dataesr/scientific-paragraphs-categorization huggingface dataset:
The BlastP dataset of reference SPs was enriched with a method similar to the original protocol  (11, 12). The metadata associated with SP families and superfamilies were manually curated and standardized. The HMM profile bank was built, composed of two kinds of HMM profiles: (i) publicly available profiles from either the PFAM  (26) , the TXSScan  (20)  or the MOBscan  (27)  resources and (ii) newly designed HMM profiles that characterize new domains of relaxases found in IMEs of Firmicutes and previously published  (11, 12, 28, 29). To create the new HMM profiles of relaxases, one or more reference sequences were selected from the ICEscreen protein database of SPs and/or from the NCBI GenBank public database. The functional domain architecture was identified using CD-Search  (30)  and SPARCLE  (31).
Coding DNA Sequences (CDSs) from GenBank with a similar functional domain architecture were retrieved. Redundant sequences were removed by a clustering at 95% identity over 100% of the length using CD-HIT  (32) (33) (34). To further decrease the number of seed sequences if needed, a second clustering at 40% identity over 100% of the length was performed. A multiple alignment was constructed with MAFFT v7. 407 using the FFTNS1 algorithm  (35). Manual curation of the multiple alignment was carried out to remove too divergent proteins by using a phylogenetic tree constructed with SeaView 4. 2  (36). The Neighbor-Joining method BioNJ (37) was used with the following parameters: gaps excluded, Poisson distance and bootstrap of 100 iterations. When a set of relaxases was still too distant, the multiple alignment was further divided into smaller but coherent sets of alignments to subsequently create HMM profiles.
Implementation and distribution of the ICEscreen workflow ICEscreen was implemented using the Python 3 language and Bash scripts. The pipeline was managed with Snakemake  (42). Two external tools were required for the detection of SPs by homology: BlastP version 2. 9. 0 (43) for the identification of close homologs and HMMscan from the HMMER3 suite version 3. 3. 2 (41)  for the detection of remote homologs. BlastP results were filtered out to remove false positive results using four types of filters detailed in Supplementary Table  S1 (a). Filters used to remove false positive HMMscan results are detailed in Supplementary Table  S1 (b). Specific additional filters were designed to remove known SP false positive results and are described in Supplementary Table  S1 (c). A Conda package for ICEscreen was developed encapsulating all the dependencies and automating the installation process.
Benchmarking ICEscreen was compared with ICEfinder  (19)  and CON-Jscan version 1. 0. 5 which is a module of MacSyFinder  (20, 21). ICEfinder was used with default parameters. As CONJscan is a generic tool to search for conjugative T4SS in all bacteria and archaea, only the subsets FA (corresponding to the Tn916 superfamily) and FATA (corresponding to the Tn5252 superfamily) of conjugation modules known to be related to ICEs and IMEs in Firmicutes were analyzed for this benchmark. FA and FATA systems (8) were searched using the CONJ model to detect conjugation modules of ICEs (relaxase + VirB4 + coupling protein). Mobilizable modules of IMEs were searched using a modified version of the MOB model to match our definition of IMEs (mandatory relaxase, accessory coupling protein and absence of VirB4).
Benchmark on experimentally supported elements from Firmicutes First, a benchmarking was performed on a set of 11 elements from Firmicute genomes collected from publications and with experimental support (see 'Genomes sheet' of Supplementary Table  S2 ). We took into account elements whose delineation was reliable and carried by assembled and well annotated genomes (RefSeq genomes) and not included in the FirmiData dataset  (25)  (see next sub-section). An element of the reference dataset was considered correctly detected by a tool when (i) at least two of its SPs were identified and grouped together and (ii) the type of the detected element was also correct (identical to the one mentioned in the publication). An element of the reference dataset was considered partially detected by a tool if less than two SPs were correctly detected.
