dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, read counts were normalized by contig length and transformed to relative abundances, in order to enable both comparison of the viral community structure between samples and comparison of the abundance level between contigs. Making the assumption that one viral contig corresponds to one species (which is wrong each time several contigs belonging to the same phage genome are present), Bray-Curtis dissimilatory index, as computed by the distance function from the R package phyloseq, was used to compare viral communities between samples and the effect of different variables on their structure was assessed using permutational analysis of variance as computed by the adonis2 function from the R package vegan v2. 6-2. For the dynamic dataset, differential analysis was performed on raw counts using the DESeq function implemented in the DESeq2 package v1.
ACKNOWLEDGMENTS T. P. is the recipient of a doctoral fellowship from the  French Ministry of Higher Education, Research and Innovation (MESRI)  and the  MICA  department of the  French National Research Institute for Agriculture, Food and Environment (INRAE). For the metabarcoding analysis, this work has benefited from the facilities and expertise of @ BRIDGe (Université Paris-Saclay ,  INRAE, AgroParisTech ,  GABI ,  78350 Jouy-en-Josas, France ). We are also grateful to the  INRAE MIGALE bioinformatics.
Purpose: The Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP) was established by the International Society for Gastrointestinal Hereditary Tumours and the Clinical Genome Resource, who set out to develop recommendations for the interpretation of germline APC variants underlying Familial Adenomatous Polyposis, the most frequent hereditary polyposis syndrome. Methods: Through a rigorous process of database analysis, literature review, and expert elicitation, the APC VCEP derived gene-specific modifications to the ACMG/AMP (American College of Medical Genetics and Genomics and Association for Molecular Pathology) variant classification guidelines and validated such criteria through the pilot classification of 58 variants. Results: The APC-specific criteria represented gene-and disease-informed specifications, including a quantitative approach to allele frequency thresholds, a stepwise decision tool for truncating variants, and semiquantitative evaluations of experimental and clinical data.
ClinVar currently lists 10,212 APC germline variants, 66% of which are VUS and only 8% overlap with the APC locus-specific database (LSDB) (retrieved 04/05/2022). To address this issue, expert bodies are curating actively under the governance of Clinical Genomic Resource (ClinGen)-a National Institute of Health-funded effort dedicated to building a central resource that defines the clinical relevance of genes and variants. 8  For well-defined genes and diseases, ClinGen variant curation expert panels (VCEP) submit variant classification with their accompanying evidence to ClinVar and the ClinGen Evidence Repository-the first regulatory-grade human variant database. The works of other VCEPs are summarized previously,  9, 10  which notably includes a VCEP for PTEN, another established polyposis gene leading to PTEN harmatoma tumor syndrome.
11  The International Society for Gastrointestinal Hereditary Tumours (InSiGHT) is a multidisciplinary consortium formed in 2005 by the merger of the Leeds Castle Polyposis Group and the International Collaborative Group on Hereditary Non-Polyposis Colorectal Cancer. The group established standardized variant interpretation guidelines for germline mismatch repair (MMR) variants, the underlying cause of Lynch syndrome. This led to the consistent and systematic evaluation of 2360 MMR variants independent of the ACMP/AMP framework. 12  InSiGHT also houses the world's largest curated LSDBs of variants in gastrointestinal polyposis predisposing genes on a Leiden Open Variation Database format, which currently lists 1867 different and 5628 total APC variants (retrieved 12/05/2022). Building on the existing connection between InSiGHT and ClinGen, a Hereditary Colorectal Cancer/Polyposis VCEP was convened with the aim to improve accuracy and consistency in variant interpretation in APC, the MMR genes, and other polyposis genes, including MUTYH (HGNC:7257), STK11 (HGNC:11389), POLD1 (HGNC:9175), POLE (HGNC:9177), SMAD4 (HGNC:6770), and BMPR1A (HGNC:1076).
14  Publicly available databases, predictive tools, and published and unpublished data (experimental results, clinical laboratory data, and case-level information) were acquired through systematic literature searching and information provided by committee members. The APC VCEP followed the general recommendations and feedback from the ClinGen Sequence Variant Interpretation working group  [15] [16] [17] [18] [19]  and ClinGen VCEP procedures, which were further revised by results of the pilot study. The APC-specific criteria and any subsequent updates are available at https://cspec. genome. net work/cspec/ui/svi/doc/GN089.
