dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of species composition The COI marker was successfully Sanger sequenced for 270 and 102 larvae from the Marañon and Napo rivers, respectively. A total of 168 sequences corresponded to siluriform species. A total of 158 sequences were very close or identical to the 86 species of our database. Ten sequences presented some divergence from these 86 species. These ten sequences were redundant, and were narrowed down to three different sequences. These three sequences were added to our dataset, as we thought they might either represent three described species not yet sequenced or three new species. They were named Pimelodidae_Pimelodus_spA-C34, Pimelodidae_Hypophthalmus_sp. C95 and Pimelodidae_Hypophthalmus_sp. A289. The final database thus contained 89 sequences (Fig  1 ). Based on the revised reference dataset, the Sanger method identified 11 different species in the Marañon River sample (Mar-S) and 10 different species in the Napo River sample (Nap-S) (Table  1 ).
Species One limit worth mentioning is the need for a reference database to map our NGS sequences. Here we focused on commercially important and hence widely distributed species. Our reference contained 86 species of Siluriformes, which does not represent the whole species diversity of Siluriformes, but we only added three putative species to our database out of 564 Sanger sequenced larvae. In a few cases, we were only able to identify a larva to the genus level. As new species are sequenced, the database will be enriched and the precision of identifications improved. Using our database, we were able to assess the temporal and spatial dynamics of species reproduction in the Ucayalı ´River. The results showed that the breeding activity of the Pimelodidae family was spread out over the eight months of the study.
This method was repeated 4 times and any peptides/transitions, which were not detected in all 4 repeats removed. A total of 9 proteins and 10 peptides have been analysed over a 35 minute gradient. Then, 20 combined (TMTLight and TMTHeavy) liver samples were analysed on the TSQ Vantage in triplicate, leading to the production of 60 raw  data files. The average CV value between technical repeats was 13. 8%. Of the 10 peptides we aimed to quantify, 5 peptides were successfully detected across all 60 replicates. Several light and heavy transitions co-eluting at the same retention time were clearly identified, as illustrated for the peptide 'ILTFDQLALESPK' of the 60S ribosomal protein L18 (Fig. 4 ). All of 5 peptides showed a corresponding FC between the two methods (Table  1 ). The SRM data therefore corroborate protein alterations detected in the initial discovery analysis.
In order to obtain insight into the biological significance of the alterations in the proteome profile observed between the control and Roundup treatments groups, we next conducted an enrichment analysis integrating annotations from KEGG and Gene Ontology (GO) databases using DAVID 6. 8 (Supplementary Table  5 ). The most enriched biological process was the response to drug (GO:0042493, FE = 6. 0, BH corrected p = 1. 9E -8 ), clearly indicating that the liver proteome of the treated animals are reflective of the treatment with a chemical agent (Table  2 ). Interestingly, the response to organonitrogen compounds (FE = 9. 9, BH corrected p = 4. 0E -2 ) was particularly enriched. Since glyphosate is an organonitrogen, this first set of proteins (BHMT, CYP2C7, HMGCS1, CYP2E1, MGST1) could constitute a signature of glyphosate metabolic effects.
This is consistent with the glyphosate metabolic process (GO:0018920), which is defined as a type organonitrogen compound metabolic process (GO:1901564) in the GO database. A majority of the enriched terms are related to a modification of redox status (GO:0055114, FE = 4. 9, BH corrected p = 2. 1E  -7  ). Overall, the proteome is reflective of an oxidative stress response (GO:0006979, FE = 8. 5, BH corrected p = 7. 6E -4 ). The alteration of glutathione metabolism (GO:0006749, FE = 21. 1, BH corrected p = 1. 1E -5  ) is indicative that the liver of the Roundup treated animals are likely to have suffered from an increase in oxidative stress. A major significant cluster of annotation was related to fatty acid degradation (rno00071, FE = 11. 5, BH corrected p = 9. 1E -5 ). Of particular note is the modulation of triglyceride metabolism (GO:0006641, FE = 21.