Benchmark on FirmiData The FirmiData dataset  (25)  was used as a reference to evaluate and compare the performances of the three tools using a diverse set of manually annotated ICEs and IMEs, including complete, partial and nested structures as well as elements in accretion. These elements were annotated in 25 Streptococcus and 15 other Firmicute public complete genomes issued from RefSeq using data from the literature and a semi-automated procedure that was described in  (11)  for ICEs and in  (12)  for IMEs. Regarding SPs, FirmiData includes a total of 137 tyrosine Integrases, 113 Serine Integrases, 12 DDE transposase integrases, 250 relaxases, 143 coupling proteins, and 98 VirB4. The number of manually curated structures annotated in FimiData includes: 98 ICEs and 148 IMEs with 42. 3% of these elements being found in complex structures (nested or in accretion).
We used three levels of result evaluation: 1. An element of the reference dataset was considered detected by a tool when at least two of its SPs were identified and grouped together. Consequently, for an IME including two SPs in the reference dataset, both SPs had to be detected and grouped. 2. The type of the detected element was considered correct if it was consistent with the type attributed in the reference dataset. Thus, an ICE was considered correctly typed if a tool had characterized it as ICE, partial ICE or conjugative module. In the same way, an IME had to be characterized as either IME, mobilizable element or mobilizable module. 3. We also evaluated the ability of the tools to detect the correct number of ICE and IMEs included in composite elements.
Case studies: three examples of ICEs and IMEs detection in three Firmicute genomes We selected three genomes of FirmiData to illustrate differences in ICE/IME prediction compared to the Firmi-Data manual annotation: Streptococcus gallolyticus subsp. gallolyticus BAA-2069, Roseburia hominis A2-183 and Clostridioides difficile R20291. ICEfinder, CONJscan and ICEscreen results were obtained as described as described above. Comparisons and graphical representations of ICE and IME contents in the FirmiData reference dataset and in the results produced by the three tools were performed using customized R scripts.
The ICEscreen workflow ICEscreen takes as input annotated genomes with predicted coding genes in GenBank format. (12) , a family is assigned to relaxases, coupling proteins, and VirB4 when it shares more than 40% identity with proteins of the database encoded by ICEs. In order to identify genome regions including closely located genes encoding VirB4, CPs and relaxases and to limit the number of combinations for the grouping of these SPs into structures, a preliminary step is implemented that defines segments as lists of SPs where the distance between two subsequent SPs is less than 100 CDSs. This cutoff is based on elements from Firmicutes where the most distant SP encoding genes are less than 50 CDSs apart and takes into account a safety margin. ICEscreen also searches for distant proteins with HMMscan using the ICEscreen HMM profile bank.
When all potential anchors have been created, the algorithm evaluates the possibility of combining anchors in order to resolve the cases of complex structures (for instance nested elements). Step C: The integrase is then searched on both sides of each anchor. Step D: The next step consists in typing each element according to its SP content and size as described in the two panels on the right. scribed in  (12)  (one profile for PF01719-like relaxases, two profiles for PHA00330-like relaxases and three profiles for PF02407-like relaxases). The domain content of SPs determined by HMM analysis has been used to classify the SPs in superfamilies as previously proposed  (11, 12). The steps described in the following are carried out with each segment. Step B: creation, extension and fusion of anchors.
Superfamilies of ICEs and IMEs are assigned using the known three ICE superfamilies (Tn916, Tn5252 and TnGBS1) and nine IME superfamilies in Streptococci (MOB T , MOB Q , MOB V , MOB P , MOB C , PF01719, PF01719-PF00910, PF02407 and PHA00330). Superfamilies of ICEs are assigned based on the relaxase and coupling protein families as defined in  Ambroset et al. , 2016 (11). Superfamilies of IMEs are assigned based solely on the relaxase family as defined in  (12).
Benchmarking results Benchmark on experimentally supported elements from Firmicutes. The ICEscreen tool was first evaluated using nine publicly available RefSeq genomes carrying 11 published elements (nine ICEs and two IMEs) with experimental support demonstrating their transfer and/or excision. Table  1  summarizes the performances of ICEfinder, CONJscan and ICEscreen on this first dataset. Detailed results obtained by each tool are provided in Supplementary Table  S2. ICEfinder exhibited the poorest performance by detecting only three of the 11 elements correctly and two partially with either a wrong assignment (e. g. : ICE instead of IME) or a wrong SP composition. Six elements were not at all detected by ICEfinder: Tn6098 of Lactococcus lactis KF147, ICE vanG-1 and ICE-r of Streptococcus agalactiae GBS1-NY, ICE-r of S.
