dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and sampling for multi-omics studies Study design. From a data analysis point of view, multiomics studies should rely on paired samples, i. e. , on samples where all omics layers per replicate are generated from one individual. Non-paired samples require any method that operates on correlations or related measures to compute these not over the individual samples but over aggregates like mean expression or fold-changes per treatment group. This aggregation generates shorter vectors compared to the data on the individual replicates and approaches like correlation network inference or many dimensionality reduction methods cannot be feasibly applied. Paired samples correspond to what Cavill and colleagues call the split-sample study, where a tissue sample is split for different omics layers and the source-matched study, where different omics layers are generated from different tissues or cells originating from the same individual.
Finally,  Tarazona et al. (2018)  categorized integration methods based on the incorporation of additional biological information and on using a supervised or an unsupervised approach. The first aims at predicting a certain response variable using the omics features as predictors, or at modeling a regulatory network of the underlying molecular system. The latter is mainly used to retrieve an exploratory overview of the data and to unravel potential relationships between omics layers. An overview of different integration principles and how different classification approaches relate to each other is given in Supplementary Figure  S1. Pathway integration. Few integration tools are available that incorporate pathway knowledge to interpret highthroughput datasets, e. g. , via term over-representation or gene set enrichment analysis like PaintOmics 3  (Hernández-de  Diego et al.
2018)  or IMPaLA 4   (Kamburov et al. 2011). Approaches like SPIA, Enrichnet, or SAFE integrate pathway topologies, they do, however, not provide an explicit support for multi-omics data  (Tarca et al. 2009; Glaab et al. 2012; Baryshnikova 2016). PARADIGM is a computational approach that integrates multiple genomic alterations such as copy number, DNA methylation, somatic mutations, mRNA expression and microRNA expression with the REACTOME database  (Vaske et al. 2010). While this is a valuable selection of omics layers for oncological questions, it is of limited applicability in toxicology. Choice of the integration approach depends on study design. As discussed above, most available multi-omics data sets are based on non-paired samples. These designs also lack an inherent, consistent ordering of samples between the omics layers, which prohibits the application of statistical integration methods.
2016) , which is so far only available for Mathematica. Flexibility of integration methods. Many integration methods have been developed for a very specific purpose and can only operate on few types of omics layers. Tools like MCD or Conexic focus on integrating data on genetic variation with gene expression or DNA-methylation data  (Chari et al. 2010; Akavia et al. 2010 ). However, correlation-and concatenationbased approaches, or approaches for dimensionality reduction and clustering are flexible regarding the type of input layers; these include moCluster  (Meng et al. 2016) , mixOmics (Lê  Cao et al. 2009; González et al. 2012) , MCIA  (Meng et al. 2014)  and MOFA  (Argelaguet et al. 2018). Publicly available web-based tools such as PaintOmics, IMPaLA, or OmicsNet 6 (Zhou and Xia 2018) 1 3 integrate biological knowledge from pathway databases.
However, if only one or two pathway databases are used this can be limiting for the analysis (see Case Studies in Section "Case studies" for an example). Another issue that is more prominent with web-based tools is the support of different gene or protein identifiers and the mapping to the used biological knowledge base. Reproducible computational research. Integrative analysis starting from raw multi-omics data is a complex, multistep process. Bioinformatic tools need to be selected from a range of options and each software comes with a set of adjustable parameters which may impact the outcome substantially. Recently, a "reproducibility crisis" has been diagnosed  (Baker 2016) , criticizing the degree of reproducibility in biomedical research  (Bustin 2014). Considering the complexity of multi-omics data analysis, independent replication of the results specifically depends on detailed reporting of the details of the analysis.
Linking executable analysis code with intermediate and final resulting data is promoted as the gold standard of reproducibility by  Peng (2011) , while Sandve and colleagues suggest to follow the "ten simple rules for reproducible computational research"  (Sandve et al. 2013). Workflow management systems, like Galaxy, KNIME, or uap promote the use of reproducible research principles in bioinformatic analyses  (Goecks et al. 2010; Berthold et al. 2007; Di Tommaso et al. 2017; Kämpf et al. 2019). None of the currently available integration methods specifically supports reproducible research principles. However, many standalone tools could easily be wrapped for use in one of the workflow management systems. Web-based tools are particularly prone to reproducibility problems since the user is usually not able to keep track of versions of tools and databases and parameters used.
