dataesr/scientific-paragraphs-categorization huggingface dataset:
DoEstRare enables to incorporate position information and is powerful to detect clusters of disease risk variants. DoEstRare is a good alternative test to use in addition to classical strategies in order to explore genetic architectures involving functional domains. This test is advantageous in the context of analyzing long transcripts which are susceptible to contain important sub-regions. However, the density estimation of rare variant positions may be limited by testing a small group of variants. As discussed previously, DoEstRare is sensitive to short genetic areas that are not well sequenced, which may result in the presence of false positives. The quality control is an important step in the analysis of rare variants, and DoEstRare can also be used to identify problematic sequences. Our newly developed test DoEstRare is so far designed for deleterious rare alleles in case/ control studies.
Simulation framework We conducted genetic simulation studies to evaluate and compare the performance of DoEstRare in terms of power and type I error. Our simulation workflow for each replicate is described with the following steps. It briefly consists in generating a haplotype matrix from which are sampled cases and controls, the disease risk model being a logistic regression model (see Fig  6). Step 1: We generate 10,000 haplotypes for a 10kb region using a backward coalescent model implemented in the COSI program  [22]. Parameters correspond to what is called the "bestfit" model by  Schaffner et al. (2005) , which were obtained by calibration. Haplotypes are sampled from what corresponds to the European population in this model. Step 2: In the haplotype matrix generated in step 1, we select rare variants with a MAF 2 [0.
001;0. 01]. We set a minimal MAF to avoid a lot of non-observed causal mutations in the simulated data, a framework leading to the null hypothesis model because of the very low frequency of a large proportion of variants. Step 3: We determine rare causal variants for the logistic regression model. Causal variants are determined according different scenarios related to their positions on the gene. These scenarios are explained further in the simulation framework description. Step 4: We sample two haplotypes from the haplotype matrix generated in step 1 to constitute the genotype data X i for the i-th individual. Step 5: The phenotype of the i-th individual is simulated with the following logistic regression model: logitðPðY i ¼ 1jX i ÞÞ ¼ b 0 þ X i T β with β 0 the intercept and β the vector of regression coefficients for the genetic effects.
Real data We performed DoEstRare and other rare variant association tests (see section Tests under comparison) on BrS and EOAD data. We analyzed the significance result similarities between the different tests by using a PCA  [28]. The PCA data is the matrix containing (-log 10 (p-value)) jt for gene j in row and statistical test t in column. The PCA was performed with the R package FactoMineR  [38]. Data from the BrS study. We applied DoEstRare and 14 rare variant association tests to BrS data published by Le Scouarnec et al. (2015)  [23]. In this study, rare variant association tests were conducted to identify new genes of susceptibility for BrS. A significant enrichment of SCN5A rare variant carriers was observed in BrS patients. In this study, cases include 167 patients diagnosed with BrS, and controls include 167 individuals aged over 65-year old and showing no history of cardiac arrhythmia.
Both cases and controls are individuals of European origin. This is a candidate gene study in which coding sequences of 163 candidate genes have been captured and sequenced. In this publication, burden test results for 45 genes are published. These genes have been previously shown to be related to cardiac arrhythmias or conduction defects and/or sudden cardiac death. The functional units tested are genes and more specifically the coding regions with a margin of 10 bp to take into account splicing sites. In the present study, rare genetic variants were defined as variants with a MAF<1% in the Exome Aggregation Consortium (ExAC) database for the Non-Finnish European population (NFE) (release 0. 3 downloaded at ftp://ftp. broadinstitute. org/pub/ExAC_release/release0. 3/ ExAC. r0. 3. sites. vep.
vcf. gz) [39]. To avoid false positives, we excluded variants found in more than 5% of cases or controls but absent from ExAC database. Unlike the burden test framework described in the original publication, we analyzed all variants regardless their Sequence Ontology (SO) terms, estimated with Ensembl (http://www. ensembl. org), which describe the type of consequence of the mutation. We evaluated significance of association tests using a standard permutation procedure (excluding CAST, SKAT, SKAT-O and PODKAT) with 1,000 phenotype permutations for all genes except SCN5A, which is a major gene implicated in BrS and where we performed 200,000 permutations. Note: More details about population sampling, sequencing and variant calling can be found in the publication of Le  Scouarnec et al. (2015)   [23].