A volcano plot of statistical differences in metabolite levels is presented in Fig. 2B. Using the same statistical analytical methods we employed to analyse the proteomics dataset with a stringent statistical cut-off threshold (Benjamini Hochberg adjusted p ≤ 0. 05), only 3 compounds (namely, N-methyl proline, N-acetyl-beta-alanine, nicotinamide riboside) were found to be significantly altered in the Roundup treated verses the control group (Supplementary Table  7 ). However, the 55 metabolites having a p-value < 0. 05 were nevertheless considered for biological interpretation. Although the level of statistical significance of these 55 metabolites does not survive the multiple comparison tests, their biological interpretation provides a coherent explanation of the treatment effect due to the fact that these statistically significant differences were not randomly distributed among metabolic pathways but concentrated in those involved in the response to oxidative stress and lipotoxic conditions.
Both enriched (8 μ l) and un-enriched (5 μ l) fractions were loaded onto a 50 cm EASY-spray column (Thermo Scientific) and quantitative analysis was performed using the Orbitrap Velos-Pro mass spectrometer (Thermo Scientific) in positive ion mode. The peptides and phosphopeptides were separated by gradient elution, from 5-80% 0. 1% trifluoroacetic acid in acetonitrile (5-40% from 0-100 minutes, 40-80% from 100-110 minutes), at a flow rate of 300 nl/min. Mass spectra (m/z) ranging from 400-1600 Daltons was acquired at a resolution of 60,000 and the 10 most intense ions were subjected to MS/MS by HCD fragmentation with 35% collision energy. Protein identification was performed with Proteome Discoverer 1. 4 (Thermo Fisher Scientific Inc. ). Raw files were imported and searched against the UniProtKB/Swiss-Prot Database using Sequest for Proteome Discoverer.
Exported raw data for the two TMT10plex sets is available as Supplementary Tables  1  and 2. Raw files for both enriched and un-enriched fractions were merged together in a single file search for each of the two TMT10plex sets. Precursor mass tolerance for the searches was set at 20ppm and fragment mass tolerance at 0. 8ppm. The taxonomy selected was Rattus norvegicus and three enzymatic miscleavages were allowed. Dynamic modifications selected on the search were Oxidation/+ 15. 995 Da (M), Phospho/+ 79. 966 Da (S, T, Y) and Deamidated/+ 0. 984 (N, Q) and static modifications were Carbamidomethyl/+ 57. 021 Da (C), TMT10plex/229. 163 Da (K), TMT10plex/229. 163 Da (Any N-terminus). Proteome verification using Tandem Mass Tag -selected reaction monitoring experiment. Samples were prepared in the same way as the discovery experiment up to the TMT labelling stage.
Peptides were separated over 35 minutes, using gradient elution 5-80% 0. 1% trifluoroacetic acid in acetonitrile (0-5% from 0-3 minutes, 5-50% from 3-7 minutes, 50-65% from 7-30 minutes and 65-80% from 30-35 minutes) at a flow rate of 300 nl/min. Data was analysed using Skyline software  46  , with all peak matching also being visually verified. Peak area ratios between Light and Heavy transitions were generated for each sample and exported into Excel. When importing raw SRM data files from the TSQ vantage into Skyline software, a unique peak picking algorithm was used to confidently and accurately assign the best transition peaks for each peptide. Peptides were only taken forward for analysis if all assigned peaks were within a similar retention time period across all 60 samples. From the assigned peaks Skyline software was then used to produce a 'total ratio' between the Light and Heavy transitions for all of the 60 raw data files (3 × 10 control sample injection and 3 × 10 treated sample injections).
Bioinformatics analysis. For the proteome, only peptides with TMT reporter ion signal intensities for all ten samples were used for further bioinformatics analysis. Any duplicate peptides were removed before the data was SumScale normalised. The two normalised data files were then merged together to give one 10vs10 file comparison. Any peptides which did not have intensity values in all twenty TMT reporter ion channels were filtered out and median values were taken of the control and treated samples respectively. A total of 1906 peptides were quantified across all liver samples. For the metabolome, raw data was extracted, peak-identified and QC processed using Metabolon's hardware and software. Raw data is available as Supplementary Table  6. Metabolites were identified by automated comparison of the ion features in the experimental samples against a reference library of more than 3000 purified standard compounds that included retention time/index (RI), mass to charge ratio (m/z), and chromatographic data (including MS/MS spectral data), and then curated by visual inspection for quality control using software developed at Metabolon 48.
