dataesr/scientific-paragraphs-categorization huggingface dataset:
Use of pext in variant interpretation To evaluate the utility of transcript expression-based annotation in Mendelian variant interpretation, we assessed the number of variants that would be filtered based on a pext cut-off value of less than 0. 1 (low expression) across GTEx tissues for three gene sets. First, we evaluated high-quality pLoF variants in the 61 manually curated haploinsufficient genes in gnomAD and ClinVar  23. The low pext expression bin resulted in filtering of 22. 8% of pLoF variants in haploinsufficient developmental delay genes in gnomAD, but only 3. 8% of high-quality pathogenic variants in ClinVar (P = 4. 7 × 10 -35 ) (Fig. 4a , Methods). We next compared pLoF variants in autosomal recessive disease genes found in a homozygous state in at least one individual in gnomAD and any pLoF variant in these genes in ClinVar and observed similar results: expression-based annotation filters 30.
Discussion We have described the development and validation of a transcript expression-based annotation framework to integrate results from transcriptome sequencing experiments into clinical variant interpretation. Although our initial analysis uses GTEx, our method can be used with any isoform expression dataset to annotate any variant file rapidly in the scalable software framework Hail (https://hail. is). For example, annotation of more than 120,000 gnomAD individuals with GTEx takes under an hour using 60 cores, at a cost of about US$5 on public cloud compute, which can be further scaled to larger datasets. In addition, the annotations we provide are flexible: although we have described the use of average transcript-level expression across many tissues, alternative approaches such as using maximum expression across any tissue may prove useful depending on variant interpretation goals (Supplementary Figs.
The code used to generate pext is available as open source software (https://github. com/macarthur-lab/tx_annotation). In addition, we provide a precomputed file of the transcript expression value for every possible single nucleotide variant in the human genome. This metric has already proven useful in variant curation for the identification of drug targets  33  and for filtering variants for the identification of human knockouts  1. Overall, our metric can be incorporated into variant interpretation in Mendelian disease pipelines, analyses of rare variant burden, and the prioritization of variants for recall-by-genotype studies.
The resulting gene list is available at gs://gnomad-public/ papers/2019-tx-annotation/data/gene_lists/HI_genes_100417. tsv. We extracted pLoF variants, defined as essential splice acceptor, essential splice donor, stop-gained, and frameshift variants, identified in the 61 haploinsufficient disease genes from the gnomAD v2. 1. 1 exome and genome sites tables, and considered only those pLoF variants that passed random forest filtering in the gnomAD dataset, and were annotated as high confidence by LOFTEE v1. 0. Of 61 genes, 55 had at least one high-quality pLoF available in gnomAD. We performed manual curation of 401 pLoF variants using a web-based curation portal to identify any reason a pLoF may have been a variant calling or annotation error, and categorized the likelihood of each variant being a true LoF.
These variants often affected poorly conserved exons, as determined by PhyloP 37 , PhyloCSF  19  and visualization in the UCSC browser  36. The only exceptions to the minority of transcript criteria were cases where the exon was well conserved, which relegated the categorization to probably not LoF. Variants within the last coding exon, or within 50 base pairs of the penultimate coding exon were also considered not LoF, unless 25% < x < 50% of the coding sequence was affected, in which case the variant was deemed probably not LoF. If >50% of the coding sequence was disrupted by a variant in the last exon, this was deemed probably LoF. Other transcript errors included: re-initiation errors; upstream stop codons of a given LoF variant; variants that fell on exactly 50% of coding RefSeq transcripts; and/or partial exon conservation.
We foresee the non-normalized ext values to be useful when only considering one tissue of interest. To allow for taking average expression values across tissues of interest, we normalize the expression value for a given value to the total expression of the gene on which the variant is found. This is carried out by dividing the ext value with the sum of the expression of all transcripts per tissue in transcripts per million (TPM) (Supplementary Fig. 3b ). The resulting pext value can be interpreted as the proportion of the total transcriptional output from a gene that would be affected by the given variant annotation in question. If the gene expression value (and thus the denominator) in a given tissue is 0, the pext value will not be available (NA) for that tissue. When taking averages across tissues, such unavailable pext values are not considered (that is, when taking the mean across tissues, we remove NA values).
