dataesr/scientific-paragraphs-categorization huggingface dataset:
Our results have been validated with cytological equivalents, experimentally generated on the fruit fly D. melanogaster genome  [4, 25, 26]. Moreover, since BREC is non-genome-specific, it could efficiently be run on other model as well as non-model organisms for which both genetic and physical maps are available. Even though it is still an ongoing study, BREC has also been tested with different species, and the results are reported. This paper is organized as follows: the set of our results, based on both simulated and real data, are reported in "Results" section. They are then discussed in "Discussion" section. Concluding remarks with some perspectives are outlined in "Conclusions" section. The full set of BREC modules, detailed within a step-by-step workflow, as well as further details on the data involved, and how the methods were calibrated and validated, are presented in "Methods" section.
This suggests that those two chromosomal arms' data might not present as good quality as the rest of the genome. Interestingly, the local markers density for these two chromosomal arms shows a high variation, unlike the other chromosomal arms. For instance, the 2L for which BREC returns accurate results, shows a lower variation (see Additional file 4). Without these two arms, the max shift for both centromeric and telomeric BREC boundaries is smaller than 1. 54Mb, with a mean shift decreasing from 1. 43 to 0. 71 Mb. This first analysis suggests that BREC methods return accurate results on this genome. However, the boundaries identification process appears very sensitive to the markers' local density and distribution along a chromosome (see Additional file 3). Therefore, we conducted further experiments on a different dataset, the tomato genome (see Additional file 5).
BREC handles low markers density We started by assessing the markers' density on the BREC estimates. We generated simulated datasets with decreasing fractions of markers for each chromosomal arm (from 100% to 30%). For that, we randomly selected a fraction of markers, 30 times, and computed the mean shift between BREC and the reference telomeric and centromeric boundaries. We have noted that BREC's resolution decreases drastically with the fraction and therefore with the marker density (see Additional file 8). However, BREC results appeared stable until 70% of the data for all the chromosomal arms, more specifically for the telomeric boundary detection. Only for the centromeric boundary of the chromosomal arm 3R, we observed the opposite pattern: BREC returns more accurate telomeric boundary estimates when the markers' number decreases.
Throughout our analysis, we consider that a chromosome presents a centromeric gap if its data exhibit a lack of genetic markers on a relatively large region on the physical map. Centromeric regions usually are less accessible to sequence due to their highly compact chromatin state. Consequently, these regions are also hard to assemble, and that is why many genomes have chromosomes presenting a centromeric gap. It is essential to know that a centromeric gap is not always precisely located in the middle of a chromosome. Instead, its physical location depends on the chromosome type (see more details in Additional file 10). We also assess the veracity of BREC on datasets with variable distributions using simulated data with and without a centromeric gap (see Additional file 9). For all six simulation datasets, BREC results overlap the reference boundaries.
Easy, fast and accessible tool via an R-package and a Shiny app BREC is an R-package entirely developed with the R programming language. The current version of the package and documentation are available on the GitHub repository: https:// github. com/ Genom eStru cture Organ izati on. In addition to the interactive visual results provided by BREC, the package comes with a web-based Graphical User Interface (GUI) build using the shiny and shinydashboard As for the speed aspect, BREC is quite fast when executing the main functions. We reported the running time for D. melanogaster R5 and S. lycopersicum in Additional files 2 and 15, respectively (plotting excluded). Nevertheless, when running BREC via the Shiny application, and due to the interactive plots displayed, it takes longer because of the plotly rendering.
We validated BREC methods with a reference dataset known to be of high quality: D. melanogaster. While two distinct approaches were respectively implemented for the We also validated BREC using the domesticated tomato S. lycopersicum dataset (see Additional files 6 and 7). At first glance, one might ask: why validating with this species when the results do not seem really congruent? In fact, we have decided to investigate this genome as it provides a more insightful understanding of the datadriven aspect of BREC and how data quality strongly impacts the heterochromatin identification algorithm. Variations in the local density of markers in this genome are particularly associated with the relatively large plateaued centromeric region representing more than 50% of the chromosome's length. Such data scenario is quite different from what we previously reported on the D.
