dataesr/scientific-paragraphs-categorization huggingface dataset:
ChIP-seq analysis All ChIP-seq analysis were done using the Galaxy Project (https://usegalaxy. org/). Reads were trimmed using Cutadapt (--max-n 4) and Trimmomatic (TRAILING 1; SLIDINGWINDOW 4 and cutoff 20; LEADING 20; MINLEN 50), and mapped using Bowtie2 onto mm10 mouse reference genome (-X 600; -k 2; --sensitive). PCR-derived duplicates were removed using PICARD MarkDuplicates. Bigwig files were generated with bamCoverage (binsize=1). Peak calling was performed using MACS2 callpeak with Input as control and with options: --qvalue 0. 05; -nomodel; --keep-dup 1; --broad (only for histone marks). Blacklisted regions were then removed using bedtools Intersect intervals. Visualization of coverage and peaks was done using IGV (  (Robinson et al. , 2011) ; http://software. broadinstitute. org/software/igv/home).
Intersection and analysis of bound genes were done using Genomatix (https://www. genomatix. de/). Chd7, Chd8 and Mock ChIPseq datasets are from  (Marie et al. , 2018). Heatmap was done using R (4. 0) using pheatmap package. GO analysis was done using Enrichr GO Biological Process 2021. Two replicates were done for Olig2, with one of them of better quality (53,960 peaks for replicate 1 and 14,242 peaks for replicate 2). Only the peaks found in both replicates (6,781) and the peaks from replicate 1 which were found in regulatory elements (13948) were considered (16578 in total). Three replicates were done for H3K4me3, two replicates were done for H3K27me3 and one replicate was done for H3K27Ac and H3K4me1. Intersection of these datasets was done using bedtools Intersect intervals. Peaks overlapping with regions between 1000bp upstream of transcription start site (TSS) and 10bp downstream of TSS were identified as "promoters" (Genomatix).
"Active promoters" were represented by peaks for H3K4me3 and H3K27Ac. "Repressed promoters" were represented by peaks for H3K27me3 and no active marks. "Poised promoters" were represented by peaks for H3K4me1 and no active or repressed mark. Regions outside promoters containing histone marks were considered as "enhancers". "Active enhancers" were represented by peaks for H3K27Ac. "Repressed enhancers" were represented by peaks for H3K27me3 and no active marks. "Poised enhancers" were represented by peaks for H3K4me1 and no active or repressed mark. Genes were considered associated if the peaks were present in the promoter or within a range of 100kb from the middle of the promoter and the gene expression was medium to high ("active"), low ("poised") or not ("repressed") expressed (based on control RNA-seq dataset in GSE116601).
Analysis of RNAseq Raw data were downloaded from GEO datasets GSE107919 and GSE116601 and processed throught the Galaxy Project (https://usegalaxy. org/) using RNAstar for alignment on mm10 reference genome and featureCounts to obtain counts. CPM (count per million), FPKM and statistical analysis were done with R (4. 0) using edgeR package. Using control RNA-seq dataset in GSE116601, genes were classified as not (below first quartile), low (between first quartile and mean), medium (between mean and third quartile) and high (above third quartile).
Analysis of scRNAseq Counts per gene were downloaded from GEO datasets GSE75330 and GSE95194, and processed in R (4. 0) using the following packages: Seurat (3. 0) for data processing, sctransform for normalization, and ggplot2 for graphical plots. Seurat objects were first generated for each dataset independently using CreateSeuratObject function (min. cells = 5, min. features = 100). Cell neighbors and clusters were found using FindNeighbors (dims = 1:30) and FindClusters (resolution = 0. 4) functions. Clusters were manually annotated based on the top 50 markers obtained by the FindAllMarkers, adopting mainly the nomenclature from Marques 2016. Using the subset function, we selected only the clusters containing neural progenitors and oligodendroglia cells. Using the merge function, we combined both oligodendroglial datasets into a single Seurat object (OLgliaDevP) containing 5516 cells.
