dataesr/scientific-paragraphs-categorization huggingface dataset:
In some fish species and the paralogs p63/p73, a lysine is found at this position. The Arg290 side chain does not interact with the rest of the DBD and is generally disordered in DBD crystal structures, as for example, in PDB entries 2XWR  [27]  and 3KZ8  [28]. This residue is not conserved across vertebrate p53; a histidine is found at this position in beluga whale and feline p53. Variant p. T312S was located in the adjacent flexible linker between the DBD and the tetramerization domain. Only one variant, p. E339K, was located in the tetramerization domain itself. Glu339 sits on the surface of the tetramer, facing the solvent, and is not involved in any intersubunit contacts stabilizing the tetramer (Fig. 5C ). Therefore, no major effects on the oligomerization equilibrium of p53 were expected for the lysine variant.
Also, they include many parameters that are not suitable for cancer. Furthermore, the prediction software, as well as population criteria used for an analysis, can influence the various scores. As TP53 variants may be somatic or germline, the ACMG guidelines must be adapted to ensure accurate classification. Allele frequency in the human population is a strong criterion to define a variant as benign. A minor allele frequency (MAF) threshold of 0. 001 and minimal allele count (AC) of five in the population are currently used to define the stand-alone BA1 criterion, which is sufficient to classify a variant as benign. In the present study, using the three aggregated datasets, only rs1042522 (p. P72R) could be ranked as BA1 (Table  1  and Fig. 6A ). One of the main issues with an aggregated database including multiple ethnic populations is the dilution of the signals for variants specific to an ethnic group.
Furthermore, these databases are skewed toward European ancestry (more than 50% for gnomAD), an aspect that can bias analyses. Using the aforementioned criteria, we analyzed the TP53 variants using the eight population subsets from gnomAD and identified only two new BA1 variants, i. e. , p. V31I in the Asian population (AF = 0. 003 and AC = 65) and p. Y107H in the African population (AF = 0. 00116 and AC = 23). The analysis of the 11 national datasets was far more informative, leading finally to the identification of nine TP53 variants that should be defined as benign using ACMG criteria (Tables 1, S5 and Figs. 6A, S11 ). Among those nine variants were the five EAS-specific SNP variants described above. Similarly, the analysis for the strong BS1 criterion (MAF ≥ 0. 0003 and AC ≥ 5) with the whole-gnomAD database did not uncover new variants, whereas that of the various datasets led to BS1 classification for eight variants (Tables 1, S5 and Figs.
The TP53 gene is a paradigm for such analyses. Indeed, the accurate diagnosis of somatic mutations in it is mandatory in multiple clinical settings, for example in chronic lymphocytic leukemia, where it is used to improve patient stratification and optimize therapeutic decisions  [2]. Furthermore, the diagnosis of germline TP53 mutations for genetic counseling is proving pertinent in such pathologies as hereditary breast and ovarian cancer syndrome and pediatric cancer  [1, 36]. Recently, the ACMG-Association for Molecular Pathology provided a systematic method for the interpretation of sequence variants for genetic disorders  [31]. Unfortunately, a high fraction of variants is relegated to uncertain significance (VUS), often due to the rarity and conflictual nature of results in existing databases and the primary literature.
Several recent publications sought to improve the classification of TP53 variants but their results were heterogeneous and thus a source of discord. Historically, two exonic missense TP53 SNPs have been identified and defined as benign constitutional variants  [37]. Somatic TP53 gene mutations have been described in more than 100,000 patients. However, in most of these cases, matched normal DNA was not available and, thus, the somatic origin of the mutation was determined by consulting reference databases. It is therefore highly likely that some of those variants were infrequent constitutional variants mistakenly identified as somatic mutations. In the present study, using a combination of in silico, in vitro, structural, and genetic approaches, we addressed a two-tiered question: (i) is it possible to identify novel and infrequent TP53 SNPs in the human population and (ii) what is the potential pathogenicity of these variants? The use of multiple independent population databases has proven very helpful for the selection of novel potential SNPs.
Although gnomAD is an outstanding resource for population analyses, the addition of novel datasets of individuals from different ethnic groups has led to a more accurate selection of potential SNPs  [38]. In silico predictive software enables the assessment of the effects of amino acid substitutions on protein structure or function with, in principle, no imperative need for functional studies. However, in silico and functional approaches each have their own strengths and weaknesses. Depending on the algorithm, the performance of in silico approaches will vary based on the specific gene and protein being considered. As all algorithms focus on protein function based on the homology of sequences and/or the physical properties of amino acids, confusion has arisen between deleteriousness and pathogenicity, which should be considered as two different features.
