dataesr/scientific-paragraphs-categorization huggingface dataset:
A comprehensive and consistent dataset for dynamic shifts between nitrogen sources To elucidate the dynamic interplay between the signaling, transcriptional, and metabolic networks controlling the cellular response to the quality of the external N-source, we subjected S. cerevisiae YSBN6 wild-type cells  (Canelas et al, 2010)  to nutritional upshift and downshifts that were each done as fully independent biological triplicate experiments. Dynamic shifts in well-controlled bioreactor batch cultures with glucose minimal medium were induced by adding glutamine to yeast growing exponentially on the poor N-source proline (upshift) or by glutamine depletion in yeast growing on glutamine plus proline (downshift) (Fig 1A, Supplementary Tables  S1  and S2 ). In the upshift, proline was nearly depleted at the time of the shift, but it still sustained exponential growth, and added glutamine was almost immediately taken up.
Since all 20 TOR-related signaling proteins showed constant abundances during the shifts (Supplementary Fig  S3 ), we assessed the consequences of signaling at the metabolic and transcription level. Altogether, 3,203, 1,585, and 2,104 transcript abundances changed by more than two-fold (maximum observed fold-change across the time course) in the upshift, the nutritional, and the rapamycin-induced downshift, respectively. We tested the consistency of our dataset across experiments by principal component analysis of transcript dynamics for the 909 metabolic enzymes  (Herrgard et al, 2008). The first two principal components, which capture ~60% of the variance, indicated that the cellular states at the start and end points of up-and downshifts were in close vicinity (Fig 1C ; see Supplementary Fig S4 for all genes).
Similarly, all the "downstream" network's prototypic feature values are maximal for all three experiments because TORC1 should control the same gene sets transcriptionally, and induce metabolite changes post-translationally in all three conditions. For a metabolite to regulate transcription in a TORC1-independent manner ("parallel network"), DD should be high in the two nutrient shifts, but essentially random upon rapamycin treatment (because rapamycin affects only TORC1 activity, not the metabolite concentration), and RG needs to be random in all experiments. Finally, when all feature values of a metabolite are random for all three experiments, we postulate that metabolic and transcriptional changes are not related. To assign the measured metabolites to the four motifs, we considered every pair of metabolite and transcript in our dataset  (Fig 4C).
ª In contrast to earlier approaches, our co-design of perturbation experiments and a probabilistic analysis framework integrates heterogeneous dynamic data and exploits existing network knowledge (e. g. TORC1 controlled genes), prototypic interaction classes (network motifs), and approximations of biological control mechanisms (dynamic dependence). We expect that our approach for model-based data integration will extend to other (nutrient) signaling networks because, while the formalization of the prior knowledge is application specific, the approach itself is generic: target sets, network motifs, and temporal relations of events and variables could be formulated analogously for other applications. Note, however, that the approach may not be applicable to existing datasets in retrospect: a co-design of experiments is critical, for example, because equivalent perturbations of signaling via natural inputs and drugs (here: amino acids and rapamycin, respectively) are needed for the inference approach.
LC-MS/MS (targeted metabolomics) Liquid chromatography coupled with mass spectrometry was adopted from Buescher et al (2010), including the selected reaction monitoring (SRM) transitions used (Supplementary Table  S7 ). Specifically, liquid chromatography separation was achieved by an ion pairing-reverse phase method implemented on a Waters Acquity UPLC (Waters Corporation, Milford, MA, USA) using a Waters Acquity T3 end-capped reverse phase column with dimensions 150 × 2. 1 mm × 1. 8 lm (Waters Corporation). The metabolites were ionized with a heated electrospray ionization source and detected with a Thermo TSQ Quantum Ultra QQQ mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) using a heated electrospray ionization source. The MS was operated in negative mode with SRM. Data acquisition and peak integration were performed with the Xcalibur software version 2.
