dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, considering only orthology-based draft networks, it is possible to continue investigations and to associate enzymatic reactions to their metabolic network origin. Fig  5B  illustrates the query result: over the 790 enzymes associated with a reaction coming from orthology information, only 77 reactions are associated to the four metabolic network models. 388 reactions originate from E. siliculosus, reflecting reaction identifiers mapping was facilitated by the use of the same reference database (MetaCyc). More generally, semantic-based queries and wiki were used to study the specificity of the T-Iso metabolic network. Indeed, the final T-Iso metabolic network led to the identification of a specific antioxidant metabolite: carnosine. In mammals, due to its antioxidant action, carnosine is an essential compound preventing brain neurodegeneration.
Customizing, tracing, and exploring GSM reconstruction Quality GSMs are true knowledge bases integrating both genomic and experimental data of studied organisms. They allow a global picture of an organism's metabolism, predict growth phenotypes and guide their study. The added value inherent to a GSM is lost if its reconstruction cannot be reproduced or if the information it contains cannot be fully accessed. Unfortunately, this is the case for many of the GSMs that have been generated to date  [58]. This problem arises in part because the reconstruction of genome-scale metabolic networks cannot be made without the use of many different tools and databases required to extract the most information possible from available data. Moreover, manual curation steps are also required to generate good quality models.
Unfortunately, there is usually no standardized record or metadata related to these steps or of how and when different tools are used in the reconstruction process making it hard to track and reproduce. We introduce here AuReMe, a workspace dedicated to the generation of GSMs that offers solutions to the aforementioned concerns related to their transparency, traceability, reproducibility and exploration  [17]. The main objective of AuReMe is to keep track of all metadata generated during the reconstruction of the GSM, either metadata linked to the model or its reconstruction process. Reconstruction can be done with high flexibility, both in terms of the database (MetaCyc, BiGG, Model SEED) and the tools/pipelines used for reconstruction. Special attention was given to potential manual curations performed in the network.
Manual curations are usually the main metadata to be lost when sharing a model; thus, we simplified and traced the curations via the creation of forms to manually modify the model. AuReMe workspace relies on three levels. i) The database representing the model is the core, as it gathers all data and metadata and is the cornerstone to every application of tool, analysis or modification to the model. ii) The wiki for the visual exploration of the model is a new way to explore large-scale data at multiple levels. iii) The ability to explore more deeply and query the model using RDF standards enables acute analyses to answer biological questions. Altogether, AuReMe offers solutions for GSM reconstruction made to suit user expectations in a world of data in which an emphasis is placed on sharing, tracing and exploration.
Positioning in the galaxy of GSM reconstruction platforms The structure of metadata associated to GSM reconstruction can be viewed as a combination of the best practices of two GSM analysis platforms. First, the PADMet format can be viewed as an extension of the content of the data files produced by the Pathway Tools platform  [13]. In addition to the information related to genome annotation, our approach enables a user to incorporate in the data format any additional information provided by orthology-based, functional gap-filling or curation steps. The traceability and flexibility of the reconstruction procedure is also close to the concept of narratives introduced in the Kbase platform. The AuReMe approach shows more flexibility in terms of references since a user can rely on any metabolic network knowledge repository, in contrast to the Kbase platform which exclusively relies on TheSeed database  [10].
Similarly, the narratives of the Kbase platform enable the partial investigation of metabolic network attributes (reactions, compounds) through the availability of internet links to The Seed environment databases. In addition, some information about the methods used to assert the presence of a reaction in the network are provided in structured tables which are very close to the structure of the AuReMe wiki. The added values of the AuReMe technology are threefold. First, it enables the exploration of the full content of the metabolic network metadata either on a local computer or on a shared webserver. Second, the PADMet library makes it possible to enrich the wiki with any additional information introduced as an attribute to the PAD-Met format. In particular, when using the MetaCyc database, AuReMe enables the exploration of each pathway according to missing reactions, which facilitates the network curation.