Experiments on these whole genomes showed that BREC works as expected and identifies chromosome types in 95% of cases (see Additional file 13). One can assume, with the exponential increase of genomic resources associated with the revolution of the sequencing technologies, that more fine-scale genetic maps will be available. Therefore, BREC has quite the potential to widen the horizon of deployment of data science in the service of genome biology and evolution. It will be crucial to develop a dedicated database to store all this data. BREC package and design offer numerous advantageous functionalities (see Additional file 17) compared to similar existing tools  [22, 23]. Thus, we believe our new computational solution will allow a large set of scientific questions, such as the ones raised by the authors of  [5, 30] , to be addressed more confidently, considering model as well as non-model organisms, and with various perspectives.
As short-term perspectives for this work, we may consider extending the robustness tests to additional datasets with high quality and mandatory information (e. g. boundaries identified with the cytological method, high quality maps). Retrieving such datasets seems to become less and less complicated. We may also improve the identification of boundaries with a more refined analysis around them, using an iterative multiscale algorithm for instance. Finally, we will be happy to consider the users' feedback and improve our tool's ergonomy and usability. As mid-term perspectives, we underline that BREC could integrate other algorithms aiming to provide further analysis options such as the comparison of heterochromatin regions between closely related species. Also, we are aware that it would be interesting to compare BREC results with more existing methods.
New approach: BREC BREC is designed following the workflow represented in Fig. 1. To ensure that the broadest range of species could be analyzed by our tool, we designed a pipeline that adapts behavior with respect to input data. Each step of the workflow relies mostly on statistical analysis, adaptive algorithms, and decision proposals led by empirical observation. The workflow starts with a pre-processing module (called "Step 0") aiming to prepare the data prior to the analysis. Then, it follows six main steps: (1) estimate Marey Mapbased local recombination rates, (2) identify chromosome type, (3) prepare the HCB identification, (4) identify the centromeric boundaries, (5) identify the telomeric boundaries, and (6) extrapolate the local recombination rate map and generate an interactive plot containing all BREC outputs (see Fig.
In contrast, R 2backwards starts from the end of the chromosome, providing the right centromeric and right telomeric boundaries. These R 2 values were calculated using the rsq package in R. To compute R 2 cumulative vectors, rsq function is applied on the polynomial regression model. In fact, there is no such function for non-linear regression models like the Loess because, in such models, high R 2 does not always indicate a good fit. A sliding window is defined and applied on the R 2 vectors to precisely analyze their variations (see details in the next step). In the case of a telocentric chromosome, the position of the centromere is then deduced as the left or the right side of the arm, while in the case of an atelocentric chromosome, the existence of a centromeric gap is investigated.
Step 6 -Extrapolate the local recombination rate estimates and generate interactive plot The extrapolation of recombination rate estimates at the identified centromeric and telomeric regions automatically performs an adjustment by resetting the initial biased values to zero along these heterochromatin ranges. Finally, all of the above BREC outputs are combined to generate one interactive plot to display for visualization and download (see details in "Easy, fast and accessible tool via an R-package and a Shiny app" section). It is important to emphasize that throughout the whole main process module, only Step 1 " Estimating Marey map-based local recombination rates " comes from previous methods (  [20, 21] ). Otherwise, each of the steps 2-6 are fully developed (designed and implemented) within BREC and represent a new contribution, in addition to step zero " Data pre-processing ", as mentioned above.
Validation data The only input dataset to provide for BREC is genetic and physical maps for one or several chromosomes. A simple CSV file with at least two columns for both maps is valid. If the dataset is for more than one chromosome or the whole genome, a third column, with the chromosome identifier, is required. Our results have been validated using Release 5 of the fruit fly D. melanogaster  [36, 37]  genome as well as the domesticated tomato Solanum lycopersicum genome (version SL3. 0). We also tested BREC using other datasets of different species: house mouse (Mus musculus castaneus, MGI) chromosome 4  [38] , roundworm (Caenorhabditis elegans, ws170) chromosome 3  [39] , zebrafish (Danio rerio, Zv6) chromosome 1  [40] , respectively (see Additional file 13), as samples from the multi-genome dataset included within BREC (see further details on the full built-in dataset in "Description of main components of the Shiny app" section).