Well-known statistical genotypephenotype relationships include pathogenic variants 5′ of codon 168, between codons 312 and 412 (alternatively spliced part of exon 10), or 3′ of codon 1580, which tend to be associated with a milder (attenuated) colorectal phenotype (less or later onset adenomas), whereas pathogenic variants between codons 1250 to 1464 usually cause a severe, earlyonset disease  24  (Figure  1 ). To reflect this, the APC VCEP defined the applicability of PVS1 at the extremities of the gene by evaluating the 5′-most and 3′-most variants. The variant NM_000038. 6:c. 147_150del p. (Lys49AsnfsTer20) was absent from population databases (PM2_Supporting), reported in 9 index patients meeting 3 phenotype points (PS4_Moderate), and segregated with FAP in 3 meioses in 2 families (PP1)  26, [32] [33] [34]  (unpublished data).
A combination of RNA analysis and splice prediction data from SpliceAI, MaxEntScan, and VarSeak were considered in the assignment of PVS1 strength to canonical ±1/2 splice single-nucleotide variants and guanine (G) to non-G change at the last nucleotide of each coding exon (Figure  2B  and Supplemental Table  3 ). The impact on reading frame was interpreted only when the in silico predictions were concordant, and the more conservative prediction was always used unless RNA evidence was available to corroborate the prediction. Based on this, canonical ±1/2 splice variants were assigned to Lists A to E with decreasing level of evidence strength from very strong to not applicable. G to non-G last nucleotide changes were also evaluated and weighed with 1 level downgrade in strength from the corresponding canonical sites if the splicing predictions were up to the same standards.
Observed in healthy adult individual (BS2) In APC VCEP's terms, a healthy individual must be ≥50 years old, and either (1) had no colorectal cancer/polyposisrelated indication for genetic testing or (2) had less than 5 colorectal adenomas detected in a colonoscopy but no other relevant phenotypic features (for details regarding this definition, see Supplemental Table  3 ). A variant heterozygote reported in a control, non-cancer, normal, or unaffected population, but lacking the above information, was counted as half a healthy individual points, thus requiring more individuals to satisfy BS2. BS2 was met when a variant was observed with ≥10 healthy individual points and BS2_Supporting with ≥3 healthy individual points. The use of the non-cancer data set of gnomAD was not considered a valid source of healthy heterozygous adult individuals because of the lack of phenotypic information (eg, insidious gastrointestinal polyps) and to avoid evidence double counting with BA1 or BS1.
Validation through pilot variant classification Representative APC variants (n = 58) were selected to encompass a range of variant types, including 25 presumed missense, 7 presumed synonymous, 8 truncating (nonsense/ frameshift), 1 stop loss, 4 splice site, 7 intronic, 3 in-frame deletion/insertion variants, and 3 large deletions or duplications, including a promoter 1B deletion. Collectively, all applicable APC-specific ACMG/AMP codes were utilized in the classification of the pilot variants except BS4 (lack of segregation in affected relatives). The most frequently applied code was PM2_supporting (n = 39), which showed the rarity of APC variants in general. The gnomAD v2. 1. 1. non-cancer population database contained 17 of the pilot variants, for which either BA1 (n = 9) or BS1 (n = 8) was applied.
A total of 8 institutions submitted clinical data for 50 variants, which aided in the classification of 39 variants through the application of PS4, BS2, PS2, PM6, and/or PP1. Experimental evidence was validated with corresponding codes (PS3/BS3) applied for 16 variants. PVS1 was used in 11, PS1 in 1, PM5 in 5, PP3 in 4, BP4 in 9, and BP7 in 7 variants. A list of all pilot variants, their assertions by ClinVar submitters, and their APC VCEP-approved classifications by the APC rule specifications with evidence codes applied are listed in Supplemental Table  2. The classification of pilot variants by APC-specific ACMG/AMP criteria were compared with their respective classification on ClinVar, which, depending on the number and quality of submissions, could be considered as a standard for validation of gene-specific rules.