Thirdly, the capability of exporting the metadata information into a RDF triplestore is in the same line as the semantic analysis modules of the BioCyc database  [8]. The main advantage is still increased flexibility since the triplestore can be designed according to user interests. For instance, the metabolic networks for several related species can be stored in the same triplestore in order to facilitate the comparison of their networks with ad-hoc SPARQL requests. The triplestore can also be enriched either with external linked open data (e. g. KOG annotation for enzymes according to their EC numbers, from the KEGG database) or with new data (e. g. expression data in response to several stresses) in order to identify the main pathways associated with particular biological phenotypes. The main weakness of the AuReMe workspace, though, is the command-line interface yielded by the Docker technology.
Dependencies to knowledge repositories The main requirement of the AuReMe workspace is the need to select a reference database to ensure the compatibility of all the information introduced in the reconstruction process. This implies that used annotation based draft reconstructions as well as the metabolic networks of template organisms used for orthology-based reconstruction have to be preliminarily conciliated with the reference database. This means that all metabolite and reaction identifiers must be those from the reference database. Even though important progress has been made  [45, 59] , to date, the automatic simultaneous use of more than one database is an unresolved challenge and the correct consolidation of the information still requires significant manual effort  [60]. This is because the translation of reaction and metabolite identifiers from one database to another is difficult.
Metabolites in different databases are not always presented with the same name or even the same chemical formula or charge. Additionally, equivalent reactions in different databases do not always have the same stoichiometry. If this is not treated carefully, the result can be artefacts in the reconstructed metabolic networks that lead to unrealistic representations of the studied organisms. Moreover, since the universe of reactions and metabolites is large and not fully explored, available databases are not completely exhaustive and therefore many GSMs include a large number of manually annotated compounds and reactions. If they are to be exploited as templates in a reconstruction process, adapting them to fit the identifiers used in the reconstruction is unavoidable. Given this scenario, we believe that currently, the use of a previously selected and, if needed, adapted reference database throughout the reconstruction process is the best way to assure its traceability and reproducibility.
The PADMet library and PADMet-utils We developed PADMet, a Python library designed to manage metabolic networks in an attribute-based format. This format allows all the biological data to be stored relative to the metabolic network but also the metadata relative to the reconstruction workflow as shown in Supp. The PADMet-utils is a suite of scripts based on the previous library to link admissible input data to the customized workflows and the various analysis tools available in the workspace. It contains four main modules for data management, connection to software, manual curation assistance and model exploration/analysis (S1 File). Regarding the latter, the library proposes several tests to assess the quality of the reconstruction: Flux Balance Analysis, Flux Variability analysis (analysis of essential and blocked reactions), percentage of reactions associated to a gene, determination of mass and/or charge unbalanced reactions using Cobrapy  [18].
Embedded technologies in the AuReMe workspace The AuReMe workspace embeds existing tools as well as ad-hoc packages developed to facilitate the interaction between tools (PaDMet, Menetools). We used the Docker technology (https://docker. com) to encapsulate the AuReMe virtual environment as container which can be run on any platform (MacOS Yosemite or later, Windows 10, Linux). The following software were installed in the AuReMe workspace: Blastall (v2. 2. 17)  [68] , CobraPy (v0. 5. 11)  [18] , Inparanoid (v4. 0)  [21] , Meneco (v1. 5. 0)  [57] , OrthoMCL (vmcl-02-063)  [20] , Pantograph (v0. 2)  [22]  and PSAMM (v0. 28)  [19]. In addition, the Docker image was developed to generate another Docker image which encapsulates MediaWiki technologies (https://mediawiki. org) in order to produce the representation of the metabolic model through local wiki webpages (see S1 File for further details).
