dataesr/scientific-paragraphs-categorization huggingface dataset:
9 and 10). Crystal structure of PsFucS1. Although about forty crystal structures of sulfatases have been characterized to date, none are from the sulfatase subfamily S1_13 like PsFucS1 as defined by the SulfAtlas database  17. The X-ray structure of PsFucS1 was solved by molecular replacement at 2. 50 Å resolution (PDB entry 7AJ0), using the human N-acetylgalactosamine-6-sulfatase (GALNS; NM_000512. 4; 4FDI; S1_5) as starting model. The crystals belonged to space group P2 1 2 1 2 1 with six molecules in the asymmetric unit. The RMSD values of Cα atoms between the six copies were low, ranging from 0. 10 to 0. 15 Å, and the monomers can therefore be considered as identical (detailed data statistics are summarized in Supplementary Table  6 ). The overall structure (Fig. 1A ) revealed that each PsFucS1 monomer is organized in a catalytic N-terminal domain (Asn21-Leu419) region and a C-terminal domain (Val430-Gly521) typical of the formylglycine dependent sulfatase fold.
coli DH5α strain (Invitrogen Life Technologies, Thermo Fisher Scientific, Waltham, MA, USA). After plasmid preparation, inserts were sequenced at Macrogen Inc. (Seoul, Korea) using the Sanger capillary sequencing method. Sequences were aligned against 16S ribosomal RNA entries of NCBI RefSeq database to identify the closest taxonomic bacterial identity. Genome sequencing of and sequence assembly. Genomic DNA of the Pseudoalteromonas sp. MB47 strain was isolated as described for the 16S ribosomal analysis. The genomic DNA was submitted to Macrogen Inc. (Seoul, Korea) for Illumina TruSeq DNA PCR-free shotgun library preparation and sequencing using Illumina Miseq (300 bp paired-end). Residual sequencing adapters from resulting reads were removed with Cutadapt 47 and BBDuk  48. After quality filtering, draft genomes were assembled with Ray using a number of different k-mer lengths  49.
Best assemblies were selected based on the assessment of the constructed deBrujin graphs and quality of the resulting contigs. Reads were mapped back to contigs using the Bowtie2 algorithm  50. To further clean assemblies abundance/GC binning was performed with MetaBat  51. The completeness of the resulting draft genome was evaluated using Checkm  52. Draft genome annotation and CAZymes identification protein-coding features and ribosomal RNA features were predicted with Prodigal and barrnap as part of the prokka annotation pipeline. Additionally, all predicted protein sequences were subjected to annotation using the Interproscan pipeline (v5. 27-66. 0), using all available databases and analyses. Taxonomy was investigated by aligning all predicted protein sequences against the NR database.
Analysis of selected phylogenetic protein markers was done using AMPHORA2  53  and analysis of identified sequences encoding 16S rRNA genes. dbCAN models were used to predict CAZymes  54. Identification of sulfatases in the Pseudoalteromonas sp. genome and sequence alignment. Protein-coding features in the Pseudoalteromonas sp. MB47 genome were predicted with Prodigal (used as part of the prokka annotation pipeline)  55  and subjected to annotation using Interproscan pipeline  56  , v5. 27-66. 0), using all available databases and analyses. All signatures corresponding to sulfatase families and domains have been retrieved from the InterPro database  57  and used for extraction of sulfatase protein sequences. Signal peptides were predicted using SignalP 4. 0  58. SulfAtlas (http:// abims. sb-rosco ff.
fr/ sulfa tlas) was used to identify the sulfatase families and subfamilies. Multiple sequence alignments were performed using ClustalW  59  via the NPS@ server (https:// npsa-prabi. ibcp. fr/)  60. Sequences included previously characterized polysaccharide-active sulfatases as well as the human GALNS sequence. The alignment was rendered using ESPript 3. 0  61. Cloning, expression and purification of PsFucS1. Enzymes and gene constructs. Constructs containing the genes encoding the sulfatases were designed without the predicted N-terminal signal peptide but with an N-terminal 6xHis tag. The synthetic codon-optimized genes (optimized for E. coli expression), all devoid of their original signal peptide, were synthesized by GenScript (Piscataway, NJ, USA) and delivered inserted into the pET-45b(+) vector between the KpnI and PacI restriction sites.