As a medically actionable gene, the classification of an APC variant into either the benign or pathogenic category has important and long-lasting clinical implications. The detailed specifications for the evidence in the APC-specific criteria therefore serve as a quality assurance tool and reduces the risk of false-positive interpretation. At the same time, the APC VCEP acknowledged that certain requirements in the gene-specific criteria were quite restrictive and high-quality data might be difficult to obtain. To not dismiss evidence lightly and avoid misinterpretation of variants with clinical consequences, the VCEP also allowed strength downgrade for evidence wherever possible to accommodate for the design of cohort studies, the data structure of reference population databases, and the set-up in routine diagnostic and screening context.
To lay the groundwork for perspective expert panel approval for the substantial number of VUS and conflicting APC variants submitted to ClinVar, the next step will be the design of a streamlined algorithm to systematically and comprehensively evaluate a variant and to implement this strategy in a large-scale classification approach, including the use of variant prioritization features of the ClinGen Variant Curation Interface. Prioritized lists of promising causative APC variants that remain at VUS will be subjected to a data mining and molecular-driven workup to collect further clinical and experimental evidence. To resolve the interpretative challenges of variants in the post-genomic era, an APC subcommittee of the InSiGHT and ClinGen Hereditary Colorectal Cancer/Polyposis VCEP was constituted, and APC-specific variant classification criteria were developed.
Acknowledgments This work was supported (not financially) by the  European Reference Network on Genetic Tumour Risk Syndromes  ( ERN GENTURIS )-Project ID No  739547. ERN GEN-TURIS  is partly co-funded by the  European Union  within the framework of the  Third Health Programme  " ERN-2016-Framework Partnership  Agreement  2017-2021. " We also thank  Mireia Men éndez  for her support in interpretation of the functional assays.
Data Availability Data are available upon request. All variants reviewed and reclassified by the ClinGen InSiGHT Variant Curation Expert Panel in this study have been submitted to the ClinVar Database (https://www. ncbi. nlm. nih. gov/clinvar/). The detailed evidence used for the classification of these variants is available in the ClinGen Evidence Repository (https:// erepo. clinicalgenome. org/evrepo/). These data may also become available upon a data transfer agreement approved by the local ethics committee and can be obtained after contacting the corresponding author (X. Y. ) upon request.
Cohort B: The study protocol was approved by the Research Ethics Board of Aix-Marseille University (NCT01277068 and NCT02332434). Cohort B involved 13 obese individuals of both sexes (77% women), mean age 37. 6 years [SD, 9. 1], undergoing sleeve gastrectomy at the General and Endocrine Surgery Department of La Conception Hospital (Marseille, France), as described previously  [17]. The clinical evaluation and laboratory tests were conducted at the Nutrition Department of La Conception Hospital. Patients were examined one month before (baseline) and one year after gastric sleeve surgery to reach steady weight loss. Each visit included measurement of anthropometric parameters and blood sampling after an overnight fast  [17].
Ethics statement Plasma samples of patients were obtained from the diagnostic archive of the Radboud University Medical Center, Translational Metabolic Laboratory, expertise center on Congenital Disorders of Glycosylation and used in accordance with Helsinki's Declaration under local ethical approval (nr 2019-5591). Plasma samples of 40 healthy controls were received from the Sanquin blood bank (Nijmegen, Netherlands) according to their protocols of informed consent. An overview of all samples with associated metadata is available from the supplementary data file.
Intact transferrin mass spectrometry Glycosylation patterns of intact transferrin that are used as a known biochemical for CDG were analysed by the established CDG diagnostics ESI-MS method  [28]. Briefly, rabbit antihuman transferrin antibody (DAKO cat. no. A0061) coupled to Nhydroxysuccinimidyl (NHS)-activated Sepharose (GE Healthcare) was used to immunocapture transferrin from plasma samples. Neutralized samples were analysed on a microfluidic 6540 HPLCchip-QTOF instrument (Agilent Technologies) using a reversed phase C8 stationary phase. Data analysis was performed using Agilent Mass Hunter Qualitative Analysis Software B. 04. 00 in combi-nation with the Agilent BioConfirm Software for charge deconvolution using the maximum entropy algorithm.