Peaks were quantified using area-under-the-curve. A total of 673 metabolites were detected. The maximum percent missing data allowed was 20%. As a result, 602 metabolites were taken forward for bioanalytical analysis. The language and statistical environment R  49  together with the ade4 package  50  method was employed in order to explore the relationship between the control and the treated samples. We regressed out the batch effects between TMT1 and TMT2 from the protein expression data, and to correct variation resulting from instrument inter-day tuning differences for the metabolome data, using the limma package removeBatchEffect  51. For plotting of results, a Principal Component Analysis (PCA) was first performed. Missing data was imputed by the value of the median of the group for a given metabolite in order to perform the PCA analysis.
A previous evaluation of different fold change (FC) rules have found that a 1. 2-fold change could be regarded as indicative of a significantly varying protein in TMT-LC-MS/MS experiments  52. Data used for the functional analysis were selected at the cut off values of q < 0. 05 (p < 0. 005) with FC > 1. 2. Pairwise non-parametric Mann-Whitney U tests were performed and a p-value was attributed to each of the 1906 peptides and the 602 metabolites. The resulting p-values were adjusted by the Benjamini-Hochberg multi-test adjustment method for a high number of comparisons. Volcano plots were also constructed in order to visualize the differences in metabolite and protein expression for each of the comparisons. The aforementioned tests and plots were performed using in-house R scripts. The pathway analysis was done using the Thomson Reuters MetaCore Analytical Suite and/or the NIH Database for Annotation, Visualization and Integrated Discovery Bioinformatics Resources 6.
We use the interaction models thus formed to perform additional tests on the interface properties, namely conventional structural features, affinity prediction and contact conservation. Here, we perform the structural comparison in order to evaluate the extent of the similarity between interacting and non-interacting protein pairs and to assess the weight of the structural information in the prediction task. We do not imply that the existence of a structural precedent indicates an interaction. It only indicates a global structural compatibility between proteins. To evaluate the current coverage of the protein-protein interaction network by the set of experimental dimers, we did the same comparison using datasets of reported interactions. Note that, in predictive studies, Figure  1. Structural comparison protocol used in this study.
Each pair of proteins with available 3D models is submitted to structural comparison with binary complexes from the PDB. When both 3D models are similar to constituents of a PDB complex, the PDB complex is termed the structural precedent of interaction. The model derived from the structural matching is termed the interaction model. it is customary to remove homologous proteins from the reference set in order to test the performance of the method  31. Here, our goal is not to predict the structure of the complexes or the interaction between proteins, but rather to estimate the coverage of the experimental data. It is thus important to keep every protein; otherwise we would underestimate the real coverage of the reference dataset.
Selection of Datasets of Non-Interacting and Interacting Proteins. There is no consensus yet on a gold standard for non-interacting datasets in the literature, but rather several alternatives based on different selection strategies  [32] [33] [34] [35] [36] [37] [38]. Here, we consider five different non-interacting datasets: 1. A random sampling among unobserved interactions  32  , 2. A balanced sampling among unobserved interactions, which takes into account the number of connections of the proteins in the native network  32  , 3. Proteins with different subcellular localization, 4. Pairs with the most dissimilar functional annotations  35  , 5. Pairs selected after two-hybrid data post-processing, in order to reduce the false negative rate  33. For the interacting proteins, we considered four different datasets: one set of high confidence physical interactions (positive BRS) from BioGrid  39  , one set restricted to direct interactions (positive BRS-direct), another set of direct interactions (positive KUPS) from KUPS  35  , and the Ito core data set  40  (positive Ito).
Coverage of Structural Information. For each pair of proteins from our different interaction data sets, 3D models of monomers are used as input to identify structural precedents in the reference set of experimental dimers. The identification is thus contingent on the availability of 3D models. The fraction of proteins with available 3D models is similar across all the data sets: around 76% for single proteins and 60% for protein pairs (see Table  1 ). Two datasets display a lower pair rate, around 53%: the "positive KUPS", and the "negative GO" datasets. Note that they were retrieved from the same resource  35.
