dataesr/scientific-paragraphs-categorization huggingface dataset:
Receptor and Docking Box In the receptor input part (see Figure  3 ), the user can upload a pdb file or enter a PDB ID. When entering the PDB ID, the user can select one or more chains by entering the PDB ID followed by a mark point and the selected chains, for example, to use chains A and E from 3EAM structure enter: "3EAM. AE". A first filter will extract amino acid and nucleic acid residues to keep only the protein, DNA, and RNA molecules. Once the receptor structure has been prepared, its structure is displayed in a second instance of NGL viewer. Thanks to NGL, the receptor display can be personalized to show or hide the receptor surface and its opacity, the protein/DNA/RNA FIGURE 4 | Overview of the docking result interface. Once docking computation has been complete, the docking pose structures in complex with the receptor will be displayed in 3D in a NGL stage.
Docking Parameters For the sake of simplicity, docking parameters are limited to few options. The main option being the docking software (AutoDock, AutoDock Vina, Smina, or Qvina). For Vina, Smina, and Qvina, the user will have to use a 1. 0 Å spacing, as with AutoDock, the user can specify it (default value: 0. 375 Å). The user can then define the mode number and the energy range in kcal. mol -  ; the mode number defines the maximum number of predicted docking to be generated within the defined energy range. Poses with affinity not within the energy range to the best pose will be discarded, no matter if the maximum number of docking poses is not reached. At last the user has to define the exhaustiveness which corresponds to thoroughness of search and is roughly proportional to time. For Vina, a value of eight is recommended.
CONCLUSION The SeamDock web server intends to provide a free and accessible molecular docking tool, in particular for teaching. SeamDock's ease of use combined with a complete 3D visualization in a collaborative mode makes it a perfect tool for nonspecialists outside of the molecular modeling community. It can be used in a collaborative mode with partners all around the world or in a classroom focused on docking methods or receptor-ligand interactions, while ignoring software installation and Unix command lines. Future developments include an increase in the number of docking engines, a better control of protonation, and a choice for scoring function. In the longer term, integration with experimental constraints used in protein-protein docking protocols such as the HADDOCK web server  (van Zundert et al.
Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e. g. , serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies.
This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.
As with other-omics technologies, pre-processing metabolomics data is critical before relating them to phenotypes, such as cancer endpoints or lifestyle exposures  [11, 12]. After a matrix of p metabolites (or features) measured in n samples has been generated, pre-processing usually involves (i) feature and sample filtering, where low-quality features and samples are excluded, (ii) data imputation, to take care of missing values, and (iii) data normalization, to correct for sources of unwanted variation in metabolomics data, such as batch effects and other factors related to the handling of samples  [11, [13] [14] [15] [16]. Following the success of data acquisition efforts in large-scale epidemiological investigation, collaborative consortia have been put in place, offering the possibility to pool metabolomics data acquired in different studies in order to increase sample size and range of biological variation, and eventually enhance the statistical power of the analysis.
However, pooling metabolomics data across studies raises methodological challenges as several preanalytical and analytical factors can induce differences in metabolite measurements and unwanted variability between datasets. Specifically, sample types (e. g. , serum versus plasma), fasting status of the participant, and any other elements related to sampling conditions, sample treatment, and storage represent preanalytical factors, while analytical factors include information on the organization of samples in batches, the acquisition instrument, the acquisition time (i. e. , time at which the sample was assayed), and the laboratory  [17]. Correcting for these sources of variations is crucial in order to conduct accurate statistical analyses on pooled datasets. Data on common quality controls assayed in all studies and/or reference assay data from a subset of samples in each study can be used for normalization  [17, 18].
However, these data are not always available in large international investigations and consortia. Accordingly, we developed a pipeline for the normalization and pooling of metabolomics data acquired in different studies that does not require data on quality controls or reference assay data, which covers four main steps. First, data cleaning identified and removed features and samples exceeding certain thresholds of missingness and outlying samples  [11, 16]. Second, the remaining missing values were imputed within each study using information on limits of detection and quantification when available and appropriate, and measurements were log-transformed to reduce skewness. Third, the principal component partial R-square (PC-PR2) technique was implemented to identify sources of variation in the metabolomics data  [13].
Last, mixed effect models were used to correct for unwanted variability while preserving biological variability  [14]. The ComBat method  [19]  implemented in the R sva package  [20]  and a PCA-based method  [21, 22]  were also implemented for comparison. Our pipeline was applied to targeted metabolomics data acquired in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC)  [23]. Comprehensive analytical and graphical examinations of measurements were performed to assess whether different normalization approaches improved the comparability of metabolomics data. For illustration, metabolomics data were pooled and related to study participants' BMI.
