dataesr/scientific-paragraphs-categorization huggingface dataset:
8%. Time evolution of the signal-strength measurements and their precision. Comparison of the signal-strength parameter μ fit results in different datasets; in each panel, from left to right: at the time of the Higgs boson discovery, using the full data from LHC Run 1, in the dataset analyzed for this paper, and the expected s. d. uncertainty for HL-LHC for = 3000 fb -1 L. The H → μμ measurements were not available for the earlier datasets due to the lack of sensitivity. Extended Data Fig. 8 | Time evolution of the coupling measurements and their precision. (left) Comparison of the expected 1 SD uncertainties in the κ-framework fit including coupling modifiers for both tree-level and loop-induced Higgs boson interactions, in different datasets: at the time of the Higgs boson discovery, using the full data from LHC Run 1, in the dataset used in this paper, and the expected 1SD uncertainty for HL-LHC for L = 3000 fb -1.
Purpose: Retrospective interpretation of sequenced data in light of the current literature is a major concern of the field. Such reinterpretation is manual and both human resources and variable operating procedures are the main bottlenecks. Methods: Genome Alert! method automatically reports changes with potential clinical significance in variant classification between releases of the ClinVar database. Using ClinVar submissions across time, this method assigns validity category to gene-disease associations. Results: Between July 2017 and December 2019, the retrospective analysis of ClinVar submissions revealed a monthly median of 1247 changes in variant classification with potential clinical significance and 23 new gene-disease associations. Re-examination of 4929 targeted sequencing files highlighted 45 changes in variant classification, and of these classifications, 89% were expert validated, leading to 4 additional diagnoses.
A current and major preoccupation in this field is the definition of standards for periodic and prospective reanalysis of existing sequencing data. Indeed, reanalyzing existing genomic data improves diagnostic yield (7% increase per year). 9, 10 n practice, such an in-depth reinterpretation is mainly manual and time-consuming, with major bottlenecks such as human and funding resources or lack of consistency between centers. 2] [13] [14]  Some companies offer paid black box services, with poorly detailed methods that cannot be reproduced. 15, 16 linical knowledge of rare diseases is contained in expertcurated databases (such as OMIM  17  or Clinical Genome Resource [ClinGen]  18  ), peer-reviewed medical literature, and information sharing between health practitioners through community-based platforms (such as MatchMaker Exchange  19  or ClinVar  20  ).
Reliability and exhaustiveness of information vary widely across these data sources. Furthermore, careful monitoring of clinical knowledge by every laboratory represents an organizational challenge for a prospective reanalysis of acquired data. To enable a systematic, reproducible, and prospective genome interpretation, a collaborative approach for clinical knowledge aggregation combined with automated medical knowledge monitoring and curation is needed. The main community-based repository of genomic knowledge is ClinVar (https://www. ncbi. nlm. nih. gov/ clinvar/), a shared variant interpretation database that featured 1 million submissions in 2020. ClinVar is updated weekly with several thousands of modifications of variant classifications that could affect the diagnostic yield of previous analyses.
There is currently no monitoring system that can highlight these changes at a scale for the complete database. Besides variant classification, gene-phenotype association catalogs are crucial because they are commonly used to design phenotype-specific gene panels for dry-lab filtering and set the frontiers for clinical genome analysis. 21, 22 Although not their primary purpose, variantcentered databases could also theoretically provide a complementary resource to gather gene-phenotype knowledge. In this article, we detail an automated method for the reassessment of variant pathogenicity and gene-phenotype associations through ClinVar follow-up. This procedure, called Genome Alert!, aims at performing a routine and systematic reinterpretation of existing genomic data. The procedure's effectiveness was evaluated through a 29-month multicentric series (2018-2019) of 5959 consecutive individuals screened using targeted sequencing (4929 individuals with hereditary cancers) and exome sequencing (1000 analyses including 356 undiagnosed individuals with suspected Mendelian disorders).