9 and 10). Fucoidan desulfation. Desulfation of fucoidans was measured after fucoidan hydrolysis catalysed by different fucoidanases in reaction mixtures containing 0. 5 µg µl -1 fucoidanase (FcnA, FdlA and FdlB) in 20 mM Tris-HCl buffer pH 7. 4, 250 mM NaCl, 10 mM CaCl 2 and 1% (w/v) fucoidans from either S. mcclurei or F. evanescens at 35 °C and 68 °C for 24 h. The PsFucS1 (1 mg ml -1 ) was then added to the reaction resulting in the following reaction mixture: 125 mM NaCl, 10 mM CaCl 2 , 60 mM Tris-HCl pH 6. 5, 0. 5% (w/v) fucoidan. Desulfation data were compared by one-way analysis of variance (ANOVA) using Tukey-Kramer's Significant Differences at p ≤ 0. 05 using JMP 15 Pro (SAS Institute Inc. , Cary, NC, USA).
The release of sulfate was related to the amount of sulfate present in the polysaccharide substrate, 33 ± 3% in S. mcclurei 2 and 30 ± 2% in F. evanescens  45. Crystallization and structure determination. PsFucS1 crystals were grown in a hanging drop set up at 20 °C using 2 µl protein solution (6 mg ml -1 ) and 1 µl mother liquor solution containing 0. 1 M sodium acetate pH 4. 6, 0. 1 M sodium chloride and 20% (w/v) PEG 3350. Crystals were flash-frozen and stored in mother liquor supplemented with 14% (v/v) glycerol prior to diffraction experiments. Diffraction data were collected on the PROXIMA-2A beamline at the SOLEIL Synchrotron. Data were processed using XDS  65  and scaled and merged with Aimless 66 in CCP4  67. An initial homology model was generated from the SWISS-MODEL server  68  based on the N-acetylgalactosamine-6-sulfatase X-ray structure from human (4fdi)  69  , with which PsFuc1 shares 25% sequence identity.
Only residues with a predicted local similarity to target > 0. 6 were kept for molecular replacement. The PsFucS1 crystal structure was then solved by molecular replacement using phenix. phaser  70. Density modification and model building was carried out with phenix. autobuild  71. Refinement was carried out using phenix. refine  72 interspersed with manual model revisions using the program Coot  73. All statistics are summarized in Table  S6.
Acknowledgements This work was supported by grants from  Vietnam National Foundation for Science and Technology Development  ( NAFOSTED-106. 02-2018. 353 ). This study was part of the FucoSan-Health from the  Sea  Project, supported by  EU InterReg-Deutschland-Denmark  and the  European Fund of Regional Development. This project was also funded by  MARIKAT JPI Cofund Blue BioEconomy Project  Grant No. 9082-00021B  and by the  Technical University of Denmark. M. C. and T. R. acknowledge support from  ANR  under grant  ANR-10-BTBR-04  (investment expenditure program IDEALG). The authors appreciate the access to the CristalO platform ( FR2424 ,  Station Biologique de Roscoff ), which is part of the  Biogenouest core facility  network and supported by the  Conseil Regional Bretagne  (Grant No:  20000943 ).
Probabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools. org), a Python package that implements a variety of leading probabilistic methods.
These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles.