Fruit fly genome D. melanogaster Physical and genetic maps are available for download from the FlyBase website (http:// flyba se. org/; Release 5) [26]. This genome is represented here with five chromosomal arms: 2L, 2R, 3L, 3R, and X (see Additional file 2), for a total of 618 markers, 114. 59Mb of physical map and 249. 5cM of genetic map. This dataset is manually curated and is already clean from outliers. Therefore, the cleaning step offered within BREC was skipped. Tomato genome S. lycopersicum Domesticated tomato with 12 chromosomes has a genome size of approximately 900Mb. Based on the latest physical and genetic maps reported by the Tomato Genome Consortium  [28] , we present both maps content (markers number, markers density, physical map length, and genetic map length) for each chromosome in Additional file 15.
This is due to computing the markers' density in two different ways with respect to the analysis. Additional file 2, presenting the genomic features of the validation dataset, shows markers density in Column 3, which is simply the result of the division of markers number (in column 2) by the physical map length (in Column 4). For example, in the case of chromosomal arm X, this gives 165/21. 22 = 7. 78markers/Mb. On the other hand, Additional file 4, aimed for analyzing the variation of local markers density, displays the mean of of all 1Mb-bins densities, which is calculated as the sum of local densities divided by the number of bins, and this gives 165/22 = 7. 5markers/Mb. The exact same analysis has been conducted on the tomato genome S. lycopersicum where the only difference lies in using 5-Mb instead of 1-Mb bins, due to the larger size of its chromosomes (see Additional file 5).
Implementation and Analysis The entire BREC project was developed using the R programming language (version 3. 6. 3/2020-02-29) and the RStudio environment (version 1. 2. 5033). The graphical user interface is build using the shiny and shinydashboard packages. The web-based interactive plots are generated by the plotly package. Data simulations, result analysis, reproducible reports, and data visualizations are implemented using a large set of packages such as tidyverse, dplyr, R markdown, Sweave and knitr among  (2)  shift = |observed_HCB -expected_HCB| others. The complete list of software resources used is available on the online version of the BREC package accessible at https:// github. com/ Genom eStru cture Organ izati on. From inside an R environment, the BREC package can be downloaded and installed using the command in the code chunk in Additional file 19.
Build-in dataset Users can either run BREC on a dataset of 44 genomes, mainly imported from  [41] , enriched with two mosquito genomes from  [42]  and updated with D. melanogaster Release 6 from FlyBase  [26]  (see Additional files 20 and 21), already available within the package, or, load new genomes data according to their own interest. User-specific genomic data should be provided as inputs within at least a 3-column CSV file format, including for each marker: chromosome identifier, genetic distance, and physical distance, respectively. On the other hand, outputs from BREC running results are represented via interactive plots.
GUI input options The BREC shiny interface provides the user with a set of options to select as parameters for a given dataset (see Fig. 3a ). These options are mainly necessary in case the user works on his/her own dataset and this way the appropriate parameters would be available to choose from. First, a tab to specify the running mode (one chromosome). Then, a radio button group to choose the dataset source (existing within BREC or importing new dataset). For the existing datasets case, there is a drop-down scrolling list to select one of the available genomes (over 40 options), a second one for the corresponding physical map unit (Mb or pb) and a third one for the chromosome ID (available based on the dataset and not the genome biologically speaking). While for the import new dataset case, three more objects are added (see Fig.