Materials and Methods Genome Alert! standardized procedure ClinVCF, Variant Alert!, and ClinVarome are a suite of tools that constitute the heart of the Genome Alert! standardized procedure. ClinVCF: A ClinVar quality processing method Before comparing different versions of the same source, data consistency needs to be verified. This first step is based on ClinVCF tool, and once every submission has been tracked, data will be processed for the next step. ClinVCF imports monthly updated ClinVar Xtensible Markup Language (XML) files. XML format was preferred over VCF mainly because of better consistency and traceability across versions for the ClinVar Variation ID, the history of changes in each variant classification, and the additional gene-phenotype data availability in XML. ClinVCF considers an automatic reclassification of variants with at least 4 submissions and conflicting interpretations of pathogenicity status.
2 file. Variant Alert!: A variant knowledge monitoring tool Variant Alert! tool aims at identifying changes in variant classification across 2 versions of the database. Changes were defined as (1) a modification in the classification of an existing variant and (2) the creation or suppression of a variant entry. Stratification of the consequences in classification modification was proposed (Supplemental Table  1 ). Major classification modification was defined as a change that may affect the clinical management of a patient (eg, uncertain significance to likely pathogenic status). Minor classification modification was defined as a change that may not affect the clinical management of a patient (eg, pathogenic to likely pathogenic status). Variant Alert! writes 2 files: (1) the list of variants that were modified, added, or removed and (2) the list of genes that were added to or removed from the database.
This gene list is notably used by ClinVarome. ClinVarome: A method for automated gene-disease association evaluation ClinVarome tool aims to periodically and automatically evaluate gene-disease association in the ClinVar database. To differentiate genes on the basis of their clinical validity, the work from European Molecular Biology Laboratory-European Bioinformatics Institute Gene2Phenotype,  24  ClinGen,  18  and Genomic England PanelApp  25  were first compared. Although theoretically comparable, their rationales and contents were partially overlapping and with conflicting classifications. To discriminate candidate genes from definitive gene-disease associations, we decided to use an unsupervised clustering model. Only the genes with at least 1 likely pathogenic or pathogenic variant (single nucleotide variant or indel affecting a single gene) in ClinVar were considered in a list called ClinVarome.
As a consensus criterion, we chose to assess the strength of a gene-disease association through the quantification of 4 variables: (1) count of likely pathogenic and pathogenic variants, (2) highest variant classification (CLNSIG, likely pathogenic or pathogenic), (3) highest ClinVar review variant confidence (CLNREVSTAT, from 0 to 4 stars), and (4) time interval between the first and the last pathogenic variant submission (replication of the gene-disease association event). For these 4 variables, values were gathered through periodic monitoring of changes in the database following the ClinVCF and Variant Alert! tool procedures. Clustering variants according to these variables allowed us to define clusters of genes according to their clinical validity. The scikit-learn Agglomerative Clustering tool (parameters: Euclidean affinity, ward linkage) was used, and t-distributed stochastic neighbor embedding representation (parameters: 2 components, perplexity 150, 2000 iterations, and 1000 iterations without progress) was performed.
Study design and participants To evaluate the clinical impact of Genome Alert!, we collected 5929 consecutive germline sequencing data samples from 3 centers in France between July 2017 and December 2019 as part of their routine genetic investigation: (1) a variant database gathering all class 3 (uncertain significance), class 4 (likely pathogenic), and class 5 (pathogenic) variants identified in a colon cancer-targeted sequencing (14 genes) sequenced in 2540 individuals in the Rouen University Hospital; (2) a cancer-targeted sequencing data set of 2389 individuals by the Cerba laboratory (66 genes); and (3) exome sequencing data of individuals with developmental disorders, rare kidney diseases, or other rare diseases as follows: 108 probands from the Rouen University Hospital, 477 probands (with 356 negative analysis) from the Cerba laboratory, and 415 probands from the Eurofins Biomnis laboratory.