Data from the Alzheimer study. We performed rare variant association tests on EOAD data from  Nicolas et al. (2015)   [24]. In this study, a significant enrichment of SORL1 rare variants was detected in EOAD patients. Published data, after quality control preprocessing, include 498 controls from 5 different French cities and 484 EOAD patients recruited by the French National CNR-MAJ consortium (205 patients with positive family history). We brought some modifications to the initial design in order to allow a robust comparison between tests. First, we decided to minimize technical biases by including only cases and controls that were sequenced by either Agilent SureSelect Human All Exons V5 or Agilent SureSelect Human All Exons V5UTR capture designs. Additional controls from another French city were also included, due to the French Exome project's progress.
Our analysis finally compared 555 controls to 431 EOAD patients. In this exome study, we annotated variants with Variant Effect Predictor (Ensembl). For each protein coding gene, we analyzed the "canonical" transcript: the transcript which presents (1) the longest CDS length, (2) if CDS lengths are equal, the longest transcript length with UTR regions. A total of 19,076 autosomal protein coding genes were annotated. For association tests incorporating position in the transcript, we used CDS regions and each variant was annotated with a CDS position. Filters on genetic variant are the same as for the BrS data analysis. Rare genetic variants present a MAF<1% in the ExAC database for the NFE population and present a MAF in cases and in controls both less than 5%. To avoid false positives we excluded all rare variants which were very significant, as they could influence results from gene-based tests.
Acknowledgments The authors would like to thank  Genomics and Bioinformatics Core Facility of Nantes (Geno-BiRD, Biogenouest ). Computations were also performed on the  "Centre de calcul intensif des Pays de la Loire" (CCIPL) computer "Erdre". We are also grateful to French clinical network against inherited cardiac arrhythmias. We gratefully acknowledge  Pierre Lindenbaum  and  Floriane Simonet  for technical support in bioinformatics, biostatistics and data management. Consortia: The  FREX Consortium 's principal investigators are Emmanuelle Genin,  Dominique Campion ,  Jean-Francois Dartigues ,  Jean-Francois Deleuze ,  Jean-Charles Lambert , and  Richard Redon. Collaborators are as follows: bioinformatics group ( Thomas Ludwig ,  Benjamin Grenier-Boley ,  Sebastien Letort ,  Pierre Lindenbaum ,  Vincent Meyer ,  Olivier Quenez ), statistical genetics group ( Christian Dina ,  Celine Bellenguez ,  Camille Charbonnier-Le Cle  ´zio,  Joanna Giemza ), data collection ( Stephanie Chatel, Claude Ferec, HerveLe Marec ,  Luc Letenneur ,  Gae ¨l Nicolas ,  Karen Rouault ), and sequencing ( Delphine Bacq ,  Anne Boland ,  Doris Lechner ).
, 2013; Wu et al. , 2016). For these reasons, we sought to apply ATAC-Seq in an effort to identify developmentally active enhancers in the zebrafish genome. To aid identification of cell type-specific enhancers, we incorporated fluorescence activated nuclei sorting (FANS) from a zebrafish transgenic line in which green fluorescent protein (Egfp) labels endothelial cells. Subsequent comparison of ATAC-Seq reads from GFP-positive and GFP-negative nuclei provided a robust and technically straightforward means to identify a compendium of putative lineage-specific regulatory elements in the zebrafish. Moreover, the resulting datasets and plasmid collection from these efforts provide a valuable resource for investigators interested in transcriptional control of vascular development and endothelial differentiation.