Database searches and consensus feature map generation Raw data were processed using DataAnalysis 4. 2 for postacquisition internal mass calibration (sodium-acetate clusters) and subsequent export of MS/MS data to MGF file format. Mass calibrated raw data files were converted to mzML using compassXport (Bruker Daltonics) and subsequently processed by OpenMS (Knime v3. 2. 1) to generate a consensus feature map that contains intensities and associated metadata for aligned features from all analysis files. The OpenMS workflow can be downloaded from pro-teomeXchange (PXD034214). The MGF-files were processed in ProteinScape TM 3. 1 (Bruker Daltonics) to classify glycopeptide fragmentation spectra using sugar mass distance pattern matching and to calculate peptide-and glycan-moiety masses, respectively.
Classified glycopeptide fragmentation MS/MS spectra were searched against the Carbbank glycan structure database (date: 2016-08-05) using GlycoQuest (ProteinScape TM 3. 1, Bruker Daltonics) and following acceptance criteria: score > 20, >10% spectral intensity coverage and > 10% theoretical fragment ion coverage (B-,Y-and internal-fragment ions). An in-house developed Perl script was used to remove oxonium ions (B-, and i-ions) based on 25% detection frequency and glycan-peptide fragment ions (Y-ions) with mass above the calculated non-glycosylated peptide moiety from CE stepping fragmentation spectra and searched against the human SwissProt protein sequence database (date: 2016-08-05) by MASCOT (v2. 5. 1. 1, Matrix science) with the following settings: tryptic cleavage specificity, precursor mass tolerance of 20.
Data pre-processing and integration In-house developed MATLAB R2014b functions and Perl scripts were used to map the identification data onto features of the consensus feature map using a rectangular bucketing approach and to perform subsequent data pre-processing and downstream analyses. All functions and scripts used in this work are available from proteomeXchange (PXD034214). Briefly, in pre-processing relevant features within specified retention time and mass-to-charge ranges (10-67 min, 600-3700 m/z) and with detection frequency of at least 75% in samples within any sample group were subjected to quantile intensity normalization prior to missing value imputation for subsequent machine learning approaches. Identified nonredundant peptide-and glycan-moieties from individual search results were first subjected to retention time alignment by LOESS regression and subsequently linked to consensus features in rectangular buckets (1 min retention time width Â 20 ppm mass width).
Bioinformatics analysis Site-specific glycosylation profiles were generated as glycoform fractions by dividing the glycan intensity by the total sum of glycan intensities for each respective peptide sequence. Composition Based glycan Classification (CBC) was performed using computational logic described as pseudo algorithm and available from the proteomeXchange deposition. Peptide sequences lacking the consensus [N-X-S/T] N-glycosylation sequon and peptide-moieties with 1 or more missed cleavages were removed from the dataset. Baseline and transferrin glycopeptide-based glycome inference was performed by summing intensities from identical glycoform features and expressed as relative glycan fractions. Glycome inference from intact transferrin -MS macro-heterogeneity data was performed by summing the respective glycan intensity for each protein glycoform after correction for the number of occupied Nglyosylation site positions and expressed as relative glycan fractions.
Chord diagrams for glycoproteome visualizations were prepared using the circlize package  [29]  in R programming language v4. 1. 1  [30]. To generate a site-specific CBC similarity network we first constructed a Pearson's correlation matrix from the CBC distributions of all peptide-moieties after which correlations between glycosylation sites with r ! 0. 8 were used as edges for peptidemoiety nodes in the MATLAB biograph function for network generation.
GA parameters used were population size: 200, selection function: tournament, uniform mutation: 0. 1, crossover heuristic: 0. 2, elite count: 2, stall generation limit: 10. Each RF classification was performed using 100 trees with a leave-one-out cross validation for prediction and subsequent performance calculation. Principle Component Analysis (PCA) and mahalanobis distance measures were performed using standard MATLAB R2014b functions. Nonlinear dimensional reduction using t-distributed Stochastic Neighbor Embedding (t-SNE) was performed using the non-parametric t-SNE MATLAB function from van der Maaten et al  [32] with 6 initial dimensions and 10 perplexity.
Towards a clinically applicable strategy for plasma glycoproteomics In view of the intended clinical application, we developed a holistic glycopeptide profiling workflow that required practical sample amounts (10 ll of blood plasma) whilst achieving reasonable sample throughput for analysis (20 samples in 24 h). Intact tryptic glycopeptides were enriched using Sepharose CL-4B  [33]  in 96-well plates and analysed by C18 reversed phase LC-MS/MS without any further fractionation (Fig. 1a ). Fragmentation experiments were performed at ''low" and ''high" collision energies to generate structurally informative fragments for the glycan-and peptide-moiety, respectively  [26]. Optimal collision energy settings were selected using glycan-and peptide-database search results based on the number of identifications and identification scores.