Great caution has been advised for the use of these algorithms, including for TP53  [39] [40] [41]. Concerning this latter, mutations in the mdm2 binding domain, localized in the amino-terminus of the protein, are predicted to be deleterious and consequently classified as pathogenic. In fact, these variants are known to impair cell growth and ultimately lead to cell death, which is the opposite of what would be expected for cell transformation. This aspect is supported by the total absence of cancer-associated mutation in the two highly conserved codons 19 and 23 of this TP53 protein domain  [42]. One major advantage for TP53 is the availability of three unbiased large-scale functional studies for all potent TP53 missense variants  [16] [17] [18]. A correlation analysis showed excellent agreement between the three datasets despite their different readouts  [43].
Although predicted as "nonfunctional" by several algorithms, variants such as p. N235S or p. E11Q do not display loss of activity. This confusion has led to a controversial classification of several TP53 variants. p. N235S has been defined as pathogenic, likely pathogenic or VUS in different studies  [32, [44] [45] [46]. It remains defined as pathogenic in the COSMIC database (version 92) despite the fact that it is considered as likely benign in Clinvar since spring 2020 and was found to be thus in the present study as well. For TP53, we strongly advocate for the use of functional studies to assess TP53 variant deleteriousness. The consequences of TP53 mutation on protein function and their repercussions on the various pathways regulated by TP53 are highly heterogeneous. Although a few variants can be considered as amorph with a total loss of function, the complex structure of the TP53 protein is known to give rise to variants with multiple consequences with hypomorph, neomorph or antimorph properties  [47] [48] [49].
Nonetheless, their high frequency in the human population and low frequency in cancer mutation databases suggest that any deleterious clinical impacts will be very low. Using different functional assays, we showed that a subset of 21 germline TP53 variants found recurrently in multiple population datasets from different ethnic groups was indistinguishable from wild-type TP53. Through the analysis of multiple national datasets, we were able to definitively validate nine of the 21 variants as BA1, i. e. , the ACMG stand-alone criterion for benignity. Seven other variants met the ACMG criterion BS1 + BS3, also indicating benignity. Thus, we identified 16 variants that may only be defined as natural polymorphisms. Among those variants, we identified for the first time five variants that were specific to the Asian population (present only at very low frequency in other populations).
Our analysis of the UMD_TP53 database confirmed the Asian origin of the five variants and furthermore showed that they were mistakenly defined as somatic pathogenic mutations. We also identified another TP53 variant specific to the Indian population. Our analysis emphasizes the need to exercise great caution when using population datasets to check patient cohorts. Matching the ethnicity of the patient or the cohort to the reference datasets appears essential for increasing the accuracy of the analysis  [55]. Also, supplementing these datasets with data from normal populations and from all geographical areas is necessary to minimize variant misclassification. For example, the Asian variant rs201753350 (p. V31I), classified as benign (BA1) in our analysis using multiple criteria, had been classified as pathogenic in a family with gastric cancer in the Japanese population  [56].
Such "false positive" results could have detrimental consequences for patients and families, who may end up experiencing anxiety, undergoing unnecessary examinations, etc. We cannot exclude that a number of the variants described in the present study may be associated with specific hereditary traits associated or not with cancer, or with other genetic characteristics. To illustrate this point, variant rs1042522 (p. P72R) does not impair TP53 activity but whether it is associated with an increased risk of cancer remains highly controversial  [23]. Similarly, variant rs1800371 (p. P47S), restricted to the African population, has been shown to be associated with an increased risk of breast cancer  [57]. Most of the variants described in set 21 are described as pathogenic or likely pathogenic in multiple databases and pipelines for the assessment of pathogenicity.
To improve the accuracy of TP53 mutation diagnostics, they should be updated with the present data. The 2020 release of the UMD_TP53 database as well as the web application Seshat have been updated with these novel data. This analysis was made possible by the numerous functional and structural analyses available for TP53. Unfortunately, the quantity of research for other cancer genes is much more limited. Nevertheless, the availability of large, independent datasets representing ethnic populations should be sufficient to identify novel benign variants and emphasize genetic heterogeneity. For an accurate assessment of somatic and germline variants, human genome reference databases used in analytical pipelines must include accurate ethnicity information to ensure that genetic analyses provide the most pertinent information possible for patients.