Previous studies have demonstrated the utility of using conservation to identify endothelial enhancers in several zebrafish studies  (Bussmann et al. , 2010; Veldman and Lin, 2012). However, the lack of closely related genomes for accurate homology alignment limits a more comprehensive identification of putative enhancers using this approach  (Hiller et al. , 2013). Indeed, analysis of our endothelial ATAC-Seq elements indicate that more than 80% do not bear known conserved sequences, suggesting that available CNE annotations only detect a very small proportion of tissue-specific enhancer elements. Similarly, whole embryo ChIP-Seq datasets likely underrepresents lineage-specific enhancers from cells that comprise a small proportion of the embryo. In any case, ATAC-Seq on cell type-specific nuclei reveals a large number of putative enhancer elements that would otherwise not be detected using previous approaches.
Data analysis For ATAC-Seq, adaptor sequences were removed using cutadapt  (version 1. 3; Martin, 2011)  and reads mapped onto Zv9 using Bowtie2  (Langmead and Salzberg, 2012). Default settings were modified to allow paired-end fragments up to 2 kb. For quality assessment of ATAC-Seq libraries, we developed and applied ATACseqQC (available through Bioconductor; https://www. bioconductor. org/packages/release/bioc/html/ATACseqQC. html). To visualize mapped reads, we generated bigwig files from BAM output using deepTools2  (Ramirez et al. , 2016). MACS (version 2. 1;  Zhang et al. , 2008)  was used to call enrichment, with the following settings: callpeak -g 1. 35e9 --qvalue 0. 05 --bw 250 --mfold 10 30. Fragments of desired length were extracted from SAM files. Heatmaps and density plots were generated using ChIPpeakAnno  (Zhu et al.
, 2010). To identify differentially mapped regions, we used Diffbind (Bioconductor;  Ross-Innes et al. , 2012). As input, we used BAM files outputted from Bowtie2 and peaks from MACS2 for each replicate. An element was GFP-positive if GFP + /GFP -log 2 fold change in read density >=1 and FDR<=0. 05. Conversely, GFPnegative elements were log 2 fold change <=-1 and FDR<=0. 05. An element was "Common" if fold-change thresholds fell below that for GFP-positive and -negative and the mean read concentration was greater than 4 in either sample. To determine concordance we used ChIPpeakAnno  (Zhu et al. , 2010)  or bedTools  (Quinlan and Hall, 2010). For embryo ChIP-Seq, we utilized available datasets (GEO accession number: GSE32483;  Bogdanovic et al. , 2012). Enriched peaks were called from these data using MACS with default settings.
For CNEs, we utilized previously described elements  (Hiller et al. , 2013). For RNA-seq analysis, reads were mapped onto Zv9 using Tophat2 (v2. 0. 9; Kim et al. , 2013). Differentially expressed genes were identified using Cuffdiff (v2. 1. 1) with a custom transcript annotation. To visualize genomic data, bed or bigwig files were uploaded to a local mirror site of the UCSC Genome Browser. Statistical comparison of fragment per kilobase per million reads (FPKM) and TSS accessibility was performed using an unpaired student's t-test. Correlation of flanking (2. 5x10 5 bp up and downstream of the TSS) GFP-positive, -negative, or common elements and log 2 FC from RNA-seq was analyzed by Spearman's rank correlation. GREAT analysis was performed on the web interface (http://bejerano. stanford. edu/great/public/ html/).
Acknowledgments pDESTtol2pACrymCherry was a gift from  Joachim Berger & Peter Currie  (Addgene plasmid #  64023 ). We thank  Manuel Garber  and  Alper Kucukural  for maintaining the local  UCSC Genome Browser mirror. We are indebted to  John Polli  and  Pat White  for zebrafish care and maintenance. We thank  Tom Fazzio  for helpful comments on the.
Preface: The sharing of legacy preclinical safety data among pharmaceutical companies and its integration with other sources of information offers unprecedented opportunities for early safety assessment through read-across and predictive modeling. The experience of the IMI eTOX project is presented. Subject terms: Drug Safety. Toxicology. Data Sharing. In silico prediction. Non-clinical safety assessment is often faced with the challenge of assessing candidate compounds with no or insufficient experimental data. Whereas relevant databases and reliable in silico tools exist for mutagenicity prediction, analogous resources for identifying potential organ toxicities are not as common or well developed. Scientists, therefore, have to resort to suboptimal procedures such as literature search and personal experience with similar compounds or classes.