Lastly, there is a checkbox to apply data cleaning if checked. Otherwise, the cleaning step will be skipped. This options could save the user some running time if s/he already have a priori knowledge that a specific genome's dataset has already been manually curated). The user is then all set to hit the Run button. BREC will start processing the chromosome of interest by identifying its type (telocentric or atelocentric). Since this step is quite difficult to automatically get the correct result, the user might be invited to interfere via a popup alert asking for a chromosome type confirmation (see Fig. 3b ). As shown in Additional file 14a, all available genomes could be accessed from the left-hand panel (in dark grey) and specifically on the tab " Genomic data " where two pages are available: " Download data files " which provides a data table corresponding to the selected genome on a scrolling list along with download buttons, and " Dataset details " displaying a more global overview of the whole build-in aata repository (see Additional file 14b).
Acknowledgements Our thanks go to the members of the  Institute of Sciences and Evolution of Montpellier (ISEM) as well as the Laboratory of Computer Science, Robotics and Microelectronics of Montpellier (LIRMM). In particular, team colleagues at the "  Phylogeny and Molecular Evolution  " and the "  Methods and Algorithms in Bioinformatics  ", respectively, for their insightful discussions.
Funding This work has been supported by the  Algerian Ministry of Higher Education and Scientific Research  as part of the  Algerian Excellence Scholarship Program AVERROES , with a full funding of YM's PhD project. As part of the  Junior Research Group  ( ERJ 2018 ) grant, the hardware used to conduct this work was funded with the support of  LabEx CeMEB , an  ANR  "  Investissements d'avenir " program  ( ANR-10-LABX-04-01 ). The publication costs are funded by the  CNRS as part of its name of the call for projects interdisciplinary program. Funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Another study used this coassembling approach to obtain the genome of five MAST-3 and MAST-4 species and explore their functional ecology and oceanic distribution. This study revealed functional differences in the motility apparatus and feeding spectra, and the presence of rhodopsins in one species. Here, we extended the dataset to 15 MAST species using SAGs from Tara Oceans and from other projects. We investigated their trophic strategy using a comparative genomics model, and focused on a set of gene families relevant for phagocytosis. Phagocytosis is a distinct form of endocytosis that incorporates particles > 0. 45 µm in diameter through the formation of membrane-bound vesicles called phagosomes. After maturation, phagosomes fuse with lysosomes and become a final phagolysosome where prey cells are degraded  [17, 18].
Gene predictions, gene family inference, and functional annotation Gene predictions from the coassembled genomes started by using the CEGMA and BUSCO retrieved genes to train SNAP (http://korflab. ucdavis. edu/software. html), which generates a set of ab initio gene models. In parallel, GENEMARK-ES  [38]  was run to obtain another set of predicted genes. Both sets were then used as input for a first run on the MAKER  [39]  pipeline. The candidate genes identified were then used as the training dataset input in a second run of MAKER, with default settings, to train the program AUGUSTUS  [40] , finally providing transcripts and protein predictions for each coassembled genome. The pipeline used can be found on GitHub (https://github. com/ guyleonard/gene_prediction_pipeline). Predicted coding sequences (CDS) from the coassembled MAST genomes were loaded into a custom instance of the PLAZA framework  [41]  together with the CDS of other stramenopiles and nonstramenopile model species (Fig.
S1 ). Based on an "all-against-all" protein sequence similarity search done with DIAMOND v. 0. 9. 18  [42]  ("more sensitive" mode with a maximum e-value cutoff of 10 -5 and retaining up to 2500 hits), orthologous gene families were delineated with OrthoFinder v. 2. 3. 3  [43]  (default parameters). Functional annotation of all CDS was performed using InterProScan v. 5. 39-77. 0 [44] , including mapping InterPro entries to GO annotations. For the model organisms in the database (Fig. S1 ), GO annotations were retrieved from the GO website. Finally, functional enrichment analyses were performed to assign informative Inter-Pro and GO terms to each orthologous gene family. The enrichment analysis used the hypergeometric distribution with a maximum Bonferroni corrected p value cutoff of 0. 05, and all coding genes from the organisms included in the gene family as background frequency.