Selection of variants with potential clinical significance All sequencing data were systematically reinterpreted according to Genome Alert!'s report and compared with the initial variant interpretation. For targeted sequencing and exome reanalysis, genomic positions of variants with major changes in classification were queried in the existing patient's variant calling files (variant-centered analysis). For exome data, we performed a reanalysis of variants in VCF with the following criteria: (1) among 75 ClinVarome morbid genes, which were not available in OMIM, and with a second event of gene-disease validation (including a likely pathogenic or pathogenic variant with ClinVar review confidence ≥ 2 stars and a likely pathogenic or pathogenic variant entry subsequent to the initial entry); (2) variant not shared with another individual in the series; (3) sufficient sequencing quality (variant allele fraction > 25% and read depth > 20 reads); (4) rare in Genome Aggregation Database  26  population (frequency <10 -5 if heterozygous genotype or 10 -4 if homozygous genotype); and (5) protein consequence among nonsense, frameshift, missense (missense are selected with Combined Annotation Dependent Depletion 27 score > 30 and MetaSVM 28 = D), or splice variants (based on dbscsnv RF  29  predicted impact score > 0.
ClinVar knowledge dynamics To get insights into variant classification and gene-disease association and to estimate the amount of new clinically relevant information in the ClinVar database available through time, a retrospective analysis of ClinVar submissions over 29 months was performed (July 2017 [included] to December 2019). Of note, VCF genomic positions in ClinVar were introduced in July 2017 and probably are associated with the largest injection in the ClinVar database. The number of variants with ACMG/AMP classification 5 increased from 144,943 to 491,838. Among modifications in the database, the count of major changes was 107,167 in ACMG/AMP classification, and among these, 103,615 resulted in a pathogenicity status, which was previously unreported, whereas 3552 resulted in the revocation of a previously established pathogenicity (Figure  1A ).
Automatic variant reclassification with conflicting interpretations A criticism of the ClinVar database is the misclassification of pathogenic variants, such as the well-known HFE pathogenic variant NM_000410. 3:c. 845G>A. We observed that it was mostly due to a unique outlier submission with a classification for a distinct condition (eg, cutaneous photosensitivity porphyrinuria phenotype). We evaluated our method to remove such outlier submissions. Among all the variants available in ClinVar in December 2019, 22,973 of a total of 503,994 (4. 5%) variants were classified with a conflicting interpretation of pathogenicity. Genome Alert! automatic reclassification method proposes to detect outlier submissions to suggest a consensus classification. This allowed the reclassification of 188 variants from conflict to likely pathogenic or pathogenic classification in 135 genes and 1625 variants in 436 genes from conflict to likely benign or benign classification (Supplemental Table  4 , Supplemental Figures  1  and 2 ).
Clinical impact of changes in variant classification To assess the clinical impact of Genome Alert!'s changes in variant classification, previously analyzed cancerpredisposition targeted sequencing data were assessed (4929 individuals from 2 genetic centers) (variant-centered reanalysis, Figure  3 ). Among all variants detected in this cohort, this method highlighted 45 variants with major changes between the time of analysis and December 2019, which were proposed for manual review by their referring geneticists (Supplemental Tables  5  and 6 ). Among the 45 variants, 30 had been already manually reported by the clinical geneticists as likely pathogenic or pathogenic at the initial time of analysis, meaning that these classifications were ahead of the ClinVar database. The 15 unreported variants were manually curated, looking for additional diagnoses.
Genes from the first cluster hold pathogenic variants with ClinVar's ≥2 stars of review confidence and the second cluster genes include pathogenic variants with different entry dates and <2 stars of review confidence. Genes in the third cluster had 1 strong argument for pathogenicity but needed another event to be fully confirmed (the third cluster genes contained at least 1 pathogenic variant and all pathogenic entries were added at the same date). Because genes in the fourth cluster were only likely pathogenic variants, their gene-disease association remained to be confirmed (Supplemental Table  7 ). To assess the exhaustivity of the ClinVarome, a comparison with the OMIM database was performed. In December 2019, there was a 95% overlap (3675/3858) between OMIM morbid clinical genes and ClinVarome morbid genes.
Clinical impact of ClinVarome morbid genes not available in OMIM We evaluated the relevance of this approach by performing a selective reanalysis of a subsample of the new entries in the ClinVarome, focusing only on the 75 genes that were absent from OMIM morbid list and were referenced in ClinVarome's first and second clusters (gene-centered reanalysis). This experiment highlighted 42 variants in 356 negative exome sequencing data. In this data set, 42 variants were prioritized and were proposed for further interpretation. Among them, 39 were excluded by the expert. The experts' arguments included the presence of variants unrelated to the disease phenotype or a single case series available in the literature. A total of 3 variants were further explored with Sanger sequencing validation, of which 2 were excluded because of artifact status or discordant inheritance pattern (Figure  5 ).