, P/N 900720) containing hybridization controls and control oligonucleotide B2 (Affymetrix Inc. , P/N 900454). Samples were hybridized to GeneChip â Yeast 2. 0 arrays for 16 h at 45°C. Arrays were then washed using an Affymetrix Fluidics Station 450 FS450_0001 protocol. An Affymetrix GeneChip Scanner 3000 was used to measure the fluorescent intensity emitted by the labeled target. The raw CEL files were normalized using R/Bioconductor, Affy package (background correction: RMA; only perfect-match probes; probe intensity normalization: QSPLINE; expression index calculation: Li and Wong method). The resulting dataset with one intensity per ORF per time point per experiment was the final working dataset. For clustering and PCA analysis, all 25 CEL files comprising the three complete time series (eight in N-upshift, eight in rapamycin and nine in N-downshift) were normalized together, allowing the relative comparison of transcripts across all experiments.
cerevisiae: (i) A proteomewide mass-spectrometric map generated from synthetic reference peptides  (Picotti et al, 2013 ) (accessible at www. srmatlas. org/ yeast/) and (ii) a yeast ion-trap consensus spectral library from the National Institute of Standards and Technology (NIST) (built 2009, accessible at http://peptide. nist. gov). Relative protein quantification was carried out based on the ratio between summed transition areas for light (endogenous) and heavy ( 15 N reference) peptide form. For proteins quantified with several peptides the average peptide ratio was used to compute the protein ratio. The quantitative SRM dataset has been deposited to the public repository "Panorama" and is accessible via the link https://daily. panoramaweb. org/labkey/ project/Aebersold/ludwig/BigY_SRMdata/begin.
Analysis of transcriptome and metabolome data Principal component analysis (PCA) PCA was performed with the R-package pcaMethods (method ppca) on the entire transcript set (5,716 transcripts), metabolic gene set [909 transcripts encoding for metabolic enzymes described previously  (Herrgard et al, 2008) ] and LC-MS/MS metabolome set (42 metabolites). Transcript and metabolite intensities were meancentered by subtracting the average intensity of each transcript/ metabolite over the entire samples and scaled to unit variance by dividing by the standard deviation.
Onset times Onset times, defined as the time for a transcript to reach half of its maximum intensity change, were determined by fitting a sixparameter impulse model  (Chechik & Koller, 2009)  to the dynamic transcript profiles. The impulse model fit was implemented in Matlab, and the parameters h0, h1, h2, t1, t2, and beta that best fit the data were identified based on an optimization problem formulated as minimization of unconstrained variables. By interpolation of the dynamic transcript response given by the impulse response, we calculated the onset time t 1/2.
Clustering Clustering was applied to the dataset containing all measured transcripts over the complete time-course datasets for the three shifts (matrix size: 5,716 transcripts × 25 samples). Model selection and statistical validation was performed with the principle of Approximation Set Coding to determine the most predictive clustering and the number of clusters  (Chehreghani et al, 2012) , which determines the best tradeoff between informativeness and stability of the clustering solutions. Here, it has been employed to compare two relational cost models: pairwise clustering  (Hofmann & Buhmann, 1997)  and correlation clustering  (Bansal et al, 2004)  with Pearson correlation similarity. Pairwise clustering yielded a higher approximation capacity than correlation clustering, that is a larger number of bits-per-sample which are reliably extracted from the data.
Finally, we selected the 10% of the transcripts with the highest maximum distance correlation value (across all the transcriptional delays) to define a set of genes associated with a metabolite (significant set of genes of a metabolite; SSG of a metabolite). The "dynamic dependence" feature value of a metabolite used for network assignment was defined as the mean value of the distance correlation values of the SSG of this metabolite. Computation of "representation of TOR genes" For each metabolite, the feature of "representation of TOR genes" is defined as the normalized fraction of genes in SSG that are known to be directly associated with at least one of the transcription factors Gcn4, Rtg1, Rtg3, Gln3, Sfp1, Fhl1, Ifh1, Msn2, Msn4, Gis1, and Sko1 that are known direct targets of TORC1. The direct association was inferred from the YEASTRACT database  (Teixeira et al, 2006)  using the direct and documented interactions (downloaded on 10 Nov 2011).