AuReMe environment user interface and customizability For all reconstructed networks, The GSM reconstruction workflow was described in a configuration file, which handled the reconstruction process by running simple commands (see details in S1 File). A local database is required to standardize the dataflow during the reconstruction process and to feed the gap-filling and curation steps. The databases Meta-Cyc 20. 0 and BiGG 2. 3 were used for the reconstruction of the metabolic networks. They are by default included in the current version of the AuReMe workspace, together with the Model SEED database. Notice, however, that the user can alter or import his/her own database from any SBML file. In order to make the workflow compliant with other pre-installed tools (e. g. running Fas-tGapfill from PSAMM instead of the topological Meneco gap-filling; or running OrthoMCL instead of Pantograph), a simple change in the configuration file is required.
Acknowledgments We wish to thank  Guillaume Collet  for his work on the premises of AuReMe. We also acknowledge  Simon M. Dittami  ( Station Biologique de Roscoff ) for the expertise and analysis support in the reconstruction process of Ectocarpus siliculosus metabolic network and  Caroline Baroukh ,  Elodie Nicolau  and  Bruno Saint-Jean  ( IFREMER-PBA ) for their biological expertise on microalgae.
This work was supported by the  French Government  via the  National Research Agency investment expenditure program IDEALG  [ ANR-10-BTBR-04 ] (http://www. agence-nationalerecherche. fr/? ProjetIA=10-BTBR-0004 ); the  INRIA  Project  Lab Algae-In-Silico  (https://project. inria. fr/iplalgaeinsilico/); and the  Fondecyt program  [11150679] (http://www. conicyt. cl/fondecyt/)and CONICYT doctoral scholarship [21140822] (http:// www. conicyt. cl/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction The current understanding of the relation between the sequence of a protein and its structure remains limited. As of November 2018, we know a lot about protein sequences (more than hundred twenty millions of them, if one refers to RefSeq, the NCBI Reference Sequence Database, https: //www. ncbi. nlm. nih. gov/refseq/, [1] ), more and more about their structures (there are more than 130,000 structures of proteins in the Protein Data Bank, PDB, http://www. rcsb. org, [2] ), but we still have difficulties in deciphering the rules that relate a sequence to its corresponding three-dimensional structure, a challenge usually referred to as the "holy grail" of computational structural molecular biology  [3]. In this paper, we focus on one of those rules, namely the prediction of geometric proximity of residues (also called residue contacts) in a protein structure from the variability of the amino acid sequence of that protein within its family.
Results We have implemented the multivariate Gaussian model described above in a program called GaussCovar, following the algorithm described in the Material and Methods section. GaussCovar mimics the program GaussDCA proposed by Baldassi et al. [27] , with the significant difference that we explore different vector representations of the amino acids, when converting a multiple sequence alignment into a numerical matrix from which contacts can be predicted. In this section, we describe and apply some of those representations, from the binarized 20-dimensional vectors originally used by Baldassi et al. [27]  and by Jones et al. [26] , a single value for each amino acid, derived from the AAindex database  [40, 41] , to vectors derived from projections of BLOSUM matrices onto their principal components.
We are particularly interested in assessing the balance between performance, and the need to add prior information to the Gaussian model. GaussCovar takes as input a multiple sequence alignment and outputs a list of predicted contacts, in decreasing order of scores. Just like GaussDCA, GaussCovar is fast: Its most computer intensive parts are the computations of the sequence weights, of order N 2 where N is the number of sequence in the MSA, and the computation of the inverse of the covariance matrix, of order L 3 where L is the length of the sequences in the MSA. We have tested GaussCovar with different amino acid encodings on two different datasets, each with 150 families. We used two different datasets to assess whether the results we obtain are consistent over those datasets, in which case they are likely indicators of the properties of GaussCovar and of the amino acid encodings, or different on those datasets, in which case they are more likely to be indicators of confounding effects specific to the construction of the datasets.