In parallel, there is a wealth of relevant data buried in the archives of the pharmaceutical industry that has not yet been leveraged. These data mainly exist in paper or pdf formats and, consequently, are difficult to search and analyze. In order to overcome these limitations and advance early safety assessment, 13 pharmaceutical companies, 11 academic partners and 6 small and medium-sized enterprises (SMEs) joined forces in the eTOX project, which started in January 2010 under the sponsorship of the European Innovative Medicines Initiative (IMI). Since the availability of a wide and representative collection of historical data is fundamental for generating reliable predictions, the main goals of the eTOX project were (i) to build a shared and mineable database containing a broad and relevant collection of data, constituted mainly by repeat-dose toxicity studies contributed by the pharmaceutical companies participating in the project, and (ii) to use the database and other sources of information for enabling more effective readacross and predictive modeling of safety endpoints.
The eTOX database and other sources of information At the end of the project (December 2016), the eTOX database contained 8,196 extracted toxicity studies carried out on 1,947 compounds, which report nearly nine million preclinical data points (in life observations, gross necropsy, histopathology, laboratory values like clinical chemistry, hematology, urinalysis). The database contains repeat-dose studies executed in different species (rat: 58%; dog: 28%; monkey: 7%; mouse: 5%), spanning different study durations (74% lasting up to 28 days; 17% between 29 and 91 days; 9% longer duration, up to 2 years) and diverse administration procedures (oral: 77%; intravenous: 14%). For a significant fraction of the compounds complementary information is included, such as pharmacokinetics (67%), pharmacological target (44%) and therapeutic indication (16%).
The chemical structure is disclosed for most (75%) of the compounds present in the database. Nearly 20% of the eTOX compounds are FDA approved drugs and the chemical space covered by the eTOX database represents well the internal chemical spaces of the pharmaceutical companies as determined by PCA on above 100 molecular descriptors. This feature is essential for obtaining reliable predictions and performing meaningful read-across. Moreover, all pairwise Tanimoto coefficients were calculated to compare the structural similarities within eTOX and FDA drugs sets, and across the two sets. The structures of FDA drugs were, on average, significantly (p<0. 001) more correlated with those in the eTOX set than among them. Another important feature for predictivetiness is the presence of adequate numbers of positive and negative compounds (e.
g. considering four weeks sub-chronic rat studies, 965 compounds out of 1854 caused treatment-related effects in liver and 870 in kidney). The eTOX database is hosted by an honest broker tasked with providing adequately protected access to the data, enabling the sharing of sensitive information among pharmaceutical companies. A small subset of database is made available for public use from the eTOXsys Dashboard. The eTOX database and other data resources, such as RepDose, ChEMBL and DrugBank, have been used by eTOX partners for developing nearly 200 predictive models (in vivo organ toxicity: 12 models; in vitro assays for organ toxicity: 30; safety pharmacology: 97; transporters: 26; ADME: 29; etc. ), in which innovative modeling strategies have been applied  (1, 2). The extraction and integration of information from public sources for model development has been facilitated by an ad hoc tool (Collector), which operates on top of the IMI Open PHACTS data infrastructure.
eTOXsys: Integrated platform for read-across and prediction The eTOX database and predictive models can be accessed using an integrated and user-friendly software platform (eTOXsys). This platform incorporates a graphical user interface (GUI) that allows performing sophisticated queries by providing an easy-touse query builder to interrogate different criteria including substructure, structural similarity, study characteristics, finding type, etc. The GUI also allows selecting and executing the available models, presenting the predictions in a way that facilitates their visualization and interpretation by providing model metadata. eTOXsys facilitates the export of queries and predictions in commonly used formats. Moreover, the system incorporates an exploratory module that queries possible human safety liabilities for a compound on the basis of its similarity to marketed drugs based on profiles of both structures and toxicity.
Use and impact in the pharmaceutical industry eTOXsys has not only found its entry into the daily practice of early drug safety assessment but also in later phases, such as the assessment of impurities in drug products that have not been qualified for in vivo studies. The main application lies in the comparison of hit or lead structures with compounds in the database to raise hypotheses regarding organ toxicities (read-across). Given that the database can be searched for a particular pharmacological target, an eTOX pharmaceutical company observed that the same target has been previously addressed by other companies in more than 15% of the compounds stored in the database. This kind of information can help assess whether observed toxicities in early in vivo studies are target-related or off-target.