agalactiae GBS2-NY, ICE 6180-RD. 2 of S. pyogenes MGAS6180 and ICE SsuD9 rplL of S. suis D9. CONJscan exhibited rather good performances by correctly detecting nine out the 11 elements. The two undetected elements corresponded to the two IMEs of the dataset (IME SanNCTC11064 oriT of Streptococcus anginosus NCTC11064 and IME SanNCTC11064 oriT of Streptococcus sp. FDAARGOS 522). ICEscreen correctly detected all the 11 elements of this dataset. Overall, ICEfinder did not achieve good results whilst CONJscan and ICEscreen performed well in the detection of elements whose excision and/or transfer has been demonstrated or is based on strong biological evidence. Benchmark on FirmiData. To evaluate more extensively the performance of ICEscreen, we used FirmiData, a public dataset of 40 genomes of Firmicutes that were chosen to illustrate the diversity of ICEs and IMEs as well as of their various organization into composite elements.
We first compared the ratio of FirmiData's SPs successfully detected by ICEscreen, CONJscan and ICEfinder (see Figure  2 ). Results are classified by SP type (Relaxase, Coupling Protein, VirB4 and Integrase). ICEscreen outperformed the two other tools and detected between 85. 6% and 98% of each type of SP. CONJscan performed well and detected 73. 1% of the coupling proteins, 75. 4% of the relaxases and 82. 7% of the VirB4. It is important to note that CON-Jscan does not search for integrases, and therefore this criterion cannot be used as a basis for comparison. ICEfinder had the lowest performance, detecting only 30% of the coupling proteins, 40. 6% of integrases, 42. 1% of the relaxases and 36. 5% of the VirB4 proteins. We then used the elements annotated in the FirmiData reference dataset to compare the results of ICEscreen, CONJscan and ICEfinder.
DISCUSSION In this article, we present ICEscreen, a new tool to detect ICEs and IMEs in chromosomes of Firmicutes and compare its performance with two existing tools: ICEfinder which is associated with the ICEberg2 database  (18, 19) , and CONJscan which is a module of MacSyFinder that was designed to detect conjugative T4SS  (20, 21). Compared to CONJscan and ICEfinder, ICEscreen brings several new important features: (i) it relies on an ad hoc new SP database composed of proteins and HMM profiles that reflect the state of the art of known Firmicute ICE and IME SPs, (ii) it combines the results of two tools (Blast and HMMscan) and specific filters to efficiently detect these SPs in Firmicute genomes while limiting false positives due to the lack of specificity of certain SPs, (iii) it implements a new algorithm capable of resolving regions carrying several elements (referred to as composite structures), (iv) it pro- vides much valuable information, such as the precise structure and SP composition of elements, the family of Relaxase and coupling proteins (where possible) and other isolated SPs that may indicate the presence of new and as yet unknown element and (v) it is based on an automated approach and is packaged to provide a simple and valuable resource to the microbiologist community interested in ICE/IME detection and annotation in any species of Firmicutes.
We evaluate ICEscreen performances using a list of 11 published elements (nine ICEs and two IMEs) with experimental support of their transfer and/or excision and on the FirmiData public dataset including 98 ICEs and 148 IMEs annotated in 40 genomes of Firmicutes  (25). We also present three case studies illustrating typical composite structures in Firmicute genomes. Taking together, this dataset includes 109 ICEs and 150 IMEs that represent the full diversity of elements known to exist in Firmicutes, whether they have been experimentally demonstrated, are predicted members of well-known families or are members of predicted non-canonical families. Overall, our results highlight ICEscreen good performances for the detection of Firmicute ICEs and IMEs. ICEscreen detects all the 109 ICEs and 149 out of the 150 IMEs of our two evaluation sets, whether isolated or in composite structures.
ICEscreen performance decreases slightly for ICEs and IMEs in composite structures but was still very good. On this dataset, CONJscan performs quite well in correctly detecting conjugation modules of ICEs but does not neither detect detects IMEs nor correctly resolves the structures of most composite elements. ICEfinder has the weakest performance on our benchmark by missing an important number of SPs and adding other false positives to elements, especially in composite structures including nested or accreted ICEs and IMEs. Composite structures mixing ICEs and IMEs are particularly frequent in Firmicutes. Thanks to its dedicated algorithm, ICEscreen detects all elements and solves most intricate composite structures while the two other existing tools miss some of them and generally merge some elements together.