Identification of Major Sources of Variations As displayed in Figure  1  (left panel), the projection of the measurements on the first two principal components of the PCA were strongly clustered by study, suggesting the presence of systematic sources of heterogeneity across studies. The PC-PR2 method was applied to assess the proportion of the overall variation in the metabolomics data that was explained by a predefined list of variables, including (i) participants' characteristics, i. e. , study center, gender, case-control indicator, age, BMI, alcohol intake, smoking status, and (ii) three variables describing possible preanalytical and analytical sources of unwanted variations: fasting status, time of the day of blood collection, study, and batch, with batch nested within study. As shown in Figure  2  (top panel), the PC-PR2 analysis indicated that these variables together explained more than 55% of the total variation of the metabolomics measurements before normalization.
Impact of Normalization When Relating a Given Phenotype to the Metabolites The relationship between the metabolites and BMI was assessed. The analysis was restricted to control samples to reduce collider bias, and one sample was randomly chosen from among duplicates. For each of the 117 metabolites, Pearson correlation coefficients were computed between BMI and, in turn, the imputed measurements, the normalized measurements, and the normalized measurements produced by a simpler normalization approach, which corrected for study and batch effects without attempting to preserve variation due to study center, BMI, gender, and alcohol intake. As displayed in Figure  6 , most correlation values were above the line y = x, especially for values greater than 0. 1: associations with BMI were stronger when using normalized data implementing our approach compared to those observed with both non-normalized data and normalized data implementing a simple, yet incomplete, normalization approach.
Discussion In this work, a pipeline for the normalization of metabolomics data acquired in different studies was described. After a screening of informative metabolites and samples, the PC-PR2 method was used to identify major sources of variation in metabolomics data and linear mixed effect models were used to correct for unwanted sources of variation, while attempting to preserve biological variation and accounting for potential heteroscedasticity. The pipeline was applied to targeted metabolomics data acquired in eight cancer-specific case-control studies nested within EPIC. Substantial inter-study and interbatch heterogeneity was observed in the original data. Accordingly, the technical reproducibility was low-to-moderate for many metabolites with ICC values lower than 0. 50, especially for the semi-quantified metabolites (e.
Discussion In this work, a pipeline for the normalization of metabolomics data acquired in different studies was described. After a screening of informative metabolites and samples, the PC-PR2 method was used to identify major sources of variation in metabolomics data and linear mixed effect models were used to correct for unwanted sources of variation, while attempting to preserve biological variation and accounting for potential heteroscedasticity. The pipeline was applied to targeted metabolomics data acquired in eight cancer-specific case-control studies nested within EPIC. Substantial inter-study and inter-batch heterogeneity was observed in the original data. Accordingly, the technical reproducibility was low-to-moderate for many metabolites with ICC values lower than 0. 50, especially for the semi-quantified metabolites (e.
In any case, performing sensitivity analyses with normalized data generated including different sets of variables in matrices X and Z is a good practice. In multicenter investigations like EPIC, study center is a sensitive variable as it expresses technical (preanalytical) variation, likely the result of specific procedures for blood collection, sample treatment, and storage, as well as biological variation reflecting specific lifestyle exposures, often characterized by geographical gradients. In addition, in a multicenter context, the relationship between two sets of variables could be evaluated at the overall level, at the center level, or at the individual level  [29]. In this study, to use the whole variability in metabolomics and BMI data, center was initially included in matrix Z. In the sensitivity analysis, study center was included in matrix X and the center-specific variability was removed.
If cases and controls are assayed within the same batch (as was the case in the EPIC metabolomics data), the effects of study and batch on the means of the measurements are not a concern and normalization is not required unless the variances of the measurements also vary across studies or batches. However, if the evaluation focuses on the investigation of lifestyle determinants of metabolomics data, as for example in mediation analysis, the matching is "broken" and control for inter-batch and inter-study variability is required  [7]. Although originally developed for the normalization of metabolomics data acquired in different studies, our pipeline could be used for data acquired in a single study, for example to correct for inter-batch variability while preserving biological variability and to correct for potential heteroscedastic structures of concentration levels across batches.