We assumed uniform priors for each metabolite-network assignment such that the Bayes factor B ij is given by: B ij ¼ PðD j M i Þ PðD j M j Þ ; namely the fraction of the likelihoods of the two motifs. The likelihood of assigning a metabolite to a network motif was computed based on the corresponding "motif prototype" values (see Fig  4B ) and the values of the metabolite-related features compared to the feature distribution of a random set of metabolites (of an initial constant value plus Gaussian noise of same coefficient of variation as in the experimental dataset). Specifically, the likelihood value was defined as the product of the distances of the features' values on the empirical cumulative density function (eCDF) of the random metabolite set from the "motif prototype" values. When a "motif prototype" value is "R", then the eCDF distance (of a feature with value x) from the median of the random set distribution is computed only when eCDF(x) > 0.
Data availability The raw gene expression data comprising all three shift experiments are deposited in the NCBI Gene Expression Omnibus (GEO) under the accession number GSE54852 (http://www. ncbi. nlm. nih. gov/ geo/query/ acc. cgi?acc=GSE54852). Each individual shift experiment can alternatively be retrieved under accession numbers GSE54844 (N-upshift), GSE54850 (N-downshift), and GSE54851 (rapamycin-induced downshift). The protein quantitative SRM dataset is deposited in the public repository Panorama and accessible via the link https://daily. panoramaweb. org/labkey/project/Aebersold/ ludwig/BigY_SRMdata/begin. view. All data were processed as described in Materials and Methods. The Matlab code for the network inference method is provided as Supplementary File S1. Supplementary information for this article is available online: http://msb.
Background: The monomeric composition of polymers is powerful for structure comparison and synthetic biology, among others. Many databases give access to the atomic structure of compounds but the monomeric structure of polymers is often lacking. We have designed a smart algorithm, implemented in the tool Smiles2Monomers (s2m), to infer efficiently and accurately the monomeric structure of a polymer from its chemical structure. Results: Our strategy is divided into two steps: first, monomers are mapped on the atomic structure by an efficient subgraph-isomorphism algorithm ; second, the best tiling is computed so that non-overlapping monomers cover all the structure of the target polymer. The mapping is based on a Markovian index built by a dynamic programming algorithm. The index enables s2m to search quickly all the given monomers on a target polymer.
Conclusion: s2m automatically creates de novo monomeric annotations for polymers, efficiently in terms of time computation and sensitivity. s2m allowed us to detect annotation errors in the tested databases and to easily find the accurate structures. So, s2m could be integrated into the curation process of databases of small compounds to verify the current entries and accelerate the annotation of new polymers. The full method can be downloaded or accessed via a website for peptide-like polymers at http://bioinfo. lifl. fr/norine/smiles2monomers. jsp.
Open Access *Correspondence: yoann. dufresne@ed. univ-lille1. fr 2 Inria Lille Nord Europe, Bonsai team, Parc scientifique de la Haute Borne, 40 avenue Halley, 59650 Villeneuve d' Ascq, France Full list of author information is available at the end of the article in the Smiles2Monomers abbreviated s2m tool (http:// bioinfo. lifl. fr/norine/smiles2monomers. jsp). The s2m tool was developed to help the determination of the monomeric structure of nonribosomal peptides (NRPs) from their chemical structure given in scientific articles. This determination is mandatory for the input of a new NRP in the Norine database  [1]. However, the applications are numerous. First, other databases can rely on s2m to increase the accuracy of their data. For example, the Protein Data Bank (PDB) carried out a remediation process concerning the small molecules  [2]  and peptide-like compounds  [3]  stored in PDB entries.
This process ends up with the remediation of the CCD and the creation of the Biologically Interesting molecule Reference Dictionary (BIRD) which contains the polymeric structures of peptides. A different application is the analysis of compounds by tools dedicated to monomeric structures to, for example, compare polymers  [4]  or predict polymer activities  [5]. These tools complement cheminformatics tools that analyze the atomic structures of polymers. With the same idea, s2m can identify fragments designed by other tools or atomic structures extracted from a database. Indeed, several cheminformatics tools rely on chemical substructures, also called fragments. The presence or absence of a list of fragments in a given molecule can be summarized in an array called fingerprint, reducing the algorithmic complexity needed to compare two molecules or search for a molecule in a database (for a recent review see  [6] ).