In certain circumstances, the data can be used to make head-to-head comparisons with competitor compounds without performing additional animal studies, thus contributing to 3R (Refine, Reduce, Replace) policies. Furthermore, the database can be used to analyze the correlation between the presence of chemical substructures and the occurrence of specific toxicities, e. g. if a chemical moiety raises the suspicion of being related to hepatotoxicity, the substructure can be queried in the database in combination with liver findings. The result of this search can be compared with the background occurrence of the liver findings for all compounds. If a significantly higher incidence is found for the substructure, then there is high probability of causality, which can be managed by the medicinal chemists by modifying the substructure without discarding the whole molecule.
Independently of the use in drug development, the eTOX database is also contributing to the refinement of thresholds of toxicological concerns (TTC). A recent analysis of the non-observed adverse effect levels of systemic toxicity studies in eTOX supports the ICH Q3A defined concept that a lifetime dose to 1 mg/day of a non-mutagenic impurity would not represent a safety concern for patients  (3). This study also provided evidence that higher thresholds can be accepted for shorter administration periods during early clinical development.
Challenges, lessons and future perspectives The first challenge to be overcome by the eTOX consortium was the apprehension of the pharmaceutical companies to share sensitive proprietary pre-clinical data. This required a combination of legal (consortium agreement), technical (e. g. database installed behind companies' firewalls and models implemented within self-contained virtual machines), organizational (honest broker concept), psychological (trust gained through collaboration), political (data sharing pressure, such as the FAIR principles) and social (snowball effect) solutions. The second challenge was the lack of standardization in the data contributed by the pharmaceutical companies. This required the collaborative development and implementation of relevant ontologies. The project developed a specific software tool (Ontobrowser) for facilitating such cooperative task.
Using this tool, 105,000 verbatim finding terms were mapped to 7,300 preferred terms aligned with existing CDISC and INHAND terminologies to enable interoperability (see Hpath in S2). We also carried out data curation campaigns to increase the quality of the data incorporated into the database. Moreover, the raw data extracted from the reports was not directly appropriate for number crunching and had to be transformed into comparable toxicity scores suitable for modeling. Quick progress was obtained in hackathons organized by the eTOX consortium, which defined rules for summarizing related toxicological findings and identified underlying relationships between chemical structures and organ toxicities (e. g. causes of bilirubinemia). The development of a high number of models by diverse organizations, their integration into a single system (eTOXsys) and their adoption by the pharmaceutical industry, was another challenge that required strict procedures and tools for enabling a harmonized development, documentation, validation and implementation of the models, as well as their versioning and maintenance.
These tasks required the development of new tools including eTOXlab, a flexible modeling framework, ADAN (4), a method for the assessment of the model applicability domain, and specific protocols for model validation  (5). Models were extensively documented following OECD QSAR reporting recommendations and stored in the eTOXsys Model Meta Database. Executive summaries are accessible though the eTOXsys GUI for facilitating the interpretation of the predictions on the fly. The eTOX IMI grant finished on December 2016 and the project entered into its sustainability phase with SME partners leading the commercial exploitation of eTOXsys. A User Board with representatives of the different partner oversees the maintenance and exploitation processes. Finally, it should be stressed that recently funded IMI projects are extending the in silico toxicology domain furthered by the eTOX project.
Increasing demand for traceable, accurate measurements of power and power quality (PQ) parameters has resulted in an intensive metrology research effort in this area. This paper describes the objectives and activities of EMPIR project 15RPT04 TracePQM, jointly funded by the European Union and the participating countries. The overall goal of this project is to develop and validate a modular, well documented metrology grade system for the measurement of power and PQ parameters using digital sampling techniques. The first results of the research project, including the description of the system design, the software structure, results of the stability tests on wideband digitizers, and the acquisition of extended waveform records using a digitizing multimeter are presented.