are the input for APE. Because EDAM and bio. tools have evolved since the previous case study, we revised the workflow specifications to match the now available terms. Concretely, we generalized the output specifications in the first and the fourth use cases from the expected data types to their parent classes, as none of the annotated tools used the originally specified types as input/output anymore. In the first use case, we substituted Amino acid index (hydropathy) with Amino acid property, whereas in the fourth use case, we replaced Gene expression prof ile by Expression data. Similarly, we updated some of the workflow constraints.
Currently, this requires formulating such constraints per individual tool, so we are considering ways of adding this as a more convenient configuration option to APE. When we explore workflows for the same specification but with the extended and full sets of tools, the number of results significantly increases. As Figure  3  indicates, the additional results comprise several new and sometimes surprising workflow suggestions, such as the combination of using MZmine before Mascot Server. However, there are also workflow suggestions that are less sensible. For example, some workflows start with the tool CompassXPort, although it cannot read Thermo RAW files. The reason is that CompassXPort is annotated in bio. tools to read Mass spectrometry data format files, which is the parent term for 30 specific file formats including Thermo RAW.
CompassXPort cannot read Thermo RAW, but this cannot be inferred when using general annotations including parent terms. Ideally, CompassXPort would be annotated in bio. tools with a precise list of accepted input formats and not their parent term. To work around this with the current version of APE, a constraint can be added that, for example, excludes CompassXPort from the exploration. Alternatively, one could restrict the domain model to include only tools described by sufficiently specific file formats. However, given the current state of tool annotations, such a restriction is likely to strongly decrease the domain model size. To solve such problems more generally, we are currently investigating possibilities for extending APE with new configuration options or heuristics for data format handling.
Like CompassXPort, it is annotated as using the general Mass spectrometry data format as input, whereas it actually accepts only some of these formats (a table containing peptide identifications), so the problem is similar to the one previously described. The other suggestions are then actually meaningless extensions of this first workflow. In the constrained case, no workflows are returned, as the constraints try to enforce the aforementioned enrichment analysis tools, which cannot be used due to the missing shims. The unconstrained exploration with the full domain model yields the same results as that with the extended model, with the exception of the ThermoRawFileParser tool for the initial file conversion. As in the previous constrained cases, the constrained exploration with the full domain model resulted in no solutions.
■ CONCLUSIONS Annotating computational tools with ontologically defined terms describing their operations, data types, and formats enables their automated composition into tentatively viable workflows. Providing such annotations (using terms from EDAM ontology) is one of the main goals of the bio. tools registry, which has become a main catalogue of computational tools in the life sciences. For this Technical Note, we applied APE to the bio. tools registry and revisited workflow use cases from a previous proof-of-concept study in proteomics. Our results show that this combination can provide an effective means for searching for possible purpose-specific workflows in large collections of semantically annotated tools. We see this as another milestone on the long way toward a workflow exploration system that would ultimately be as comprehensive, reliable, and easy to use as contemporary route planners.
e. , for format conversion tools that change the data format but maintain the data type) and quantified constraints (such as constraints that should hold for all tools in a set individually, e. g. , when using constraints to avoid the redundant use of tools). On the practical side, bio. tools entries need to be updated (or flagged) automatically when EDAM changes and the used annotation terms become obsolete. Another pragmatic next step could be the introduction of an "automated exploration badge" in bio. tools that marks tools with validated semantic tool annotations. Thinking further, it might be desirable for bio. tools to provide quantitative annotations that could be informative for automated workflow exploration tools to prioritize possible workflows over others. In addition to the technical monitoring information from Open-EBench 26 that is already now available for some tools, this could include download numbers or marking of tools highly used in workflows (for example, derived from the WorkflowHub, https://workflowhub.
Mass spectra in Thermo RAW format Amino acid property in any format (i) Use operation peptide identification; (ii) Use operation Target-Decoy; (iii) Use operation retention time prediction; (iv) Do not use operation Protein identification #Mass spectra in Thermo RAW format Pathway or network in any format (i) Use operation peptide identification operation; (ii) Use operation enrichment analysis; (iii) Use operation enrichment analysis only after peptide identif ication operation; (iv) Use tool ProteinProphet only after PeptideProphet #Mass spectra in Thermo RAW format Protein identif ication in any format (i) Use operation PTM identification; (ii) Use operation PTM identification only after operation Target-Decoy; (iii) Use operation Target-Decoy only after operation peptide database search; (iv) Do not use operation Target-Decoy more than once #Mass spectra in Thermo RAW format Expression data in any format (i) Use operation iTRAQ; (ii) Use operation iTRAQ only after operation Target-Decoy Journal of Proteome Research pubs.
1 2 3 4 3 : 2 : 1 : 1234321 Mass spectra in Thermo RAW format Amino acid property in any format (i) Use operation peptide identification; (ii) Use operation Target-Decoy; (iii) Use operation retention time prediction; (iv) Do not use operation Protein identification #Mass spectra in Thermo RAW format Pathway or network in any format (i) Use operation peptide identification operation; (ii) Use operation enrichment analysis; (iii) Use operation enrichment analysis only after peptide identif ication operation; (iv) Use tool ProteinProphet only after PeptideProphet #Mass spectra in Thermo RAW format Protein identif ication in any format (i) Use operation PTM identification; (ii) Use operation PTM identification only after operation Target-Decoy; (iii) Use operation Target-Decoy only after operation peptide database search; (iv) Do not use operation Target-Decoy more than once #Mass spectra in Thermo RAW format Expression data in any format (i) Use operation iTRAQ; (ii) Use operation iTRAQ only after operation Target-Decoy Journal of Proteome Research pubs.