ACKNOWLEDGMENTS We thank  D. Bergles  for the PDGFRα::CreER T mice. All animal work was conducted at the  ICM PHENOPARC Core Facility. Data generated relied on ICM Core Facilities: PHENO ICMice, iGenSeq, iVector, CELIS, Histomics, and ICM Quant, and we thank all personnel involved for their contribution and help. The Core Facilities were supported by the " Investissements d'avenir"  ( ANR-10-IAIHU-06  and  ANR-11-INBS-0011-NeurATRIS ) and the  "Fondation pour la Recherche Médicale ". This work was supported by funding by grants from the  National Multiple Sclerosis Society  ( NMSS RG-1501-02851 ), and the  Fondation pour l'Aide à la Recherche sur la Sclérose en Plaques  ( ARSEP 2014 (ARSEP , 2015(ARSEP , 2018(ARSEP , 2019(ARSEP , 2020)). E. M, H. H, and C. M. were supported by funding from  Sorbonne Université.
Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains.
There are many solutions for pathway enumeration and search  15  , which are sometimes integrated into the retrosynthesis software itself. The third step is to find the most promising enzyme sequences catalyzing the metabolic reactions. This can be achieved either through similarity search to enzyme annotated metabolic reactions  [16] [17] [18]  , or machine learning trained on metabolic databases  19, 20. Once the pathways have been annotated with enzyme sequences, they can be ranked in a fourth step. The ranking criteria are diverse, they can be among others based on thermodynamics  21  , predicted yield of the target  22  , target rate of production through flux balance analysis  9, 11, 21  , chassis cytotoxicity of the target and intermediates  21  , along with simpler criteria like pathway length.
Several computational tools can be used to perform this sixth and last step of assembly design before constructing the pathways, these tools compute parts to be synthesized depending on the chosen assembly protocol. Computation tools to help the build tasks are sparser than for design. One can cite here Aquarium  26  , which provides instructions to a person or a robot to perform the assembly tasks along with Antha 27 , BioBlocks  28  , and DNA-BOT  29. Engineered pathways are generally evaluated using HPLC or mass spectrometry analyses. Here too, computational tools can help in particular the workflows produced by OpenMS  30  or Worlflow4Metabolomics  31. Considering the above, we are clearly at a stage where the pathway engineering process is not that far from being fully driven by computer software products.
Tools selection criteria To develop an integrated ecosystem, we selected software applications from among the computational tools mentioned above. Several criteria were used for this selection: the tools needed to (i) be relevant for pathway design and engineering, (ii) be published, (iii) be opensource (MIT, GNU GPL, or related licenses), (iv) be well documented and deposited in GitHub, (v) make use of standard input/output, and (vi) exist as a standalone command-line tool. Within a workflow, each tool connected to one or more tools must share common file format for data exchange, i. e. , each output file of a tool has to be compatible with the input file format of downstream tools in the workflow. The file format relies on the nature of the data (e. g. , metabolic model, metabolic pathway, and construct design) and the implementation choice made for each tool.
Pathway design and engineering tools and workflows The selected tools are further described in the 'Supplementary_Text' file (cf. Galaxy-SynBioCAD Tools). These tools can be divided in three categories: (i) those aimed at finding pathways to synthesize heterologous compounds in chassis organisms (RetroRules, RetroPath2. 0, RP2Paths, and rpCompletion), (ii) those aimed at evaluating and ranking pathways (rpThermo, rpFBA, rpReport, rpViz, and rpScore) and (iii) those related to genetic design and engineering (Selenzyme, SbmlToSbol, PartsGenie, OptDOE, DNA Weaver, LCR Genie, rpBA-SICDesign, and DNA-Bot). Following FAIR principles  32  , all selected tools are open-source with code available on GitHub and installable through the Conda package manager 36 (cf. Tools design and integration process in the 'Supplementary_Text' file).