Therefore, a monomer can match by chance and take the position of the smaller but accurate one. So, these methods will certainly fail to infer the correct structure of intricate polymers. For example, nonribosomal peptides contain amino acid derivatives or other types of monomers. The chemical bonds between the monomers can be peptide bonds, glycosidic bonds or oxidative cross-linking leading to non-linear monomeric structures. Some monomers are able to form up to five bonds. In these cases the algorithm needs to be smarter by exploring several solutions. Moreover, the O'Donnell et al. and CHUCKLES methods are not distributed in a free program. Otherwise, other tools  [11] [12] [13]  break polymers into fragments that are not identified monomers. For example, these tools infer a set of fragments by extracting frequent common patterns between molecules in given databases.
This strategy, known as deconstruction, generates fragments that can then be linked to reconstruct new drug-like molecules in the fragment-based drug design approach (see  [14]  for review). Finally, our algorithm identifies the monomers occurring in a target polymer based on a database of monomers. This strategy enables to obtain the name of the determined fragments. We design dedicated algorithms and data representation for the two steps of s2m: first the monomers of the database are mapped on the atomic structure, second the best tiling is computed so that non-overlapping monomers cover all the polymer structure. The first step, which is the monomer search, corresponds to the following two common graph theory problems: "Maximum Common Subgraph" (MCS) problem  [15]  and "Subgraph Isomorphism" (SI) problem  [16].
Cheminformatics libraries such as CDK  [25]  and Open-Babel  [26]  use the VF2 algorithm  [27]. This algorithm is still exponential due to the NP-completeness of the problem but is faster than the Ullman one in practice. We designed an efficient strategy to optimize the search, with a fast branch and bound algorithm. We optimized the VF2 algorithm with the strategies presented in Shang publication  [28]  and used by Zhu for the search of molecular graphs into molecular databases  [29]. The novelty of our SI algorithm is the computation of indexes by dynamic programming to construct the best Markovian chain that minimizes the search time. On its first step, our algorithm guarantees finding all the occurrences of any given monomer in a polymer. The second step, which is the monomer tiling, is the main contribution of our method.
The monomers are tiled while they are mapped. So, an atom already assigned to the monomer m1 cannot be assigned to another monomer m2, even if the first assignment is false. In practice, this method works for small monomer databases by performing the isomorphism monomer by monomer in decreasing atomic size order. But, when the number of monomers exceed hundreds and when the database contains similar monomers such as amino acid derivatives, this greedy method will fail. So, we created a method to refine a greedy pre-solution by successive and local branch and cut algorithms. In the second step, our algorithm outputs an adequate solution, but not necessarily the exact one. The main steps of s2m method will be described with a focus on the tiling approach. Then, the results obtained on two manually curated databases are presented and discussed.
Norine: a dataset of nonribosomal peptides to test s2m Norine  [1]  is a database containing nonribosomal peptides (NRPs), represented by their monomeric structure. These peptides display challenging features that complicate the monomeric decomposition: they are composed not only of amino acids (including derivatives and non-proteinogenic ones), but also carbohydrates and lipids ; their monomers are linked not only by peptide bonds but also by oxidative cross-linking and by glycosidic bonds, among others. We automatically extracted chemical structures from PubChem  [31]  for a set of 327 NRPs.
Chemical Component Dictionary: a dataset of small molecules to test s2m The Chemical Component Dictionary (CCD)  [32]  contains all the residues and the small molecule components found in some Protein Data Bank (PDB) entries. PDB  [33]  is the major worldwide resource of 3D structures of biological macro-molecules. The CCD dictionary provides, among other annotations, SMILES representation for each small component present in PDB. Among the components stored in CCD, some polymers have detectable internal substructures, and the list of their sub-components is included in their entry. We extracted from CCD 378 components with documented internal substructures. The two datasets are independent. Indeed no polymer of Norine is also present in CCD. In their respective monomer databases, only 61 are in common (for more than 500 in each monomer dataset) and in most of the cases they are classical compounds like the 20 proteinogenic amino acids.