Pathway analysis Based on the overview of current pathway-based approaches in Supplementary Text and Figures Section S3. 1, we selected two tools for further evaluation. IMPaLA employs several well-known pathway databases, such as KEGG, Reactome, or WikiPathways combining more than 5000 pathway definitions. However, it is restricted to either transcriptome or proteome analysis versus the metabolome layer  (Kamburov et al. 2011 ) meaning that IMPaLA always integrates two omics layer. Additionally, it is restricted to human pathways only. PaintOmics allows to include several different omics layers into its pathway enrichment analysis, provides feature mapping to KEGG IDs, but solely relies on KEGG pathways (Hernández-de Diego et al. 2018). PaintOmics supports the interpretation of time series experiments and uses fold change interpolations (i.
3 When applying IMPaLA to this data set, we achieved similar results. The number of significantly enriched pathways was always highest when all omics layers were combined. In case of Doxycycline and MB, even more pathways were found to be significantly enriched than by simply adding both two-layer approaches (transcriptome/metabolome + proteome/metabolome). Again, this gain in the amount of detected pathways was accompanied with an increase in confidence, i. e. , lower adjusted p values (Supplementary Section S3. 1, Supplementary Figures  S7, S8 ). Murine hepatocyte data. For transcriptomics and proteomics, approximately 1% of features could not be mapped to KEGG-identifiers using PaintOmics. For metabolomics, the 163 metabolites of the Biocrates AbsoluteIDQ™p150 kit resulted in 26 unique KEGG compounds, including 15 amino acids and mainly carnitines.
This restricted number of KEGG-matched metabolites hampered the ability of PaintOmics to identify enriched pathways supported by all three omics layers. PaintOmics interpolates all given samples of one time series into one 'fold change curve', independent of other covariates, e. g. , different concentrations. Hence, we had to compute on both concentrations separately, which weakened the power of the analysis. For high-dose B[a]P exposure, PaintOmics found 6 significantly out of 240 total enriched pathways at the transcriptome level, while only two significant pathways were found for the multi-omics approach [FDR < 0. 05, Supplementary Figure  S4(a) ]. This discrepancy to the findings of the mitochondrial data can be explained by the low coverage of the proteome and metabolome layer of this comparably old data set.
G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G Actinonin (  39 ) Doxycycline (  63 ) FCCP (  56  PaintOmics has decent visualization capabilities. Figure  4  displays the B[a]P metabolism pathway, which is part of the KEGG pathway on xenobiotics metabolism by cytochrome P450. Within this subnetwork of 20 total nodes, 17 and 8 nodes were covered by transcriptomic and proteomic data, respectively. Transcripts showed a clear upregulation starting between 4 and 12 h and continuing up to 24 h of exposure, which was not reflected at the proteome level. The two main known response pathways of B[a]P exposure, the AHR and NRF2 pathways, were not detected by PaintOmics. The simple reason is that these pathways are not represented in the KEGG database. This clearly illustrates the need for a pathway-based analysis to utilize a large variety of high-quality pathway databases to increase the coverage of molecular response pathways or to include custom-defined pathways.
As IMPaLA is limited to human pathways, we mapped the murine transcript and protein IDs accordingly. When IMPaLA was applied to transcriptome and metabolome data, 3841 pathways were found to be enriched, 17 significantly (q value < 0. 05 ). Alternatively, we manually combined tran- scriptome and proteome data prior to the IMPaLA-integration with the metabolome layer and yielded 3956 enriched and 16 significant pathways. We did not observe a consistent trend between both approaches. In some cases, the q values changed drastically. The q value for the NRF2 pathway, for example, dropped by nearly an order of magnitude in the latter approach compared to the two-layer one. See Supplemental Text and Figures, Section S3. 1, for details on the IMPaLA-based analysis. In summary, neither PaintOmics nor IMPaLA completely satisfy the needs for a pathway-based integration of toxicological multi-omics data.