Despite the appeal of probabilistic models, several obstacles impede their community-wide adoption. The first obstacle, coming from the perspective of the end user, relates to the difficulty of implementing and running such models. As contemporary implementations of probabilistic models leverage modern Python machine learning libraries, users are often required to interact with interfaces and objects that are lower-level in nature than those utilized in popular environments for single-cell data analysis like Bioconductor  [33] , Seurat  [34] , or Scanpy  [35]. A second obstacle relates to the development of new probabilistic models. From the perspective of developers, there are many necessary routines to implement in support of a probabilistic model, including data handling, tensor computations, training routines that handle device management (e.
g. , GPU computing), and the underlying optimization, sampling and numerical procedures. While higher-level machine learning packages that automate some of these routines, like PyTorch Lightning  [36]  or Keras  [37] , are becoming popular, there still remains overhead to make them work seamlessly with single-cell omics data. To address these limitations, we built scvi-tools, a Python library for deep probabilistic analysis of singlecell omics data. From the end user's perspective (Section 2. 1), scvi-tools offers standardized access to methods for many single-cell data analysis tasks such as integration of single-cell RNA sequencing (scRNA-seq) data (scVI  [5] , scArches  [38] ), annotation of single-cell profiles (CellAssign  [39] , scANVI  [12] ), deconvolution of bulk spatial transcriptomics profiles (Stereoscope  [40] , DestVI  [41] ), doublet detection (Solo  [42] ), and multi-modal analysis of CITE-seq data (totalVI  [21] ).
All twelve models currently implemented in scvi-tools interface with Scanpy through the annotated dataset (AnnData  [43] ) format. Furthermore, scvi-tools has an interface with R, such that all scvi-tools models may be used in Seurat or Bioconductor pipelines. Here we also demonstrate two new features that add important capabilities for the analysis of large, complex datasets. These two features are accessible for several of the implemented models, and thus, applicable to several types of omics data. The first feature offers the ability to remove unwanted variation due to multiple nuisance factors simultaneously, including both discrete (e. g. , batch category) and continuous (e. g. , percent mitochondrial reads) factors. We apply this in the context of an scRNA-seq dataset of Drosophila wing development (Section 2.
2), which suffered from nuisance variation due to cell cycle, sex, and replicate. The second feature extends several scvi-tools integration methods to iteratively integrate new "query" data into a pre-trained "reference" model via the recently proposed scArches neural network architecture surgery  [38]. This feature is particularly useful for incorporating new samples into an analysis without having to reprocess the entire set of samples. We present a case study of applying this approach with totalVI by projecting data from COVID-19 patients into an atlas of immune cells (Section 2. 3). From the perspective of a methods developer, scvi-tools offers a set of building blocks that make it easy to implement new models and modify existing models with minimal code overhead (Section 2. 4). These building blocks leverage popular libraries such as AnnData, PyTorch  [44] , PyTorch Lightning  [36] , and Pyro  [45]  and facilitate (deep) probabilistic model design with GPU acceleration.
scvi-tools is a unified resource for single-cell omics data analysis The single-cell omics data analysis pipeline is composed of several steps  [46, 47]  (Figure  1a ). First, data are staged within a data object using packages like Scanpy  [35] , Seurat  [34]  or scater  [48]  and preprocessed with quality control filters. The data are then analyzed through a variety of subsequent steps, which aim to normalize, simplify, infer structure, annotate, and extract new insight (Figure  1b ). scvi-tools aims to provide a rich set of methods for these latter steps, while relying on probabilistic models for statistically sound interpretation of data. The models currently implemented in scvi-tools can perform normalization, dimensionality reduction, dataset integration, differential expression (scVI  [5, 49] , scANVI  [12] , totalVI  [21] , PeakVI  [24] , LDVAE  [50] ), automated annotation (scANVI, CellAssign  [39] ), doublet detection (Solo  [42] ), and deconvolution of spatial transcriptomics profiles (Stereoscope  [40] , DestVI  [41] ).