Our pipeline could also be adapted to the normalization of biomarker data and other molecular data, possibly with some modifications. In particular, for the normalization of untargeted LC-MS metabolomics data, a step to exclude features based on comparison with blank samples should be added to the data cleaning  [16] , and a K-nearest neighbors approach has been shown to perform particularly well for the imputation of missing data  [15, 30]  in the context of untargeted metabolomics data. Importantly, when processing untargeted metabolomics data from individual studies separately, different feature identifiers (e. g. , mass to charge ratio and retention time) would characterize the same molecule in each study. Therefore, the pooling of several untargeted datasets would generally require an additional feature alignment step consisting of identifying the features present in the different datasets, which might be particularly challenging with data acquired in different laboratories  [31].
With the increasing availability of metabolomics data in large scale epidemiological investigations, such as those participating in the COnsortium of METabolomics Studies (COMETS)  [32] , pooling will be more and more relevant as a strategy for increasing the statistical power when investigating the relationship between metabolomics data with disease indicators, environmental exposures, and/or other-omics and biomarker data. Combined with analytical and graphical inspection of the data to determine sources of unwanted variability to be removed and sources of biological variability to be preserved, linear mixed models provide a flexible tool to normalize metabolomics data, and possibly other -omics and biomarker data, prior to pooling data from different studies. As the comparability of measurements across studies is improved, our normalization approach could also be useful for studies that aim at the meta-analysis of individual patient data from different studies, in particular if heteroscedastic patterns of variability were observed.
In Denmark, blood fractions were stored locally in the vapor phase of liquid nitrogen containers (-150 • C), and in Sweden, they were stored locally at -80 • C in standard freezers. Our analyses used targeted metabolomics data collected within the EPIC study and generated through the AbsoluteIDQ p180 or p150 commercial kit (Biocrates Life Science AG, Innsbruck Austria). All participants provided written informed consent to participate in the EPIC study. This study was approved by the ethics committee of the International Agency for Research on Cancer (IARC) and all centers.
The Pipeline to Normalize Data Given a matrix of p metabolites acquired on n samples, our pipeline implemented four main steps, as summarized in Figure  7  and detailed hereafter for the EPIC targeted metabolomics data. R scripts implementing these four steps, along with Rmarkdown and html documents that can be used to reproduce the analysis of the EPIC data, are available at https://code. iarc. fr/viallonv/pipeline_biocrates, accessed on 14 September 2021. All participants provided written informed consent to participate in the EPIC study. This study was approved by the ethics committee of the International Agency for Research on Cancer (IARC) and all centers.
The Pipeline to Normalize Data Given a matrix of p metabolites acquired on n samples, our pipeline implemented four main steps, as summarized in Figure  7  and detailed hereafter for the EPIC targeted metabolomics data. R scripts implementing these four steps, along with Rmarkdown and html documents that can be used to reproduce the analysis of the EPIC data, are available at https://code. iarc. fr/viallonv/pipeline_biocrates, accessed on 14 September 2021.
Step 2: Data Imputation All missing values, including the out of measurable range values, were imputed in the cleaned dataset in each batch separately. Values below batch-specific LOD, below kitspecific LLOQ, or above kit-specific ULOQ were set to LOD/2, LLOQ/2, and ULOQ, respectively. Values below an unknown batch-specific LOD were set to LOD/2 after setting batch-specific LOD to study-specific medians of known LOD values. Fully missing values were set to the batch-specific median of non-missing values if less than 50% of the measurements in the batch were missing and to the study-specific median of the batch-specific medians otherwise. Measurements were log-transformed to reduce skewness.
Step 3: Data Normalization, Part 1: Identification of Sources of Variation The PC-PR2 technique was used to identify main sources of variation in the metabolomics data  [13]. The PC-PR2 is a multivariate technique that combines PCA with multiple linear regression to assess the proportion of the variability of the full metabolomics dataset explained by a set of explanatory variables, including samples characteristics (age, sex, BMI, alcohol consumption, study center), as well as preanalytical and analytical factors.
Step 2: Data Imputation All missing values, including the out of measurable range values, were imputed in the cleaned dataset in each batch separately. Values below batch-specific LOD, below kit-specific LLOQ, or above kit-specific ULOQ were set to LOD/2, LLOQ/2, and ULOQ, respectively. Values below an unknown batch-specific LOD were set to LOD/2 after setting batch-specific LOD to study-specific medians of known LOD values. Fully missing values were set to the batch-specific median of non-missing values if less than 50% of the measurements in the batch were missing and to the study-specific median of the batchspecific medians otherwise. Measurements were log-transformed to reduce skewness. The PC-PR2 technique was used to identify main sources of variation in the metabolomics data  [13]. The PC-PR2 is a multivariate technique that combines PCA with multiple linear regression to assess the proportion of the variability of the full metabolomics dataset explained by a set of explanatory variables, including samples characteristics (age, sex, BMI, alcohol consumption, study center), as well as preanalytical and analytical factors (fasting status, sample processing protocol, blood matrix, study, batch, laboratory instrument).