Overall, this method could ascertain a new diagnosis from the 356 negative exome sequencing data. A nonsense DLG4 variant NM_001128827. 1:c. 1840C>T was reported 42 variants identified 356 consecutive exome sequencing patients with negative results from 0 to 2 years since time of analysis were enrolled 1 variant was classified likely pathogenic and had a direct impact on patient care Figure  5  Experimental design for a targeted gene-centered reanalysis. These 75 genes were reported in ClinVarome and not in OMIM and classified as related to a disease (clusters 1 and 2). This list of 75 genes was used for the reinterpretation of negative exome sequencing data (n = 346). Green box represents new diagnosis. Orange boxes represent excluded variants. gnomAD, Genome Aggregation Database. as likely pathogenic, responsible for the patient's phenotype (intellectual disability and microcephaly).
ClinVarome comparison with the GenCC database A comparison of gene-disease validity confidence and exhaustivity of ClinVarome with the GenCC database was performed. In October 2021, there was a 65% (3332 of 5187) gene overlap between the 2 databases. Nonoverlapping genes represent mostly the uncertain gene-disease associations from these 2 databases. Exclusive genes in GenCC (n = 334) were significantly enriched in orange and red genes (151 of 745 orange genes [P <. 0001], 158 of 252 red genes [P <. 0001]). Exclusive genes in ClinVarome (n = 1471) were significantly enriched in third and fourth cluster genes (407 of 501 third cluster genes [P <. 0001], 448 of 743 fourth cluster genes [P <. 0001]). The 2 databases present a high concordance in gene-disease association confidence (Supplemental Table  8 ).
Discussion With the increasing amount of genetic testing performed in health care, there is a critical need for standardized methods to enable prospective genomic data reinterpretation in clinical routine. Through the reassessment of variant pathogenicity and gene-phenotype associations in ClinVar, Genome Alert!'s data mining method proposes the automatic report of a handful of variants that can reasonably be manually interpreted. Our method was applied to a multicentric series of 4929 sequencing tests with various local bioinformatic systems. Genome Alert! successfully allowed new diagnoses in targeted and exome sequencing through query of laboratory's VCFs or variant database and proposed a portable and open-source framework for an automated reanalysis of sequencing data. Retrospective monitoring of the cutting-edge medical literature on existing genomic data is a major concern for paving the way to genomic medicine.
30 There are numerous technical and medical challenges in setting up a routine procedure for reanalysis. This work explored the dynamics of change across all fields of genomic medicine in ClinVar. Several medical indications for genomic testing were noticed to bear numerous changes in variant classification. Retrospective analysis of the ClinVar database provided an estimation of new clinically relevant information reported each month, which may lead to additional diagnoses in the existing data. 31 Overall, 9. 94 % (1125) of likely pathogenic variants were eventually downgraded and reclassified as benign variants, likely benign variants, variants of uncertain significance, or variants with conflicting interpretation of pathogenicity in ClinVar over the study period (Supplemental Table  3 ).
The prospective reassessment of ClinVar highlighted numerous conflicts in variant classification. Although our system rarely reclassifies variants with conflicting interpretations, this automatic reclassification method aims to at least remove these potential errors. The expert review of ClinVCF automatic reclassification validates this method on the basis of outlier submission removal using the IQR method, and succeeds in reclassifying abnormalities such as the HFE pathogenic variant NM_000410. 3:c. 845G>A. This work highlights the value of the persistence over time of a classification for relevant genomic information. This work specifically focused on oncogenetics and cardiogenetics, fields in which variant interpretations are particularly conflicting and shifting. 32, 33 Overall, in the ClinVar database, 188 variants could be reclassified in 29 months (ranging from 2017 to 2019).