These models span multiple modalities including scRNA-seq (scVI, scANVI, CellAssign, Solo), CITE-seq  [51]  (totalVI), single-cell ATAC-seq (PeakVI), and spatial transcriptomics (Stereoscope, gimVI  [52] , DestVI  [41] ) (Supplementary Table  1 ). Importantly, these models make use of stochastic inference techniques and (optionally) GPU acceleration, such that they readily scale to even the largest datasets. Each model also comes with a simple and consistent application programming interface (API). They all rely on the popular AnnData format as a way to store and represent the raw data (Figure  1b ). Consequently, scvi-tools models are easily integrated with Scanpy workflows. This also enables users to interface with AnnData-based data zoos like Sfaira  [53]. Furthermore, the globally consistent API of scvi-tools allows us to maintain a reticulate-based  [54]  workflow in R, such that scvi-tools models may be used directly in Seurat or Bioconductor workflows.
Therefore, after running methods in scvi-tools, results can be visualized and further assessed with a broad range of analysis packages like Scanpy, Seurat, VISION  [55] , and cellxgene  [56]. Datasets may contain multiple layers of omic information, along with metadata at the cell-and feature-levels. Quality control (QC) and preprocessing are done with popular packages like Scanpy, Seurat, and Scater. Subsequently, datasets can be analyzed with scvi-tools, which contains implementations of probabilistic models that offer a range of capabilities for several omics. Finally, results are further investigated or visualized, typically through the basis of a nearest neighbors graph, and with methods like Scanpy and VISION. b, (left) The functionality of models implemented in scvi-tools covers core single-cell analysis tasks.
Nonlinear removal of unwanted variation due to multiple covariates As the quantity, size, and complexity of single-cell datasets continues to grow, there is a significant need for methods capable of controlling for the effects of unwanted variation  [58]. Some factors that contribute to unwanted variation depend directly on the data generating process, such as differences between labs, protocols, technologies, donors, or tissue sites. Normally, such nuisance factors are observable and available as sample-level metadata. Unwanted variation can also come at the level of a single cell and can be calculated directly from the data. It can stem from technical factors like quality as gauged by proxies such as the abundance of mitochondrial RNA or the expression of housekeeping genes, as well as biological factors like cell cycle phase.
Nuisance factors can come in either categorical or numerical form. These factors can affect the data in a nonlinear manner  [59] , and controlling for them is essential for most forms of downstream analysis. Many methods have been proposed for removing unwanted variation, but most target the subtask of dataset integration, which consists of controlling for one categorical confounding factor at the sample-level, like sample ID  [60, 61]. Harmony  [15]  is, to the best of our knowledge, the only method capable of nonlinearly controlling for multiple categorical covariates simultaneously. While the focus of the integration task is on categorical factors, some pipelines provide an additional layer of normalization, where a given numerical confounder (typically, though not limited to the cell-level) can be regressed out prior to batch correction using a linear model (e.
To demonstrate this capability, we applied scVI to a dataset of Drosophila wing disc myoblast cells  [63]  that suffered from strong effects due to cell cycle and sex of the donor organism, both of which were observed via a set of nuisance genes (i. e. , numerical covariates; Supplementary Table  2 ). Both of these confounding factors are observed at the cell-level, as these cells were taken from batches of fly larva and were processed together without sex sorting. The dataset also featured a sample-level confounding factor (batch ID; two batches) and non-nuisance factor (developmental time point; Figure  2d ). When scVI was trained only conditioned on the batch covariate, we observed that it successfully integrated each batch, however the effects from PCNA, a cell cycle gene, and IncRNA:roX1, a gene that is expressed by males (Figure  2c ), still manifested in the latent space.
While we found a tradeoff between retaining biological signal of marker genes and removing nuisance variation across all workflows, these results demonstrate that scVI strikes a balance in removing unwanted variation and retaining wanted variation. The scVI workflow with multiple covariates is also more scalable than the alternatives; we evaluated scalability using subsampled versions of the Heart Cell Atlas dataset  [66]  that had categorical covariates like donor and continuous covariates like cell-level mitochondrial count percentage (Supplementary Figure  2 ; Methods).