The full transcript-expression aware annotation pipeline, implemented in Hail 0. 2, is fully available at https://github. com/ macarthur-lab/tx_annotation with commands laid out for analyses in the manuscript. Passing a Hail table through the tx_annotate() function returns the same table with a new field entitled 'tx_annotation' which provides either the ext or pext value per variant-annotation pair, depending on parameter choice. We provide a helper function to extract the worst consequence and the associated expression values for these annotations. All analyses in the manuscript are based on the worst consequence of variant, ordered by VEP  38.
9 for pathway enrichment analysis and used the web browser for FUMA GENE2FUNC feature  39  , which incorporates Reactome  40  , KEGG  41  , Gene Ontology 42 (GO) as well as other ontologies. Default parameters were used for FUMA, with all protein coding genes as the background list. Results from FUMA pathway analysis are available in Supplementary Fig. 12 , and full results are available in Supplementary Table  7. Analysis of pext values for LOFTEE flags and the MAPS calculation were performed using the gnomAD v2. 1. 1 exome dataset. Calculation of MAPS scores was previously described  21  and is implemented as a Hail module, as also described previously  1. MAPS is a relative metric, and cannot be compared across datasets, but is a useful summary metric for the frequency spectrum, indicating deleteriousness as inferred from rarity of variation (high values of MAPS correspond to lower frequency, suggesting the action of negative selection at more deleterious sites).
Gene list comparisons To evaluate the filtering power of the pext metric for Mendelian variants, we evaluated the number of variants that would be filtered with an average GTEx pext cutoff of 0. 1 (low expression) in the ClinVar and gnomAD datasets. We downloaded the ClinVar VCF from the ClinVar FTP (version dated 10/28/2018), imported it into Hail, annotated it with VEP v85 against Gencode v19, and added pext annotations with the tx_annotate() function. All evaluated variants were annotated as HC by LOFTEE v1. 0, and ClinVar variants were filtered to those marked as pathogenic, with no conflicts, and reviewed with at least one star status. For variants in 61 haploinsufficient genes, we identified any variant identified in at least one individual with any zygosity in both datasets. For variants identified in autosomal recessive disease genes, we used a list of 1,183 OMIM disease genes deemed to follow a recessive inheritance pattern by Blekhman et al.
44  and Berg et al. 45  (available as https:// github. com/macarthur-lab/gene_lists/blob/master/lists/all_ar. tsv). We compared the pext value for all pLoF variants identified in ClinVar versus any variant in a homozygous state in at least one individual in the gnomAD exome or genome datasets. Finally, we used a LOEUF cutoff of 0. 35 to denote constrained genes, and compared any synonymous or pLoF variant in these genes in the gnomAD exome or genome datasets.
De novo and rare variant analysis De novo variants were collated from previously published studies. We collected de novo variants identified in 5,305 probands from trio studies of intellectual disability/developmental disorders (Hamdam et al. 27  : n = 41, de Ligt et al. 28  : N = 100, Rauch et al. 29  : N = 51, DDD  24  : n = 4,293, Lelieveld et al. 26  : n = 820), 1,073 probands with congenital heart disease with co-morbid developmental delay (Sifrim et al. 46  : n = 512, Chih Jin et al. 47  : 561), 6,430 ASD probands, and 2,179 unaffected controls from the Autism Sequencing Consortium  25. We also used a previously published dataset of variants in 8,437 cases with ASD and/or attention-deficit/hyperactivity disorder and 5,214 controls from the Danish Neonatal Screening Biobank  48. In this analysis, we analysed pLoF variants identified in highly constrained genes (first LOEUF decile) with a combined total allele count of ≤ 10 in cases and controls.
Isoform quantifications via salmon To evaluate whether use of a different isoform quantification tool would affect results, we compared results of TCF4 base-level expression (shown in Fig. 2b ), MAPS (Fig. 3c ) and comparison of the number of variants filtered in haploinsufficient developmental disease genes in ClinVar vs gnomAD (Fig. 4a ) using RSEM quantifications used in this study with quantifications using salmon v. 0. 12  17. Due to the intractability of re-quantifying the entire GTEx dataset, we downloaded and requantified 151 GTEx brain cortex CRAM files from the V7 dataset. We first converted CRAMs to fastq files using Picard 2. 18. 20 and ran salmon with the 'salmon quant -i index -fastq1 -fastq2 -minAssigned-Frag1 -validateMappings' command. The index was created with the 'salmon index -t transcript.