The first dataset, PSICOV, was originally used for assessing the performance of the namesake program PSICOV  [26]. The MSAs in PSICOV have a number of sequences N between 511 and 74836, and a length L between 50 and 266. In parallel, we have used NOUMENON, a more recent dataset based also on 150 protein families, significantly different however in its conception than PSICOV  [42]. NOUMENON is designed to be as bias-free as possible, by only considering protein sequences whose homologues in the MSAs have little, or no structural information available in the PDB, the database of protein structures. As such, NOUMENON is expected to mimic more realistic applications of residue contact predictions. NOUMENON contains MSAs with a more diverse number of sequences than PSICOV, ranging from N = 2 to N = 513, 407, but with similar lengths L, between 64 and 275.
Note that the actual number of sequences may be misleading and that a better measure of the information content of a MSA is its effective number of sequences, M e f f. This will be discussed below. PSICOV and NOUMENON are described in more details in the Material and Methods section below, with links to where their authors have made them available. Results are presented as averages over the 150 protein families for each dataset, unless specified. In particular, we generate the precision or positive predictive value (PPV) curves as functions of the number of predicted contacts considered. More specifically, PP(k) is the rate of true positive contacts among the top k predicted contacts, where "true" refers to a contact that exists in a gold standard protein structure, i. e. , a contact between two residues that are close in space (see Material and Methods for details).
Amino Acid Representation 1: 20-Dimensional Binarized Vectors As a first test, we ran GaussCovar on the two datasets of 150 protein families presented above using a simple twenty dimensional (20D) representation of amino acids. To emphasize that we have used a 20D representation, we refer to these experiments as Gauss20. In this representation, an amino acid of type i is represented with a vector of 20 zeros, except at position i where it is given the value 1. In Figure  1 , we compare the performances of Gauss20 for two different values of λ (the weight of the prior, see Equation (  7 )), namely λ = 0. 8 and λ = 0. 2, against the performance of the PSICOV program, which uses the same representation of amino acids. Results for PSICOV on its namesake dataset were kindly provided by its authors  [26] , along with the dataset itself.
In parallel, we obtained the program PSICOV (http://bioinfadmin. cs. ucl. ac. uk/downloads/PSICOV/), and ran it over all 150 MSAs in the NOUMENON dataset. Following the advices provided in the original PSICOV paper  [26] , we set the lasso regularization parameter ρ to a constant value of 0. 001 for all MSAs with more than 100 sequences. We found that PSICOV struggles to converge on a solution for MSAs with a smaller number of sequences; for all of those, we increased the ρ parameter to 0. 01. We observe similar levels of performance between PSICOV and Gauss20 with λ = 0. 8 (the two-tailed t-probabilities computed when comparing the distributions of Gauss20 (λ = 0. 8) and PSICOV results over the 150 families are always greater than 0. 05 at all levels of contacts considered), and significantly lower performance for the latter when λ is set to 0.
2 (where "significance" comes from the fact that the two-tailed t-probabilities computed when comparing the distributions of Gauss20 (λ = 0. 8) and Gauss20 (λ = 0. 8) results over the 150 families are smaller than 0. 05 at all levels of contacts considered, with the exception of the first few data points, when the number of contacts is below 5). Similar relative differences between the performances of Gauss20 (λ = 0. 8) and Gauss20 (λ = 0. 2), and between the performances of Gauss20 (λ = 0. 8) and PSICOV are observed between the two datasets considered. Of interest, however, we note that their absolute performances vary greatly from one dataset to the other, with all three methods performing better on the PSICOV dataset, with approximately a 50% performance drop on the NOUMENON dataset. This behavior was already reported  [42]  for the PSICOV program on the same datasets, and for the program CCMpred that implements a method derived from statistical mechanics  [43].