, W Q , for j = 1, 2. For variables with a nested structure, for example study (S) and batch within study (B), the formula was R 2 partial, k (S) = [SSE(C k |W 2 ,. , W Q ) -SSE(C k |S, W 2 ,. , W q )]/ SSE(C k |W 2 ,. , W Q ), R 2 partial, k (B) = [SSE(C k |S,W 2 ,. , W Q ) -SSE(C k |B, S,W 2 ,. , W Q )]/ SSE(C k |S,W 2 ,. , W Q ). An overall R 2 partial (W 1 ) was obtained by the average of terms R 2 partial,k (W 1 ) weighted by the eigenvalue of each component. This overall estimate provides a measure of the variability in the ensemble of metabolite concentrations that each explanatory variable contributes to explaining. The PC-PR2 technique is implemented in the pcpr2 R package available on GitHub. In order to correct for unwanted sources of variability while preserving biological variability, a random effects model was used for each metabolite separately  [14] , as y = α + Xβ + Zθ + ε, ( 1 ) where y is the n-vector of the measurements for the metabolite/feature under consideration (all studies combined), the matrix X expresses variables corresponding to sources of variations that should be corrected for, and the optional matrix Z expresses variables corresponding to biological variations that should be preserved.
For example, variances of components of ε may vary across studies. This was accounted for by working under heteroscedastic random effect models with a specified structure for the variance matrix Σ of the residuals, e. g. , Σ was made of blocks of the form σ s 2 I ns for observations corresponding to study s (with ns the number of observations in study s). Then, residuals e were replaced by the Pearson residuals in Equation (  2 ), after rescaling them to ensure that their overall variance equals that of the standard residuals. Homoscedastic models were implemented with the lmer function of the lme4 R package, while heteroscedastic models were implemented with the lme function of the nlme R package, using the weights instruction to specify the within-group heteroscedasticity structure. For comparison, we also considered the ComBat method  [19]  of the sva R package  [20] , under which a fixed-effects version of model (  1 ) is estimated using an empirical Bayes approach, to leverage the fact that sources of variation may affect many metabolites in similar ways.
Computation of the Intraclass Correlation Coefficient Using Duplicated Samples The EPIC data included duplicate samples corresponding to aliquots of a baseline blood sample from the same subject measured twice in different batches or in different studies. These duplicated samples were used to assess the technical reproducibility of metabolomics measurements, and in particular to compare technical reproducibility before and after normalization. In sensitivity analyses, ICC values were estimated using only duplicate samples originating from distinct blood matrices (serum and citrate plasma). For each metabolite, we estimated its ICC using a linear mixed effects model of the form  [16] m ik = γ i + ξ ik (3) where m ik is the k-th replicate measurement of subject i, k = 1, 2, γ i ~N(µ, σ γ 2 ) is a subjectspecific random effect (with µ corresponding to the general mean of m ik ), ξ ik ~N(0, σ ξ 2 ) is the residual random error for replicate k of subject i, and Cov(ξ ik , γ i ) = 0.
Motivation: The retrieval of a single gene sequence and context from completely sequenced bacterial and archaeal genomes constitutes an intimidating task for the wet bench biologist. Existing web-based genome browsers are either too complex for routine use or only provide a subset of the available prokaryotic genomes. Results: We have developed BAGET 2. 0 (Bacterial and Archaeal Gene Exploration Tool), an updated web service granting access in just three mouse clicks to the sequence and synteny of any gene from completely sequenced bacteria and archaea. User-provided annotated genomes can be processed as well. BAGET 2. 0 relies on a local database updated on a daily basis.
Materials and methods BAGET 2. 0 has been completely rewritten in C# 8. 0 and. NET Core 3. 1 to comply with the industry-standard Model-View-Controller architectural framework and the latest HTML 5. 0 specifications. The use of JQuery (jquery. com) and D3 (d3js. org) JavaScript libraries enables asynchronous data transfer and access to the HTML 5. 0 graphical canvas, respectively. The BAGET 2. 0 database is hosted locally and shared with the FITBAR and SYNTTAX web services  (Oberto, 2010 (Oberto, , 2013)). Incremental database updates occur daily by retrieving new GBFF genomes from the NCBI (ftp. ncbi. nlm. nih. gov) (Sayers et al. , 2020). Multiple replicons present in some genomes are accessible separately in BAGET 2. 0 and identified by the organism name tag followed by the C1, C2. Cn suffices, by decreasing size.