While IMPaLA provides a better integration of pathway databases, PaintOmics exclusively utilizes time-series data and supports more than two omics layers. Both lack support for species-specific or custom-defined pathways and the option to export charts in easily editable formats, like svg or pdf. Our results illustrate that the combination of comprehensive information on multiple molecular levels increases the explanatory power (mitochondrial response data). In contrast, combining omics layers with strongly varying degree of coverage in the different layers, as observed for the older murine hepatocyte data set, is not only not beneficial but also detrimental compared to single-omics analysis of the layer with the highest information content.
Multi-omics factor analysis -MOFA As a member of the group of simultaneous, statistical integration tools we evaluated MOFA (multi-omics factor analysis), which is a framework for unsupervised integration of multi-omics data sets to discover the principal sources of variation  (Argelaguet et al. 2018). The method implements a generalization of principle components analysis aiming at inferring a low-dimensional representation of the data using latent factors. A latent factor is analogous to a principle component but combines data over different omics layers, i. e. separate data matrices. Due to the non-paired nature of the datasets used, we had to aggregate these over treatment groups. The resulting low number of conditions may limit the results of the MOFA-analysis. Figure  5  illustrates the explained variance per latent factor (LF) across all omics layers after training the model.
Joint gene set enrichment analysis As an alternative to the omics-layer-specific gene set enrichment in MOFA we evaluated the results of a GSEA in a union of transcriptome and proteome data versus the individual omics layers relying on the 12 h and 24 h time points of exposure to the high B[a]P concentration. Overall, we observed lower adjusted p values when combining transcriptome and proteome information compared to the single-omics approaches (Supplementary Figure  S12 ). Venn diagrams of the overlap between the enriched pathways indicate that a combination of omics layers mainly lead to a gain of additionally enriched gene sets, while only few were found only in single-omics experiments at 24 h, which was less pronounced at 12 h (Fig. 6 ). Apart from the increase in detected gene sets, the adjusted p values of sets found to be significantly enriched with a single transcriptomic and a combined approach were similar (12 h) or stronger (24 h) when omics layers were combined.
These findings suggest that the advantages of more identified gene sets with stronger p values, outweigh the disadvantages, of a small number of gene sets not found in the multi-omics approach and that an increase of noise that might have been provoked by the combination of multiple omics layer seems a minor issue. In summary, several approaches seem applicable for multi-omics data integration and pathway detection in a toxicological setting. However, all available methods have inherent disadvantages that need to be addressed to use them for risk assessment purposes. Furthermore, only a combination of different tools is currently able to draw a comprehensive picture of the triggered molecular responses in the cell.
Conclusions Here, we reviewed the state-of-the-art options for multiomics data acquisition and data analysis and the prospects and challenges for employing multi-omics data integration in toxicology. In part, we base our conclusions on a retrospective analysis of available multi-omics data for toxicological research questions. Toxicologic multi-omics data sets beyond two omics layers are rare. Multi-omics data are distributed over several repositories and typically there are no links between corresponding data sets. Therefore, we implemented the publicly available web-tool MOD-Finder to facilitate the search for multi-omics data for a compound of interest  Canzler et al. (2019). Using MOD-Finder and manual curation we compiled a table of multi-omics data sets for toxicological research questions (Supplementary Table  S1 ).
While each of the evaluated tools had its strengths and weaknesses, none fully addressed a set of basic requirements, which we consider fundamental for multi-omics pathway analysis in toxicology: (i) The capability to deal with more than two omics layers, e. g. , the four layers that we have selected for the second phase of XomeTox in Section "Multi-omics in toxicology" or ideally with an arbitrary number of omics layers. (ii) Interacting with a flexible set of pathway databases and permitting the definition of custom pathways. (iii) Providing at least rudimentary support (e. g. , like PaintOmics) for time series data. (iv) Allowing to use concentration and time resolved data. (v) Providing capabilities to visualize enriched pathways and provide this visualization in a vector graphics format.