Transcript expression aware annotation with fetal isoform expression dataset Although our analyses were based on transcript expression aware annotation from the GTEx v7 dataset, we provide necessary files for pext annotation with the Human Brain Development Resource (HBDR) fetal brain dataset  49  in gs://gnomad-public/papers/2019-tx-annotation/ data/HBDR_fetal_RNaseq. HBDR includes 558 samples from varying brain subregions across developmental time points. We downloaded HDBR sample fastq files from European Nucleotide Archive (study accession PRJEB14594) and obtained RSEM isoform quantification on HBDR fastqs using the GTEx v7 quantification pipeline, publicly available at https://github. com/broadinstitute/gtex-pipeline/) which briefly involves two-pass alignment with STAR v2. 4. 2a  50 and isoform quantification with RSEM v1.
Article with  RNA-SeQC v1. 1. 8 51 , and isoform quantification with RSEM v1. 2. 22. Variants used in all gnomAD analyses in the manuscript passed random forest filtering, and all pLoF variants were annotated as high-confidence by LOFTEE v. 1. 0,which is described in an accompanying manuscript 1. Scripts to quality control the gnomAD dataset are available at https:// github. com/macarthur-lab/gnomad_qcand the scripts to generate files for the analyses are available at https://github. com/macarthur-lab/tx_annotation. Acknowledgements We thank all of the research participants for contributing their data. This work was supported by  NIDDK   U54 DK105566 ,  NIGMS R01 GM104371 , and the  Broad Institute. KJK was supported by  NIGMS   F32 GM115208. A. O. L was supported by  NICHD   K12 HD052896.
Data availability We used the gnomAD v. 2. 1. 1 sites Hail 0. 2 (https://hail. is) table that is accessible publicly at gs://gnomad-public/release/2. 1. 1 and at https:// gnomad. broadinstitute. org. The GTEx v7 gene and isoform expression data were downloaded from the GTEx portal (gtexportal. org). The LOEUF constraint file was downloaded from gs://gnomad-resources/ lof_paper/. All files used in the analyses in the manuscript are available in gs://gnomad-public/papers/2019-tx-annotation/.
APcNER corpus We randomly sampled (stratified on document types) 147 documents from the dataset used for de-identification at AP-HP, excluding prescriptions and admission reports. The AP-HP de-identification dataset is a set of 3223 French-language medical documents sampledwith upsampling of rare documents types -over 50 millions documents from the AP-HP Data Warehouse, which included EHR data from 39 hospitals. The APcNER included 4 main types of documents: discharge summaries, letters from physicians, operating reports and additional examination reports. Detailed document types can be found in the supplementary Table  S1. We based our annotation guideline on the UMLS® semantic types  [13]  , Table  1  details the 5 medical entities that we annotated (drug name, sign or symptom, disease or disorder, diagnostic procedure or lab test and therapeutic procedure).
We used BRAT Rapid Annotation Tool (BRAT)  [14]. The general guidance for annotation was to annotate the most complete entities (e. g. "Non-ST segment elevation myocardial infarction" and not only "myocardial infarction") with no possible overlap between entities (see Appendix Section 2 for the detailed annotation guideline). Documents were annotated by groups of 10 and the annotation time monitored. Documents were pre-annotated using a terminology-based annotator (see below). IL, a medical resident, annotated all the dataset. Then, in order to estimate the quality of the guidelines, we randomly selected 10 documents that NG, a medical doctor, annotated blindly. We then assessed the agreement between the two annotators. Conflicting annotations have been discussed between IL and NG and are referenced in the Annotation Guideline (see Appendix Section 2).
IL went through all the documents a second time in order to disambiguate the conflicting annotations. Finally, for homogenization purposes, we fitted a simple conditional random field (CRF) model to the dataset with the default NER features using Wapiti  [15]. We used this model to detect annotation inconsistencies or case errors, and manually corrected them. Finally, we randomly divided all the documents into 6 folds by stratifying on the types of documents and the length of the documents. The corpus is made available on condition that a research project is submitted to the scientific and ethics committee of the AP-HP health data warehouse ( https://recherche. aphp. fr/eds/recherche/ ).