Therefore, any user can install the tools needed on their own computer and run these as standalone programs or chain them together to process more complex calculations. This workflow provides a direct link between machine-enabled design and automated assembly. It takes as input a pathway and generates a script to operate an Opentrons liquid handler robot performing assembly and chassis transformation. In the reminder of the paper, a metabolic pathway is a succession of chemical reactions transforming reactants into products, while a construct is the assembly of genetic parts that encodes a metabolic pathway. Several constructs with different enzyme and regulatory sequences can encode for the same pathway. The Retrosynthesis and Pathways analysis workflows generate annotated SBML files describing pathways (cf.
Benchmarking workflows for Lycopene production The Retrosynthesis workflow was run at the Genoscope laboratory (Paris region, France) for the production of lycopene in E. coli. We used iML1515  41  as a model for E. coli. The retrosynthetic map was composed of 12 unique compounds and 7 unique reactions, resulting in 3 "master" pathways (cf. Retrosynthesis from target to chassis in Methods section). Only the 10 best pathways were kept per master pathway, after reaction completion with cofactors and removal of duplicates only 9 pathways remained. Additional details are provided in the Supplementary file 'Dataset 1'. The Pathway Analysis workflow was run at the University Polytechnic of Valencia, Spain. The workflow took as inputs the list of nine pathways generated by the Retrosynthesis workflow.
Results are shown in Fig. S6  in the 'Supplementary_Text' and 'Dataset 1' files. The top-ranked pathway was composed of 3 reactions with EC numbers listed from chassis metabolites to target: 2. 5. 1. 29, 2. 5. 1. 96 and 1. 3. 99. 31. The Genetic design and engineering workflow for BASIC assembly was run at two different locations: Paris (Micalis Institute) and London (Imperial College). In both cases, as design input we used the top lycopene ranked pathway predicted by the Pathway analysis. Constraining the enzyme search within the organism Pantoea ananas, enzymes CrtE (UniProt ID: P21684), CrtB (P21683), and CrtI (P21685) were predicted by the Selenzyme tool for the three-reaction pathway (Fig. S8  in the 'Supplementary_Text' file and 'Dataset 1' Supplementary file). A total of 88 construct designs were automatically generated by the genetic design and engineering BasicDesign tool.
The designs were coded in a CSV file that was fed to DNA-Bot, which was executed in Paris and London with different labware identifiers and associated parameters (cf. Genetic design and engineering workflow execution in Methods). In both laboratories, DNA-Bot generated four executable python scripts (clip reactions, purification, assembly and strain transformation) that were run on Opentrons robots. Additional information can be found in the Supplementary file 'Dataset 1'. Following the executable scripts produced by the Genetic design and engineering workflow the three genes of the pathway (crtE, crtB and crtI) were assembled in varying gene order in an operon, together with six different RBS-linkers (Fig. 1. a). Each of these linkers held a ribosome-binding site of high or low strength for each of the three genes (cf.
Supplementary file 'Dataset 1'); the remaining untranslated region upstream of the RBS provides the overlap sequence that drives the assembly. For consistent RBS context the three untranslated sequences of the linkers were always upstream of the same gene in the assembly. The pathway operon was expressed using one of two different promoters (medium strength PJ23105 and low strength PJ23116). In both laboratories (Paris and London) the scripts were used for the 88 constructs and to spot 10 µL of the transformed cells (E. coli DH5-alpha) on a rectangular LB-agar plate (cf. Lycopene production in Methods section). Of these, only 30 (22 red + 8 white) and 33 (21 red + 12 white) constructs gave transformant colonies at Paris and London respectively (Fig. 1b ). However, only 12 (11 red + 1 white) of these transformants were common across the two laboratories, suggesting that 10 µL may be too low a volume to spot for these transformations.
A list of 77 pathways corresponding to 60 expressed compounds in engineered organisms (E. coli, S. cerevisiae, and P. putida) was collected from the literature (cf. Supplementary file 'Dataset 2'). For each of the 77 collected pathways and each heterologous pathway reaction, we compiled the EC number of the reaction along with the corresponding substrates and products. Each target compound within that list was used to run the Retrosynthesis and Pathways analysis workflows to generate a collection of 5874 predicted pathways that produced the same target molecule in the same host organism as those reported in the literature. Following that, the predicted collection of pathways were compared with their corresponding literature pathways using a matching algorithm described in the 'Supplementary_Text' file.