Metabolomics should be acquired using a broad targeted approach that provides metabolites, which also can be mapped to pathways in standard databases, or ideally additionally including a nontargeted strategy. Epigenomics may provide important information for studies aiming at trans-generational or long-term effects. However, detecting a pathway response from epigenomic data is still limited. Acquired data should be deposited in the available repositories. Meta data should follow the guidelines proposed by consortia like MAQC  (Shi et al. 2017). To enable linking of the omics data, links to the other layers should be included in the meta data. Based on the experience made with MOD-Finder, we call for encoding a unique identifier for the substance used and the concentration and time point in the meta data.
SLR A SLR was performed to address the experts' questions regarding the management of patients with mCRPC who have progressed after on ADT plus DOC or ADT plus ABI. For this purpose, a protocol was defined with the coordinators and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was followed. 9 arch strategy Studies were identified by sensitive search strategies in the main bibliographic databases (supplemental data). An expert librarian collaborated with the expert panel and checked the search strategies. The following bibliographic databases were screened: Medline and Embase from 1961 to 11 January 2018, and Cochrane Central register of Controlled Trials (CENTRAL) up to 11 January 2018. We used specific Medical Subject Headings (MeSH) and additional keywords to identify studies.
Screening of studies, data collection, quality evaluation, and data analysis These processes were performed by two reviewers. Both reviewers independently screened the titles and abstracts of the retrieved articles for selection criteria. When the reviewers encountered any discrepancy, a consensus was reached by including a third reviewer (LC). After the articles originating from the selection process had been read in detail following the same principles, a list of studies to be included was established. Afterwards, the reviewers collected the articles data independently. As in previous phases, in the event of discrepancies, a consensus was reached by looking at the original article or by including a third reviewer (LC). To grade the quality, we used the Jadad score  10  for RCT and a modification of The Oxford Centre for Evidence-based Medicine Levels of Evidence in its updated version of May 2011  11  for the remaining study designs.
SLR and Delphi results The SLR retrieved more than 5000 articles. We additionally analyzed 633 abstracts from ASCO/ ASCO-GU and ESMO meetings, yet none met the inclusion criteria (see supplemental data). As described in the methods section, due to a lack of robust evidence the experts based their recommendations on other aspects like their experience, patients/disease/health system characteristics, indirect data from other RCTs, and preliminary published data. Finally, we show the preliminary data (from observational studies or phase I-II RCTs) captured from the project. The expert panel generated 14 recommendations that were voted. The Delphi response rate was 70%. All but one recommendation achieved the pre-established LA in the first round. During this round, however, the Spanish Medicine Agency journals.
Discussion The results of CHAARTED and LATITUDE trials,  2, 3, 33  along with the approval of several new drugs,  [4] [5] [6] [7] [8]  have raised new questions and issues as for the management of mCRPC. In this setting, the impact of upfront DOC or ABI plus ADT on subsequent therapies has so far been poorly explored. In this research, we sought to create practical recommendations for different clinical scenarios, which prove to be quite common in daily practice. As there are no randomized controlled trials pertaining to this clinical setting, we performed a comprehensive literature search using not only the main medical databases, but also the proceedings of international congresses and scientific meetings. We decided to deliver a treatment algorithm and formulate recommendations for general clinical scenarios, and provide some guidance for specific situations within these scenarios.
Acknowledgements We would like to thank  Luis León Mateos ,  Natalia Fernández Nuñez ,  Martín Lázaro Quintela ,  Sergio Vázquez Estevez ,  Ovidio Fernández Calvo ,  Francisco Javier Afonso Afonso ,  Rodrigo Lastra ,  Pablo Gajate Borau ,  Carlos Aguado ,  Guillermo de Velasco ,  Carlos Álvarez Fernández ,  María Sereno Moyano , and  Diego Soto de Prado  for their participation in the Delphi process.