First prerequisite: monomers to annotate the target polymer The monomeric structure is inferred by mapping given monomers to the atomic structure of a target polymer. The monomer database can be either dedicated to the type of target polymers, if known, or general with all types of monomers. For example, if a peptide is submitted, only the 20 proteinogenic amino acids are needed in the database. s2m tool supplies precomputed databases, but users can also submit their own. The 529 monomers stored in Norine were used on the Norine dataset, while the 506 sub-components parts of the CCD components with documented internal substructures were used on the CCD dataset.
Second prerequisite: chemical reaction rules to generate residues As monomers are truncated during chemical reactions while incorporated in the polymer, s2m needs to delete the lost atoms before searching for them in the polymer. The truncated monomers are called residues. So, another input of the software is a list of chemical reaction rules that are atoms lost by functional groups potentially altered while bonded with other monomers. The corresponding reactions also depend on the type of target polymer. For example, amino acids are bonded by peptide bonds between amino and acid moiety or by disulfide bonds between two thiol groups. We built the chemical reaction rules by studying the synthesis of the polymers stored in the operated databases. So, s2m tool supplies a set of rules describing the most frequent bonds observed in biological polymers.
Methods Given the atomic structure of a polymer and all the candidate monomers for a given type of polymer, our goal is to extract its monomeric structure (see Fig. 1 ). To reach this goal, we split the monomeric structure inference problem into two disjoin steps that are solved sequentially. First, each candidate residue is independently searched on the polymer and then, the mapped residues are tiled against the polymer to find an optimal coverage of its atoms. As the monomer database is an input of s2m, a smart index is built during a preliminary step, to optimize the first step.
Step 0: indexation to minimize the search time To begin, all possible residues are generated from the monomers of the given database. Indeed, the monomers are not totally included in the polymer, but they lose atoms during the chemical reactions leading to the polymer. The truncated monomers are called residues. All residues generated are searched for in the target polymer. As the number of residues can exceed hundreds and as they are generated once before the searches, we designed an efficient strategy to optimize the search. The main idea is to start with parts of the searched molecules that are rare in the target molecules. So, an index is built by ordering the atoms of the searched molecules. A molecule index starts by a chosen atom (label) and extends it with neighbors atoms recursively to construct successive sub-patterns.
To be even more selective, when toolkits like CDK  [25]  look for the first most selective label  [35] , we propose to extend the reasoning to all the labels. We design Markovian chains that construct the most selective succession of sub-patterns. This succession minimizes the time of the isomorphism of step 1. More details about the method to create and search the chains are provided in Additional file 1. Step 1: search of all monomers to cover the polymer As explained before, residues are generated from monomer databases by applying the predefined chemical reaction rules. Most of the monomers can form at least two bonds with other monomers, but all these bonds are not always formed when a monomer is integrated into a polymer. So, all the possible residues must be generated from a given monomer by applying recursively the chemical reaction rules.
Modulation and complementary tilings to optimize the coverage When the polymer is partially covered, the light matching is applied to the regions that remain uncovered and another tiling is performed. The strategy to fill in these regions is based on the possible causes of the partial covering, which could be the following: • Selection of too large monomers that cover regions they should not. So, their correct neighbors cannot map where they should. • Mismatch of the correct monomer because of tautomery or other little differences in the structure representation. • Lacking of the correct monomer in the database. Moreover, uncovered regions are often partially occupied by small monomers mapping by chance. To solve these problems, we start with the tiling obtained by the monomer search and try to refine it.
Results and discussion As mentioned in Materials part, we use two independent sets, the Norine database and the Chemical Component Dictionary (CCD) to validate the s2m tool. We run s2m on polymers of each set, using their respective monomer databases and chemical reaction rules designed by ourselves. The aim was to compare the monomeric structures obtained by s2m with the manually annotated ones. The results can be consulted on: http://bioinfo. lifl. fr/norine/smiles2monomers. jsp.