Terminology based system In English, we extracted drug names using regular expressions from UMLS® (including SNOMED 3. 5 CT®) to create a large dictionary of drug names. In French, we used 10 terminologies, 8 of which were previously referenced in  [10]  (ATC, BPDM, CCAM, CIM-10, DRC, SNOMED, UMLS), and 2 terminologies held by AP-HP (GLIMS, QDOC). Table  1  details the extracted UMLS semantic types by entity type. Terms were extracted using minimal regular expression rules, then tokenized using Stanford CoreNLP  [17]. We discarded common terms based on Wikipedia word count. The matching rules were based on the apache-UIMA framework, CoreNLP and dkPRO and allowed multiple word matching, stop words, accent normalization and case insensitive matching. The source code is available with the GLP-3 license ( https://github.
Evaluation methodology and metrics First, we evaluated the systems on the i2b2-2009 corpus and its reduced version, i2b2-small, and on the APcNER corpus with labels limited to drug names. Then we evaluated the systems on the entire APcNER corpus, as one multi-class task. We compared the models based on F-measure, precision and recall using the CONLL definition: " precision is the percentage of named entities found by the learning system that are correct. Recall is the percentage of named entities present in the corpus that are found by the system. A named entity is correct only if it is an exact match of the corresponding entity in the data file ". We also compared the model based on partial match, which allowed the boundaries of the entity to mismatch. Neural network models training is highly non-deterministic and is subject to the random seed choice.
Corpus and annotation process APcNER corpus The first round took in average 87 min per 10 documents and a total of 22 hours. The second round took in average 28 min per 10 documents and a total of 7 hours. Both round took in average 115 minutes per 10 documents and a total of 28 hours. The inter-annotator agreement after the first two round and before the CRF harmonization is reported in Comparison between a terminology-based system, a supervised model (biGRU-CRF) and a hybrid system on the i2b2-2009 corpus, i2b2-small (a reduced version of the former corpus), and APcNER. The evaluation metrics are F-measure, precision and recall for exact match and partial match. Table  3  summarizes the results of Drug name recognition in the i2b2-2009 corpus, i2b2-small (reduced version of the former dataset) and the APcNER corpus.
Since BS are naturally present in the small intestine lumen where sterol and BS absorption take place, we hypothesized that bacterial BS deconjugation and cholesterol-to-coprostanol conversion could impact the cholesterol fate in the host body compartments. In order to assess this impact, we adopted an integrative approach in which literature based knowledge as well as in vitro and in vivo experimental data are used to generate a whole-body mathematical model of cholesterol metabolism in human holobiont including its associated gut microbiota. In a dedicated experiment, cholesterol was tracked in mice, in order to investigate the distribution of ingested cholesterol in different host compartments, and determine the amount of bioavailable cholesterol in the gastrointestinal tract. We also characterized in vitro BS deconjugation and cholesterol-to-coprostanol conversion activity in several commensal bacterial strains.
Whole-Body Model Calibration We adapted a strategy previously used for model calibration  (van de Pas et al. , 2011 (van de Pas et al. , , 2012)). Documented steady-state flux and levels of cholesterol in mice were collected, discarding at this stage the bacterial metabolism. The unknown flux were reconstructed through mass-conservation equations: at steady state, flux balance equations involving the unknown flux are derived. Additional equations are set to conserve the ratio of transport flux between blood and liver compartments. At end, as many conservation equations as unknown flux are defined. All the parameters were then obtained straightforwardly by direct computation of the parameters, given the flux and cholesterol levels at steady-state, as indicated in Table  S1. Next, we upscaled the growth models of specialized strains obtained in vitro to mimic the metabolism of a complex microbiota in vivo: the dynamics of coprostanol degradation was calibrated on in vivo data collected from the literature (see Table  S1  and Table  2  for references), and the BS degradation was deduced from the BSH activity measured during the animal experiments.