6 threshold. Interestingly, the rate of structural precedents is more variable across the datasets of interacting pairs than across the non-interacting pairs. A possible factor is the disparity of experimental techniques. The "positive BRS direct" and the "positive KUPS" datasets both contain only direct interactions and they have consistent rates of structural precedents (23 and 21%). By contrast, the "positive BRS" dataset, which contains direct and indirect interactions, has a lower rate of 17% of structural precedents. However, this factor does not explain why the "positive Ito" dataset, obtained by large-scale yeast-two hybrid, has a lower rate of 15%. This illustrates the recurrent problem of the interaction data quality  44. Notably, the strategy used to select the non-interacting pairs has very little impact on the rate of structural precedents (see Table  S1 ).
Extrapolation of the Rate of Structural Precedents of Non-Interacting Pairs. We now focus on the numbers obtained at the 0. 6 threshold and what they mean. One could object that the low rates of structural precedents in the non-interacting datasets are merely noise due to false negatives (actual interactions not reported as such). But let us recall that the ratio between effective and potential interactions in yeast is about 0. 2% (37,600 physical interactions, among 6000 proteins  4  ). Here, we observe 15 to 23% of structural precedents for interacting pairs. If all our non-interacting datasets were contaminated by false negatives, in a purely random manner, we would then expect [15-23]%*0. 2% = [0. 03-0. 05]% of structural precedents. Instead of 0. 03-0. 05%, we obtained 2-3%, i. e. 40 to 100 times more than expected by chance (Fisher test p-value = 3.
7e -3, see Table  S2 ). This can mean that (1) either non-interacting protein pairs also have structural precedents or (2) all the non-interacting datasets are richer in real interactions than expected by chance (by a factor 40 to 100 times). This is extremely unlikely, since we considered five non-interacting datasets, generated by different and orthogonal approaches. The rates of structural precedents are high if we consider the total pool of potential interactions (99. 8% non-interacting, 0. 2% interacting), and even more if we take into account the incompleteness of the PDB. Indeed, using the rate of structural precedents of interacting pairs as a proxy of the PDB completeness (23% in the most optimistic scenario) and the lowest observed rate of structural precedents (2% at least), we obtain 2/23*100 = 8.
3A. The bottleneck edges are the top 10% with the highest betweenness. Because bottleneck edges are key connections in the network, they are critical in terms of the perturbation of the network topology. Is has been shown, in physiological context, that bottlenecks are linked to gene essentiality  45. As a physiological network, we considered the network formed by the 5621 interactions from the "positive BRS" dataset. Protein-protein interaction networks typically contain permanent but also transient interactions that are difficult to detect. In our case, the 'positive BRS' dataset gathers interactions detected by several methods. Notably, one third of the 5621 interactions are detected by yeast-two hybrid, which is well-suited to detect transient interactions  44. Unconnected components were removed, leaving a subnetwork formed by 5374 interactions between 1934 proteins.
We introduced extra interactions from the "negative balanced" dataset, restricted to the 5128 interactions between the proteins present in the native network. The enrichment in bottleneck edges is expressed by the log-odd score of the observed versus expected number of bottlenecks in each category. As shown in Fig. 3B , we observed a significant enrichment of bottlenecks in non-interacting pairs with structural precedents at high TMmin: log-odd = 0. 9 at TMmin > 0. 6 (18% of bottlenecks). This enrichment persists at medium TMmin: log-odd = 0. 6 at TMmin [0. 5-0. 6] (16% bottlenecks), log-odd = 0. 4 at TMmin [0. 4-0. 5] (12. 5% bottlenecks). Such enrichment is not observed in the native unperturbed network (see Figure  S1 ). Our results thus show that non-interacting pairs with structural precedents correspond to key extra connections in the network.