This is due to the fact that the existence of composite elements is not taken into account in the current versions of neither CONJscan nor ICEfinder. It is important to mention that ICEscreen, is still imperfect, as many factors render the detection of some ICEs and IMEs elusive: (i) ICEScreen cannot detect IMEs that do not encode any relaxase. However, many known IMEs of Gammaproteobacteria are devoid of relaxase (6) and the 40 genomes composing the FirmiData data set carry many integrative elements that could be IMEs devoid of relaxases (data not shown); (ii) knowledge of the different families of SPs of ICEs and IMEs is still highly partial, which could prevent detection of some yet unknown elements, (iii) ICEscreen algorithm does not permit detection of too decayed elements, i. e. IMEs that have lost one SP gene, or ICEs that have lost VirB4 and two other SP genes.
ICEscreen's ability to solve composite structures will be valuable. As a perspective for this work, we also plan to extend ICEscreen's scope to address the detection of ICEs and IMEs from other bacterial phyla (Proteobacteria, Actinobacteria, Bacteroidetes). This implies two main tasks: (i) the extension of our SP database to include representative families specific of these phyla and (ii) the addition of new detection rules to the ICEscreen algorithm to take into account the structures observed in non-Firmicute genomes. Together with the ICE/IME delineation module, these will be the main new features of the next version of ICEscreen software.
It generates three output result files: (i) one summary file including general information about the ICEscreen run (like parameters, number of detected SPs and number of detected elements), (ii) a second file listing the detected SPs in the query genome(s) and (iii) a third one listing the features of the detected ICEs and IMEs in the query genome(s). Output files including ICE and IME SP annotation are provided in gff and GenBank format. The ICEscreen workflow is described in Figure1.
DATA AVAILABILITY The ICEscreen software is freely and publicly available on the gitlab repository of the MathNum division of INRAE at https://forgemia. inra. fr/ices imes analysis/icescreen. It is available to install from source, Conda (https://anaconda. org/search?q=icescreen), and the Galaxy tool shed (https: //toolshed. g2. bx. psu. edu/). The documentation of the tool is available at https://icescreen. migale. inrae. fr. ICEscreen is a free software under the Affero GPLV3 licence.
It generates three output result files: (i) one summary file including general information about the ICEscreen run (like parameters, number of detected SPs and number of detected elements), (ii) a second file listing the detected SPs in the query genome(s) and (iii) a third one listing the features of the detected ICEs and IMEs in the query genome(s). Output files including ICE and IME SP annotation are provided in gff and GenBank format. The ICEscreen workflow is described in Figure1 (11)consists of four steps: (A) detection of SPs, (B) search of possible transfer modules by creation, extension and fusion of anchors, (C) assignation of the integrases to the anchors, and (D) classification of the ICEs and IMEs elements based on their content in SPs. Step A: detection of Signature Proteins and segmentation.
ICEscreen first searches for homologs of SPs with BlastP using the ICEscreen protein database. This includes SPs that were chosen for their diversity and that are encoded by demonstrated or predicted ICEs or IMEs from Firmicutes. Currently, the ICEscreen protein database comprises 1022 non-redundant SPs, including 317 relaxases, 231 coupling proteins, 140 VirB4 and 334 integrases (239 tyrosine integrases, 73 serine recombinases and 22 DDE transposases). According to the classification proposed by Ambroset et al. (11)andColuzzi et al.
We modeled the temporal component using asymmetric eigenvector maps (AEMs) using the aem. time function from the adespatial package  (Blanchet et al. , 2008; Dray et al. , 2019). We used the percentage of variation explained by each component in the random forest, regardless of its statistical significance. Second, we used hierarchical modeling of species communities (HMSC)  (Ovaskainen et al. , 2017) , which is a hierarchical Bayesian joint species distribution model that uses fixed environmental predictors and spatiotemporal random effects to make community-level inferences. We used species abundance with an assumed Poisson distribution as the response variable. The environmental variable was included as a fixed linear and quadratic effect to fit the Gaussian-shaped response of species to the abiotic environment.