OGs found in only one species were discarded, and the number of genes per OGs was normalized to percentages in each genome. Based on the OG table, genomes were compared using Bray-Curtis dissimilarities and analyzed by nonmetric multidimensional scaling (NMDS) with the R package vegan v2. 5-6  [46]. The grouping of species based on trophic lifestyle was tested by a PERMANOVA analysis using vegan's function adonis2 (). A multilevel pattern analysis to identify OGs that characterize a given trophic mode (indicator value (IndVal) > 0. 7 and p value < 0. 05) was performed using the function multipatt() implemented in the R package indicspecies v1. 7. 9  [47]. A heatmap displaying OGs annotated as peptidases and proteases was created with R package pheatmap v1. 0. 12  [48] , using Ward's method for hierarchical clustering with log 10 -transformed OGs gene counts (with a pseudocount of 1).
Homology searches and phylogenetic analyses for specific proteins Protein sequences from three gene families of proton pumps were retrieved from public databases. Reference sequences for V-ATPases were extracted from Mulkidjanian et al. [49] , while for V-PPases we used the phylogenetic tree in Goodenough et al. [20]. Rhodopsin reference sequences were collected from several articles  [28, 50, 51] , and the MicRhoDE project  [52]. Using these reference datasets, homologous MAST sequences were identified by sequence similarity using BLAST v. 2. 2. 28 (maximum e-value threshold of 10 -5 ). The selected contigs were checked to discard potential bacterial contamination. Homology searches using Pfam domains were conducted against the key enzymes involved in retinal formation: GGPP synthase (PF00348), phytoene synthase (PF00484.
18), phytoene dehydrogenase (PF01493. 23), lycopene cyclase (PF05834), and β-carotene 15,15′-dioxygenase (PF15461. 5). Selected sequences were aligned with MAFFT v7. 470  [53]  (--globalpair) and trimmed with TRIMAL v1. 4  [54]  (-automated option) to obtain a curated subset for phylogenetic analyses. Phylogenetic trees were constructed with the maximum likelihood method using the LG+F +R6 substitution model in IQ-TREE  [55]  and topology support was determined with 1000 bootstrap replicates. Rhodopsin expression data were retrieved from the Marine Atlas of Tara Oceans Unigenes database (MATOU)  [56] , which presents expressed eukaryotic genes clustered at 95% identity. Rhodopsin MAST sequences were used as query in BLAST 2. 7. 1 against MATOU v1 and we kept the most similar unigene for each type.
S4 ). A PERMANOVA analysis showed that 22% of the variance in the plot (p < 0. 001) was explained by the trophic mode, and this justified the use of the IndVal statistic to this dataset. Among the 28 OGs indicators of the phagocytosis trophic mode (Table  1 ), we identified many digestive enzymes (peptidases, glycosidases, lipases) and other genes related to cell growth and responses to the environment. A larger number of OGs characterized osmotrophs (Table  S2 ) and phototrophs (Table  S3 ), 133 and 744 OGs, respectively. In particular, phototrophs displayed many genes encoding for photosystem and other plastidic proteins. We focused on a given group of digestive enzymes, the peptidases, and explored how frequent they were among the complete set of stramenopile genomes. For this, we selected the 295 OGs that were functionally annotated as peptidases or proteases and studied their distribution in the 30 genomes, both at OGs level (Fig.
6  Expression level of MASTs rhodopsins in Tara Ocean metatranscriptomes (metaTs). The bar plot shows the gene expression in surface samples (57 metaTs) and DCM samples (36 metaTs) of each key rhodopsin gene based on its highly similar unigene of the MATOU dataset. The top upper-right inset indicates the percentage of samples in each water layer where each expressed transcript has been identified. carried out by phytoene synthase (crtB), followed by a sequential desaturation and isomerization via phytoene desaturase (crtI) to synthetize lycopene. The enzymes crtE, crtB, and crtI are present in most of the studied MAST species and in many of the individual SAGs (Fig. S8 ). Synthesis of β-carotene is then catalyzed by the lycopene cyclase (crtY). The key and final step is the oxidative cleavage of β-carotene into retinal by the enzyme β-carotene 15,15′-dioxygenase (blh).