After 8 months, in August 2020, a total of 307 variants were reclassified, highlighting the importance of a systematic and partially automated variant reassessment (Supplemental Figure  2 ). Existing literature for gene-centered reanalysis has emphasized the importance of OMIM as an updated resource but not exhaustive. 34 To explore and evaluate specifically the ClinVar database for gene-centered reanalysis, we chose to focus our reanalysis on 75 high-confidence ClinVarome morbid genes (first and second clusters) not available in OMIM morbid genes list. Complementary to OMIM morbid genes, these high-confidence ClinVarome morbid genes from the first and second clusters could provide additional diagnoses in exome or genome sequencing analysis (genecentered reanalysis). One additional diagnosis was identified with this tight subsampling of variants among the 356 negative exomes, validating the proof of concept.
Additional experiments could be performed to fully evaluate the Clin-Varome, such as reanalysis with the full list of ClinVarome morbid genes not found in OMIM, additional cohorts, or an extended analysis considering the variants with different phenotypes not reported in the literature. On the basis of literature data and feature engineering processes from all ClinVarome features during clustering model development, we identified 4 discriminative features for gene-disease clinical validity available in ClinVarome data. Overall, the evaluation relies mainly on the amount of knowledge but also on reported review confidence and more importantly on the time-scale of entries. The Genome Alert! gene-curation via machine learning methods provides an original attempt for automated evaluation of gene confidence in disease.
Genome Alert! proposes a standardized clinical validity confidence score that could allow a prospective gene-phenotype association assessment. As such, this approach could be useful to update in silico gene panels. This procedure proposes a complementary approach to the aggregation of multiple expert-reviewed databases such as DDG2P, Genomic England PanelApp, or ClinGen gene-disease validity available in the GenCC database. 35 owever, ClinVarome gene-disease validity confidence is defined for all diseases associated with a gene, which is less precise than curations submitted to the GenCC database. As ClinVarome is a more exhaustive database, this resource could prioritize genes to be curated by GenCC submitters, particularly in the first and second clusters. In summary, Genome Alert! highlights changes with potential clinical significance and provides a large retrospective study of a partially automated system for sequencing data reinterpretation.
Data Availability Software summary Project name: Genome Alert! Project home page: https://genomealert. univ-grenoble-alpes. fr/ Operating system(s): UNIX (Mac, Linux) Programming language: Nim, Python, R License: Apache Licence 2. 0 Any restrictions to use by nonacademics: No Genome Alert! results are publicly available at https:// genomealert. univ-grenoble-alpes. fr/. Relevant data used to generate Genome Alert! results are available from ClinVar FTP (all monthly ClinVar full XML release data were downloaded from https://ftp. ncbi. nlm. nih. gov/pub/clinvar/ xml/) and in the following resources: OMIM (https:// omim. org/), Genomic England PanelApp (https://panelapp. genomicsengland. co. uk/), and RefSeq annotation (ftp://ftp. ncbi. nlm. nih. gov/refseq/H_sapiens/annotation/GRCh38_ latest/refseq_identifiers/GRCh38_latest_genomic.
gff. gz). All codes for generating Genome Alert! procedures are available at public GitHub repositories: ClinVCF tool for ClinVar XML full release processing and extraction to VCF format (https://github. com/SeqOne/clinvcf), Variant Alert! tool to compare ClinVCF release (https://github. com/ SeqOne/variant_alert), ClinVarome tool to evaluate clinical validity of ClinVar morbid genes (https://github. com/ SeqOne/clinvarome), and the Genome Alert! shiny app (https://github. com/SeqOne/GenomeAlert_app).
ChIP-seq analyses Quality controls of ChIP-seq Fastq files were performed by FastQC. ChIP-seq Fastq read files are mapped to the mm10 reference genome using Bowtie2 with default parameters. To determine for ChIP-seq peaks with a narrow profile (CTCF, SMC1 and H3K4me3), datasets were processed using MACS2 with default parameters and -g mm option. For profiles characterized by enrichments in broad regions (H3K9me3, H3K36me3, H3K36me2, H3K36me1, H3K27ac, H3K4me1, SETDB1, MED12, RNApol II and Ser5-P), datasets were processed using MACS2 with -broad and -g mm options. BigWig files were generated using Deeptools bamCoverage with -bs 50 and -normalizeUsing RPKM options. H3K36me3, H3K36me2 and H3K36me1 bigWig files were normalized using drosophila spike-ins, which were mapped to dm6 reference genome using Bowtie2 with default parameters.