At the end of this process, among the 7919 pathways, 754 were labeled positive either because their matching score with a literature pathway was above 0. 5 or because they were selected as feasible to engineer by the experts (cf. Supplementary file 'Dataset 3'). Using the literature and expert validated pathways, we developed a machine learning model whose purpose was to evaluate if any given pathway is a valid one or not. To that end, we used a classifier (cf. Machine Learning Global Scoring in Methods section), which returned a global score for each queried pathway. The global score distribution for the dataset of 7919 pathways is given in Fig. 2g  (raw data are in Supplementary file 'Dataset 3'). The classifier exhibited excellent performances with an average cross-validation accuracy of 0.
The number rises to 94% in retrieving literature or expert validated pathways among the top 50 machine learning scored pathways. To assess the advantage of making use of a machine learning scoring schema, we computed a direct score based on (i) the inverse of the pathway length (favoring shorter pathway), (ii) the opposite normalized pathway free energy (favoring high negative values), (iii) the normalized averaged enzyme availability score, and (iv) the normalized FBA calculated product flux value. All these values were calculated using the Pathway analysis workflow (addition information are found in Table  3  in Methods section). A direct score calculation, which does not make use of machine learning, can simply be obtained summing the above four parameters. Results are presented in the Supplementary file 'Dataset 3' (tab 'Lit_Pathway_Rank_noML') where 58% of the identified literature/expert pathways are found within the top 10 scored pathways generated by Galaxy-SynBioCAD, this number is lower than the one obtained when using machine learning (83%).
At our request, a new Galaxy category named 'Synthetic Biology' was created, currently comprising 25 tools stored in the ToolShed. The offering in Galaxy-SynBioCAD focuses on providing tools for pathway design and engineering. However, as Galaxy-SynBioCAD is a community effort, we anticipate our toolset will grow. Regarding pathway design tools, many of the software products listed in the introduction could be considered to be added to the portal. In particular, strain design including knockout genes to maximize targeted product fluxes could easily be implemented via the flux balance analysis tools. Additionally, there are already Galaxy workflows to take up and analyze metabolomics flow cytometry data in the ToolShed  34  , and these workflows could directly be incorporated into the portal to deal with data generated in the 'Test' step of the synthetic biology Design-Build-Test-Learn (DBTL) cycle.
All of the above-suggested additions could be implemented in our portal with relatively small efforts (cf. Tools design and integration process in 'Supplementary_Text' file). There are other applications that could be envisioned beyond pathway design and engineering. For instance, as shown in Delépine et al. 10  retrosynthesis software can easily be adapted to design biosensors. Such an adaptation has been proposed as a Galaxy-SynBioCAD workflow to predict and implement biosensors for the detection of various metabolites using the hydrogen peroxide compound as a metabolic hub, such that the metabolite signals are transduced to hydrogen peroxide before being sensed by the OxyR transcription factor  50. Tools for genetic logic circuits engineering could also be considered. Also, as the cell resources consumption due to the level of expression of heterologous genes are not considered in the global score of a pathway, integrating in the overall design pipeline a tool enabling the prediction of the metabolic burden of genetic constructs based on enzyme expression levels would be of great interest and should be targeted for the future.
Retrosynthesis from target to chassis Typically, the target compound, also named "source compound" is the compound of interest one wishes to produce, while the precursors are usually compounds that are natively present in a chassis strain. In the present implementation, the target can be any chemical that could be described by an InChI, and the chassis should be a metabolic model described in an SBML file. Starting from the source compound at the first iteration, reaction rules matching the chemical structure of the source are applied and newly predicted chemicals are generated. Reaction rules are generic descriptions of (bio)chemical reactions encoded into the community standard SMARTS  51. The use of reaction rules allows estimating the outcomes of chemical transformations based on the generalization of reactions available in knowledge databases such as BRENDA  52  , MetaCyC 52 , Rhea  53  , or MetaNetX  54.