The drop is assumed to be related to over-estimation of the performances of contact prediction methods on the PSICOV dataset due to bias in the selection of the protein families it contains, a bias that was presumably removed when designing the NOUMENON dataset. PPV(k) is the rate of correctly predicted contacts within the first k contacts. Data for the Gaussian models were generated with GaussCovar, with a 20-dimensional binarized vector representing amino acids, with λ = 0. 8 (black curve), or λ = 0. 2 (red curve). Data for the PSICOV predictions (blue curve) on the PSICOV dataset were obtained from the data provided by the authors  [26] , while the similar data on the NOUMENON dataset were generated by running directly PSICOV (see text for details). All PPV curves are arithmetic means over 150 protein families.
The shaded areas represent standard deviations. To further investigate the impact of λ, we explored the whole range of its values between 0 and 1, with a step size of 0. 1. Results are shown in Figure  2. The performance of the Gaussian model Gauss20 is clearly improved as λ is increased, with a peak at λ = 0. 8, for both the PSICOV dataset and the NOUMENON dataset. As above, we see a drop of performance on the latter. The need for a large value for λ was already described for GaussDCA and is therefore not a surprise here as GaussCovar is equivalent to GaussDCA when a 20D binarized representation of amino acids. The need for a high value for λ can be assigned to the large sparsity of the numerical representation of MSAs with this amino acid representation. Even for MSA with a large number of sequences, it is unlikely that all amino acids have been observed at each position of the MSA.
It is interesting however that the addition of a very simple prior improves significantly the performance of GaussCovar. This prior by itself has no predictive power, as observed in Figure  2  for λ = 1. 0. The 150 protein families included in each of the two datasets considered are quite diverse, within the dataset themselves, and between the datasets. They include alignments with a number of sequences N varying from 511 to 74,836 for PSICOV, and between 2 and 513,407 for NOUMENON. We note, however, that N is not a good measure of the information content of a multiple sequence alignment: An MSA with a large number of very similar sequences will likely not be more informative (with respect to contact prediction) than a MSA with a smaller number of sequences, but with more diversity. One way to account for this effect is to consider the effective number M e f f of sequences of a MSA.
There are many ways to define this effective number (see for example  [44] ). Here we rely on the definition provided by Baldassi et al. [27] ; we briefly explain how it is computed in the Material and Methods section. To assess whether the need to add the prior differs for alignments with different information contents, we repeated the analyses presented above on three subsets of the families, namely those with M e f f < 500 (small MSAs), with 500 < M e f f < 1000 (medium MSAs), and with 1000 < M e f f (large MSAs). We note that the splitting up of the two datasets into those three groups are very different, with approximately equal sized subgroups for PSICOV, with 56, 47, and 47 as small, medium, and large MSAs, respectively, and skewed distributions for NOUMENON, with 97, 14, and 39 as small, medium, and large MSAs, respectively, i.
e. , with many more small MSAs. Results of the analyses of the importance of the parameter λ on all three groups of MSAs, for the two datasets we consider are shown in the same Figure  2. We observe the same behavior over all groups of MSAs, namely that the performance of the Gaussian model is improved as λ is increased, with a peak at λ = 0. 8. These results do mirror those presented in  [27]  that were computed using the same method, but on a different dataset of protein families. The overall performances however differ in the different groups, with a significant improvement in the large MSAs group compared to the small MSAs group, as intuitively expected. The same behavior is observed on the two datasets, however with much larger differences between the three groups of MSAs for the NOUMENON dataset.
Results for the large MSA are very similar over the two datasets, indicating that a large information content in a MSA is usually sufficient to generate good contact prediction results, independent of additional structural information. The MSAs in our dataset are characterized by two parameters, namely their size, N (or more appropriately their effective size, M e f f ), and their length, L. We have seen above that the larger M e f f , the better the performance of GaussCovar. To assess the importance of L, we compared the averaged AUC200 values for GaussCovar over different ranges of L, over the three groups of MSA effective sizes defined above, and over the two datasets PSICOV and NOUMENON, with λ set to 0. 8. Results are shown in Figure  3. The performance of GaussCovar improves as the lengths of the alignments increases, for the two datasets.