For pre-processing of acquired data we used available software to extract LC-MS feature information (OpenMS  [34] ) or to identify glycan compositions (ProteinScape TM -GlycoQuest  [35] ) and peptide sequences (MASCOT  [36] ). MATLAB scripts were developed to integrate the output of individual software tools by mapping peptide and glycan identities onto their respective LC-MS features in the consensus map. This strategy supported the use of all quantified LC-MS features in subsequent statistical and chemometric analyses irrespective of identification status and maximized feature annotations by integrating identification results from all analyses. Robust analytical performance and sample stability are prerequisites for successful clinical application of plasma glycoproteomics technology. A single control plasma sample was used to determine the intra-and inter-essay reproducibility via five replicate injections of the same sample preparation and analysis of five independent glycopeptide preparations of the same sample.
Glycopeptide identification Plasma glycopeptide identification by LC-MS/MS is known to be challenging due to low electrospray ionization efficiency, glycoform signal dilution and poor fragmentation of the peptide moiety in collision induced dissociation experiments  [26]. We implemented acetonitrile-enriched nitrogen source gas  [37]  to significantly increase ionization efficiency of intact glycopeptides as compared to other organic solvents that we tested (supplementary Fig. 1 ), thereby shifting the distribution of precursor ions towards higher charge states for enhanced peptide-moiety fragmentation. Application of low collision energy conditions produced rich glycan fragmentation spectra for subsequent glycan database searches. The relatively low intensity of peptide moiety b-and y-fragment ions in MS/MS spectra under high collision energy conditions complicated protein database searches.
We tackled this by MS/MS spectrum pre-processing (supplementary Fig. 2 ) by removing most of the glycan B-, Y-and internal fragment ions prior to database searching. As a result, MASCOT identification scores significantly improved by 25% and raised the total number of peptide-spectrum matches by 42% to yield 57% more identified unique peptide-moieties (supplementary Fig. 2 ). To maximize the number of annotated glycopeptide features in the consensus feature map, we then applied the plasma glycoproteomics method to a cohort of 200 individuals that consisted of 40   [33]. Glycopeptide mixtures were analysed by C18 reversed phase liquid chromatography with online tandem mass spectrometry using low and high collision energies (CE) for glycan-and peptide-moiety fragmentation experiments, respectively  [26].
(b) Data processing workflow. Quantitative information was extracted from data files as feature maps and subsequently combined into a consensus feature map in OpenMS software. ProteinScape TM 3. 1 was used to perform MS/MS glycopeptide spectrum searches and classified MS/MS spectra were searched against the CarbBank glycan database using GlycoQuest or against the Swiss-prot human protein sequence database using MASCOT. In-house developed MATLAB scripts were used to map identified glycan-and peptide-moieties onto the consensus feature map for subsequent analyses. healthy donors, 12 disease controls with normal transferrin glycosylation, 30 patients with unknown genetic defect and abnormal transferrin glycosylation and 118 patients spanning 35 genetically resolved congenital disorders of glycosylation (CDG).
The broad variation in glycan structures in these CDG patients was key to identify disease-specific or otherwise low-abundant glycoforms in control samples with the aid of increased signals from accumulated glycoforms. In total, 267. 394 glycan-spectrum matches were obtained through GlycoQuest searches (191 unique glycan compositions) that could be mapped onto 7. 229 features of the LC-MS consensus feature map which contained merged features from all individual samples  [38]. The qualitative glycome representation of detected glycan species, agglomerated in glycan traits, is shown in Fig. 2b. For peptide moieties, 5. 988 peptide-spectrum matches were obtained for the full CDG cohort dataset which could be mapped onto 1. 430 LC-MS features in the consensus feature map. Peptide sequences that resulted from missed tryptic cleavages or that lacked a predicted or known N-glycosylation sequon were removed from the dataset to yield a total of 58 unique peptide sequences identified from 34 proteins for which at least one glycoform was identified.