The degree of generalization is controlled by describing the surrounding environment of the reaction center up to a given diameter as described in Duigou et al. 14. To ensure the accuracy of the predicted transformations that will outcome from the reaction rules, the RetroRules dataset provided by the Galaxy RetroRules tool has been validated by (i) checking that rules allow to reproduce the template reactions, and (ii) checking that results obtained by decreasing diameters are supersets of results obtained with higher diameters. Only the reaction rules that successfully passed the 2 checks are retained. The validation of this dataset has a success rate of 99. 3%. For each reaction rule, a score is calculated based on the ability to retrieve enzyme sequences catalyzing substrate to product transformations, the method is detailed in Delépine et al.
Then for each reaction rule, again the algorithm creates a new fork per template reaction used to build the current rule. The enumeration of all forks create a set of slightly different pathways (made of chemical reactions) for one master pathway. To perform the enumeration, datasets from RetroRules and MetaNetX are used. The reaction completion tool (rpCompletion) takes as input the CSV outputs of RP2paths and RetroPath2. 0 and produces a collection of annotated SBML files. Those SBML files are "enriched" with additional information that are not stored as part of the normal SBML schema (cf. Pathway annotation section).
Pathway annotation Some results generated by the workflow produced in this study cannot be readily stored in the SBML files natively (informations about chemical reactions and species, thermodynamic and fluxes properties, as well as pathway information). Using the Minimal Information Required In the Annotation of Models (MIRIAM) conventions, one can store within the SBML file information that instructs the user on the provenance of the reactions and chemical species within the model by cross-references to a wide range of databases. However, this needs a third-party database lookup to match the database ID with its structural information. In this work, intermediate products are generated ad hoc references and may not necessarily have database entries whereas some other information cannot be contained under the MIRIAM annotations as it is: SMILES, InChI, InChIKey for chemical species, reaction rule ID (RetroRules ID), associated template reaction ID, and rule score based on the expected enzyme availability.
As such, we elected to enrich the SBML format in such a way that our information can be stored directly within the SBML file without breaking any standard of the original file. Because SBML files are based on XML, new XML annotations are created outside the standard scope of an SBML file and thus are ignored by any standard SBML readers  55. As a result, this enriched file format (denoted rpSBML) is fully compliant with SBML version 3 specifications. Standard SBML extensions are also used in this project. The "groups" package is used to link the heterologous reactions and chemical species to identify them easily, as well as classifying the chemical species that are main actors in an heterologous pathway  55. The SBML FBA package is used to define the FBA simulation conditions  56. The tools also adhere to the MIRIAM annotation standard for the crossreferences of chemical species to public databases  57.
Thermodynamics Thermodynamics is critical in synthetic pathway design by providing quantitative indicators to determine best metabolic pathways among a set of predicted ones. Thus, one can perform thermodynamic analysis to know whether a reaction direction of a pathway is feasible in physiological conditions. In this work, we performed thermodynamic analysis for species, reactions and pathways. We use eQuilibrator  59  to compute the formation energy of chemical species and the Gibbs free energy for each chemical reaction and the heterologous pathway. For each species involved in a heterologous pathway, the first challenge is to find the corresponding compound in the eQuilibrator database. To find the right compound, we try to exactly match species ID, InChIKey, InChI or SMILES and stop with the first hit.
Then, if no compound is found, in the last resort, the first part of species InChIKey is looked for within the eQuilibrator cache and when the result (a list) is not empty, the first compound is taken. If species have no known structure neither in eQuilibrator database nor in any public one, the user has the possibility to specify substitution for identifier, InChI and InChIKey for these species. This substitution is documented in the SBML file with the group named rp_thermo_sub-stituted_species. If a species has no known structure and is not substituted, then the reaction which involves this species will not have a thermodynamics value. Conversely thermodynamics can be computed by eQuilibrator for all reactions for which all species have been identified. At the level of the pathway, we build a global pseudo-reaction linking chassis substrates to the target molecule and we compute thermodynamics with the eQuilibrator engine for the global pseudoreaction.