As a ChIP target to enrich promoters (as well as other generegulatory elements), we selected the histone modification H3K4me1. H3K4me1 marks both promoters and enhancers that are active in a particular cell type  28  ; however, differently to other H3K4 methylation states, H3K4me1 is also present at many transcriptionally inactive promoters, and at most inducible promoters in the pre-stimulation state (Supplementary Figs. 1c,  d  and 2a ). Fragments mapping to regions surrounding annotated promoters were significantly enriched within the H3K4me1 ChIP-MNase datasets compared to the level corresponding to uniform genomic coverage (or, put differently, the same level of coverage at these regions was attained from fewer sequencing reads, compared to non-targeted MNase digestion). Promoters with enriched coverage included those driving both high-and low-levels of gene expression, as well as promoters of inducible and non-expressed genes, and included a diversity of chromatin and sequence features associated with promoter function (for instance CGIs; Fig.
Nucleosome patterning defines two inducible promoter classes. We next analysed nucleosome positioning during activation of stimulus-inducible promoters. The high resolution of ChIP-MNase is instrumental when analysing minor subsets of promoters, for which large numbers of instances are not available to generate averaged profiles from lower-coverage data. We treated DCs with bacterial lipopolysaccharide (LPS), a potent proinflammatory stimulus that activates rapid expression of hundreds of target genes, and identified LPS-inducible genes by transcriptomic analysis of mRNA levels (note that genes assigned as non-inducible in this setup may nevertheless include some that might be induced by other stimuli). We then analysed nucleosome positioning in ChIP-MNase datasets from replicate samples before and after cellular stimulation.
5d, e , Supplementary Fig. 13d ). BAF knock-down also did not impair inducible recruitment of the transcriptional activator NF kappa B p65 to target sites at TNF-α-responsive promoters (Supplementary Fig. 14 ). Using publicly available datasets of genome-wide binding by the BAF complex in fibroblasts  61, 62  , we found that in unstimulated cells, the BAF complex binds to promoter regions of the majority of both expressed and inducible genes (Supplementary Fig. 15 ). Furthermore, at inducible genes, BAF complex binding is detectable before stimulation at both patterned and nonpatterned promoters, despite their different remodelling requirements for activation. It is not known how the BAF complex is generally recruited to its genomic target sites, but these data imply that it broadly binds to active and stimulus-inducible promoters independently of their remodelling requirements.
MNase digestion efficiency was assessed by anaysis of 1 μl of each digestion using Agilent HS DNA chips, and for each initial sample, the digestion yielding approximately 80% mononucleosome-sized fragments was selected for sequencing. This was chosen to encourage complete digestion of inter-nuclesomal DNA, while limiting the scope for MNase nibbling into the edges of nucleosome-bound regions (which is revealed by the appearance of sub-nucleosomal-sized DNA fragments in over-digested samples; see 50 U digestion in Supplementary Fig. 1a ). For single-end sequencing, mononucleosome-sized fragments in the range 120-170 bp were gelpurified; for paired-end sequencing, all DNA fragments were used, and mononucleosome-sized fragments were identified during data analysis. Sequencing libraries were prepared using the NebNext Ultra II DNA library kit (New England Biolabs), and sequenced using HiSeq2000 or NextSeq500 instruments (Illumina).
The level of residual Brg1 and Brm mRNA (Fig. 6a ) was measured by quantitative RT-PCR, using primers agagaagcagtggctcaagg and agatttcttctgccggacct (to detect Brg1) and gccagtggatttcaaaaagataaa & ttgtgacagagaagcatgacg (to detect Brm). Antibodies. Monoclonal anti-HeK4me1 (clone CMA-302) was provided by H. Kimura  86. Anti-pol-II S7P (clone 4E12; cat 04-1570) was from Millipore, anti-pol-II S5P (cat ab5121) was from Abcam, anti-p65 (C20, cat sc732) was from Santa Cruz biotechnology. ChIP-MNase data processing. Demultiplexed sequence data was aligned to the mouse mm9 genome using bowtie (with options -v 2 -a -m 5 --tryhard for singleend sequencing, or -v 2 -a -m 5 --maxins 2000 --tryhard for paired-end sequencing); alignment rates were typically around 80% for H3K4me1 ChIP-MNase, and around 90% for pol-II ChIP-MNase.