7e -2). These pairs are thus enriched in signaling proteins. Analysis of Protein Abundance. We analyzed the abundance of the proteins involved in non-interacting and interacting pairs, on the dataset used for the network analysis. We found that negative pairs are slightly but significantly depleted in highly abundant proteins compared to positive pairs (see Figure  S2 ). However, negative pairs with structural precedents (TMmin > 0. 6) are not significantly different from other negative pairs. We thus see no evidence of a particular down-regulation of non-functional interactions acting on negative pairs with structural precedents, via low protein abundance. Let us note that the control of protein abundance is one mechanism of control among several others. The global regulation results from the juxtaposition of these different mechanisms.
Positive and Negative Models Are Not Separable Using Simple Structural Descriptors. Having shown that a considerable fraction of non-interacting protein pairs are similar to interacting ones, and are potentially highly deleterious, we now analyze the features of the hypothetical interaction models to which they correspond. More precisely, we compare the interaction models, obtained by superimposition of the 3D models onto the PDB dimers, for the interacting pairs-termed positive models-and non-interacting pairs-termed negative models. In order to work with models of comparable confidence, we selected interaction models with TMmin in the range [0. 6-0. 8]: 113 models of interacting pairs from the "BRS direct" and "KUPS" datasets, and 118 models of non-interacting pairs from the union of all negative datasets (see Figure  S3 ).
Estimation of the Fraction of Potential Physical Interactors among the Negative Pairs with Structural Precedent. Our hypothesis is that the lack of discrimination between interacting proteins and non-interacting proteins with support is due, at least in part, to the absence of pressure against misinteraction avoidance on the proteins, due to physiological regulation. If this holds true, then some of the non-interacting proteins with structural precedence should be able to interact in vitro, as suggested by our affinity prediction results. In support to this hypothesis, we here provide an estimate of the fraction of negative pairs with structural precedent that are potential in vitro interactors. To do so, we used two different approaches. One is based on the prediction of interactions by Struct2Net  15, 16  and the other one is based on the presence of interologues in the public interaction databases.
For interacting pairs, the fraction predicted by Struct2Net is 44%, indicating that the corrected fraction of non-interacting proteins with favorable interfacial energy would be around 9%. This result allows us to provide a first estimate of 9% of the negative pairs with compatible structures that could interact in vitro. In addition, we screened the interaction databases to identify interologues, i. e. , homologue proteins with reported interactions. Results are shown in Table  2. Using the protocol described in Material and Methods, we found that 36% of non-interacting pairs with structural precedents have interologues, versus 18% only for non-interacting pairs without support. For interacting pairs, the ratio is 70%, yielding a corrected ratio of about 50% for negative pairs with support.
Material and Methods Interaction Datasets. All the data used in this study are for the model organism Saccharomyces cerevisiae. The different datasets of interacting and non-interacting protein pairs are presented in Table  1  and described hereafter. When necessary, the mapping between Uniprot and gene ids was done using the Uniprot REST interface http://www. uniprot. org/uniprot. Three data sets from the Yu et al. study  32  were retrieved from http://fbs3pcu112. leeds. ac. uk/BRS-nonint/ PPI_RandomBalance. html: • Positive BRS: high confidence physical interactions, obtained after filtering out the "HC-BIOGRID-2. 0. 31. tab" dataset to remove proteins without PFAM domains. The experimental methods supporting each interaction were retrieved from BIOGRID. • Negative random: random sampling of non-interacting pairs within the "positive BRS" list.
• Negative balanced: sampling of non-interacting pairs within the "positive BRS" list, while preserving the number of connections of each gene. We derived two additional datasets: • Positive BRS-direct: by filtering the "Positive BRS" list using BioGrid data  39  to select only interactions described by one of the following terms: "Two-hybrid", "PCA", "Biochemical Activity", "Co-crystal Structure", "Reconstituted Complex", "Far Western", "FRET", • Negative non-colocalized: by filtering the "Negative random" list using Uniprot annotations  50  to select pairs of proteins with different subcellular localization. Proteins with several localization were removed. Two datasets were retrieved from the KUPS database  35  (http://www. ittc. ku. edu/chenlab/kups/), using the following input parameters: size of the data set = 1500, species = Saccharomyces cerevisiae, for the positive set:    b  The search was done on a random subset of 125 pairs.