Transfer learning for reference-query integration with scArches While dataset integration provides a way to leverage information from many sources, current methods do not scale well to the subtask of reference-query integration in which a "query" dataset is integrated with a large, annotated "reference" dataset. This is an increasingly common scenario, however, that is driven by community efforts for establishing consolidated tissue atlases. These atlases are meant to be used as general references of cell states in a given tissue and may consist of millions of cells  [67]. scArches  [38]  is a recent method that was developed to address this scenario. scArches leverages conditional (variational) autoencoders and transfer learning to decouple the reference-query integration task into two subproblems: First, a reference model is trained on the reference data only; and second, the neural network from the reference model is augmented with nodes that are only influenced by the query data.
This new part of the network is subsequently trained with the query data, resulting in a joint model that describes both the train and the reference datasets, while correcting for their technical variation. This procedure dramatically reduces the computational burden of dataset integration. Assuming a pre-trained reference model is available (e. g. , representing the "atlas" of cell state for a particular tissue), one only needs to process the (typically much smaller) query data. Another advantage is that the addition of the query data does not change the representation of the reference data in the joint model. Beyond the aforementioned reference-query scenario, this property is also useful in studies where data is accumulated gradually as it eliminates the need for reanalysis when new samples are collected.
We implemented the scArches method in scvi-tools through the addition of one class called ArchesMixin. This class contains a generic procedure for adding new nodes to a given reference model, as well as appropriately freezing the nodes corresponding to the reference dataset during training. The ArchesMixin class can therefore be inherited by scVI, scANVI, totalVI, and peakVI, without any other custom code. From a user's perspective, this inheritance adds one function called load_query_data to each of these models that is used to load a pre-trained reference model with new query data. We applied scArches-style reference-query integration with totalVI in order to quickly annotate and then interpret a dataset of immune cells of the blood in donors responding to COVID-19 infection  [68]  (Figure  3a , Methods).
First, we used totalVI to train a reference model of immune cell states, using an annotated CITE-seq dataset of 152,094 peripheral blood mononuclear cells (PBMCs) with over 200 surface proteins  [69]. Next, we applied scArches to augment the reference model with an additional CITE-seq dataset of 57,669 PBMCs with 35 proteins from donors with moderate and severe COVID-19, as well as healthy controls  [68]. Running totalVI in this setting is made straightforward with the scvi-tools interface and required only a few lines of code (Figure  3a ). After query training, we visualized the joint latent representation of the reference and query cells using UMAP (Figure  3b, c ). We transferred the annotated cell type labels from the reference cells to the query cells using a random forest classifier operating on the 20 dimensional joint latent space.
CD4 CTLs have not been as well characterized in terms of their response to COVID, but the predicted CD4 CTLs had relatively high expression of cytotoxic molecules like PRF1, GZMB, GZMH, GNLY, and were found to be most prevalent in donors with moderate COVID. This pattern is consistent with evidence suggesting that the presence of CD4 CTLs is associated with better clinical outcomes in other viral infections in humans  [72] , though more targeted study designs may be necessary to better understand this relationship  [73]. Overall, these results suggest that integrating with reference atlases can lead to a more rapid, and potentially more accurate and consistent annotations of cells across studies. Finally, we compared the totalVI-scArches approach to default totalVI, namely training totalVI in one step, using both the reference and query datasets.
scvi-tools accelerates probabilistic model development scvi-tools has a convenient programming interface for rapid construction and prototyping of novel probabilistic methods, built on top of PyTorch  [44]  and AnnData  [35]. The primary entry point is the Model class, which includes all the components needed to fully specify a new probabilistic model. To ensure flexibility, we implemented the Model class in a modular manner through four internally used classes (Figure  4a ). The Module class specifies the probabilistic form of the model (Figure  4b ) and contains the elementary calculations that make up the generative model and the inference procedure, including the objective function to optimize during training (e. g. , log likelihood or a lower bound thereof). The TrainingPlan class defines the procedure for training the model (Supplementary Figure  4a ).