Numerical Implementation The model was implemented in the Matlab software (MathWorks, Natick, MA, USA). The time integration of the ODEs and DDEs was achieved with, respectively, the ode15s and the dde23 matlab functions. Bayesian inference was performed with the MCMC matlab toolbox (https://mjlaine. github. io/mcmcstat/) (Haario et al. , 2001 (Haario et al. , , 2006)). Linear regression was performed with the R lm function.
For the cholesterol cycle, we selected the bacterial capacity for cholesterol converters (CCC MAX ), cholesterol synthesis rates (ICS max , HCS max and PCS max for respectively the intestinal epithelium, the liver and the peripheral tissues, that were shifted all together), transport from blood to liver (k LDL,ha and k HDL,ha , shifted conjointly), transport from liver to blood (k HCo ), cholesterol release (BCR max ) and dietary intake (f meal ). We sampled uniformly (n = 11 • 10 5 samples) the parameter hypercube ranging in ± 50 % the basal value obtained after model calibration, except for the bacterial carrying capacities that were uniformly shifted between 0. 01 and 100 times the basal value, with the fast99 method  (Saltelli et al. , 1999). The R package sensitivity (https://cran. rproject.
Funding for this work was from  INRA metaprogramme MEM  ( Modchocycle  project),  SATT   Paris-Saclay Micro-chol  project  2016-1211l. AK was supported by the project  CMCU-PHC Utique  (no  19G0819 )- Campus France  (no  41786NC ). This publication has been written with the support of the  AgreenSkills+ fellowship  programme which has received funding from the  EU's Seventh Framework Programme  under grant agreement N  • FP7-609398  (AgreenSkills+ contract).
) and externally (the correct large scale structure). The simulations presented here thus constitute an extension and improvement on a canon of previous work. A number of improvements have been made regarding the constrained simulation algorithm, the MHD galaxy formation models, as well as the input data. These are the first hydrodynamic zoom-in, z = 0 simulations run on ICs constrained by peculiar velocities presented in the CosmicFlows-2 catalogue  (Tully et al. 2013, see Section. 2. 1). The CosmicFlows database and catalogue have been used extensively before to study the local universe, including the cold spot repeller  (Courtois et al. 2017) , the Vela super cluster  (Courtois et al. 2019) , the bulk flow  (Hoffman et al. 2015) , and the Virgo cluster  (Sorce et al. 2016c (Sorce et al.
Magnetohydrodynamics with the moving-mesh code Arepo The arepo code  (Springel 2010; Pakmor et al. 2016 ) solves the ideal magnetohydrodynamics (MHD) equations on an unstructured Voronoi-mesh with a second order finite volume scheme. The Voronoi mesh is generated from a set of mesh-generating points, each of them creating one cell. The mesh-generating points move with the gas velocity, which reduces the mass flux over interfaces and the advection errors compared to static mesh codes, making the code quasi-Lagrangian. In addition, mesh-generating points of highly distorted cells are slowly moved towards their cell centers to make sure that the mesh stays regular  (Vogelsberger et al. 2013). Additional explicit refinement and derefinement is used to control the properties of the cells where appropriate.
Thus in some cases, the LGs shown here are able to produce Magellanic Cloud or M33 like analogs. Figure  12. The radial distribution of satellite galaxies whose luminosity L V > 10 5 L. This cut is chosen since it is believed to be the completeness limit for the observed LG satellites. Data for the M31 and MW are shown as the blue horizontally and vertically hatched (respectively) corridors. Observational data is taken from the compilation presented in  Yniguez et al. (2014)  updated with newly discovered dwarfs.
2015). The ICs are run with the cosmologiocal MHD code arepo  (Springel 2010)  and the Auriga galaxy formation model of  Grand et al. (2017). The result of the procedure is a set of 13 intermediate and 3 high resolution cosmological magneto-hydrodynamical simulation of the LG within the dynamical environment as described by measurements of the peculiar velocity field. The Virgo cluster is reproduced in the correct location and at the correct distance as is a void region analogous to the Local Void. A filamentary structure, abutted by the simulated Local Void and stretched from the LG to the Virgo cluster is also reproduced. Within this environment, pairs of galaxies are found at roughly the correct distance from each other and from Virgo, with roughly the correct mass and mass ratio, negative relative velocity, and with cold gas discs.