ACKNOWLEDGMENTS This paper resulted from the sTURN working group funded by sDiv, the  Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( FZT 118 ). Additional funding came from  Österreichische Forschungsgemeinschaft (ÖFG; International Communication , project  06/15539 ). Patrick L. Thompson was supported by  Killam  and  NSERC postdoctoral fellowships. Luc De Meester acknowledges KU Leuven.
Acknowledgements The authors would like to apologize for not citing all the relevant research articles and reviews due to space limitation. We thank the editor and the anonymous reviewers for their constructive suggestions that greatly improved this review. EK is grateful to the  Biotechnology and Biological Sciences Research Council  for a  new investigator grant award  ( BB/M023079/1 ) and the  COST action CA0125 EPI-CATCH  (epigenetic mechanisms of crop adaptation to climate change). AZ is funded by a  MVLS PhD studentship  from the  University of Glasgow. Work by FB is supported by  Velux Foundation (Switzerland) , the  CNRS EPIPLANT Action (France) , grants  ANR-18-CE13-0004-01  and  ANR-17-CE12-0026-02  from  Agence Nationale de la Recherche  ( ANR, France ) and the  EPISEEDLINK doctoral network (EU).
| Design of species assemblages with contrasting species and functional diversities Using literature data (Table  S1 ), we built eight plant assemblages (Appendix S1) by varying species richness (low, medium, or high, named LS, MS, and HS, respectively), the functional diversity of traits (low or high, named LF and HF, respectively) and species identity (two sets of species). For designing the assemblages, we focused on a few traits involved in plant-arthropod interactions and easily accessible in databases (Table  1 ): (1) provision of flower resources, that is, floral and extrafloral nectar or pollen, (2) temporal availability of the resource, that is, the duration of the flowering period, (3) accessibility of the resource, depending on flower shape, and (4) provision of habitats through height at flowering.
Trait values were either measured or retrieved from published data or online databases for the 151 observed species. In order to assess the ability of plant assemblages to support higher trophic levels (first objective) and to analyze the temporal dynamics of plant assemblages (second and third objectives), the traits analyzed (Table  1 ) were related to (1) plant-arthropod interactions (provision of trophic resources, temporal availability, accessibility, and attractiveness of the resource, provision of physical habitats;  Gardarin et al. , 2018) , and (2) plant-plant interactions (plant morphology, resource acquisition, or conservation strategy). We checked that these traits were not highly correlated (correlation coefficient <0. 70).
| Flower traits Flower traits were measured, by a single observer, on plants found in the experimental field, or on plants found in the surrounding area, for the sown species that did not emerge (for comparison with the sown and the realized functional assemblage characteristics). Between 2015 and 2017, we recorded the species that were flowering (i. e. , flowers present in at least 20% of the individuals). We determined two traits: the calendar week (numbered from 1 to 52) in which flowering began and the duration of flowering (numbers of weeks). We averaged the values of these traits over the 3 years of field observations. The presence of extrafloral nectar (0 = none, 1 = present) was also noted. These measurements were completed with published trait measurements. Data concerning basic flower color and the presence of a UV reflection pattern were extracted from the BiolFlor database  (Kühn et al.
| Plant vegetative and architectural traits We used data for leaf distribution (rosette, semi-rosette, or leaves distributed along the stem), flowering height, specific leaf area, and leaf dry matter content from the LEDA database  (Kleyer et al. , 2008) , for Raunkiaer life form  (Julve, 1998)  and for the Competitive and Ruderal scores (percentage) of Grime strategy  (Bàrberi et al. , 2018; Kühn et al. , 2004; Pierce et al. , 2017). For each trait, values were obtained for more than 95% of the species, with the exception of nectar depth, for which data were available for only 81% of the species, but accounting for 99. 6% of total plant cover. On average, trait data were available for 97% of the 151 species we observed, accounting for 99. 9% of the total plant cover. To deal with the first objective (temporal dynamics of functional diversity of plant traits involved in interactions with higher trophic levels), functional diversity was calculated on the basis of the 11 fol- leaf dry matter content.