Population datasets TP53 variants from a range of population databases were downloaded from their respective portals (Table  S1 ). The origin of each dataset was carefully checked to avoid redundancies. Because a part of the 1000 Genomes Project was included in gnomAD, the data of the former were not used in the present study. To ensure accurate comparisons between the various datasets, data from all databases were obtained using the genomic coordinates of the entire TP53 gene as defined by RefSeq. In a first step, minimal genomic data, such as genomic coordinates and genetic events, were extracted from each dataset to define the correct annotation according to HGVS recommendations. In a second step, variant annotation was validated by using the Name Checker tool developed by Mutalyzer (https://mutalyzer.
nl/ ). This latter handles all types of TP53 gene variations, such as substitutions, insertions, duplications, deletions, and more complex insertion/deletions  [58]. The current version (2. 0. 26) of Mutalyzer uses the stable NCBI sequence NG_017013. 2 as reference for TP53. This is an essential issue for avoiding problems due to the use of multiple genome references (NCBI Build 36. 1/hg18, Genome Reference Consortium GRCh37/hg19 or Genome Reference Consortium GRCh38/hg38) by the various NGS pipelines as well as noncompliant nomenclature. To minimize database contamination by potential carriers of germline TP53 variants, the non-cancer version of gnomAD was used in the present analysis. Taken together, exonic TP53 variants issued from gnomAD correspond to 56% of the variants issued from the 14 cohorts.
Cancer-associated TP53 variants were downloaded from the UMD_TP53 database (Released 2017), which includes 80,402 mutations (6870 different TP53 variants) identified in tumors, in cell lines (somatic mutations) or in patients with hereditary cancers (germline mutations)  [37]. This release includes mutations identified by conventional sequencing or by NGS (Table  S1 ). The sequence nomenclature used for TP53 variants in this work is in accordance with the Human Genome Variation Society's guidelines using the NM_000546. 5 transcript sequence and the full-length protein NP_000537. 3 [59]. We previously defined and validated a specific set of pathogenic TP53 variants called the "CSD"  [15]. Briefly, TP53 variants were extracted from four different nonoverlapping datasets on various types of cancer.
The Sanger dataset includes 4299 variants extracted from the latest version of the UMD_TP53 mutation database excluding all NGS studies (Leroy et al. [37] ). The other three sets of TP53 variants were compiled from large independent cancer sequencing projects: TCGA  [60] , ICGC  [61] , and MSK-IMPACT  [62]. Each of these studies described more than 2000 different TP53 variants occurring at various frequencies in the coding regions or splice sites (+2/-2) of TP53. We created the CSD by combining these four datasets to define a core of 471 recurrent TP53 variants found at least once in each database  [15]. The four datasets resulted from independent studies using different patients and different methodologies. We showed that shared variants were true recurrent pathogenic variants  [15].
Functional analysis from large-scale mutagenesis data The UMD_TP53 database includes three sets of functional data for TP53 variants. The first set was described in detail in a previous report  [16, 63]. Briefly, haploid yeast transformants containing 2314 TP53 variants and a green fluorescent protein reporter plasmid were constructed. TP53 activity was tested by measuring the fluorescent intensity of green fluorescent protein that is controlled by eight different promoter sequences regulated by the p53 protein after 3 days of growth at 37 °C. The second set of functional data integrated in the UMD_TP53 database corresponds to the analysis of 5300 TP53 variants performed by Kotler et al. [17]. The growth activity of all variants localized in the DBD of TP53 was assessed in H1299 cells. The third set corresponds to the study by Giacomelli et al.
Selection of TP53 variants for in silico and in vitro analysis A subset of 27 TP53 variants (set 27) were chosen for in silico analyses based on their occurrence in multiple datasets (more than 2). We also added the five Asian-specific variants and eight of the ten outlier germline variants found in the UMD_TP53 database. The variant p. T125R, found at high frequency as a germline variant in the database, was excluded because missense and synonymous mutations in this codon close to a splice site are known to impair TP53 splicing  [64, 65]. The pathogenic variant p. R337H (known as the Brazilian mutation) associated with adrenocortical carcinoma was also excluded  [66]. Set 21, used for in vitro analyses, is derived from set 27 using more stringent criteria. It includes variants found in multiple datasets (more than 4) at frequencies higher than 0.
0005. Asian and outlier germline variants were retained but variants p. R248Q, p. R283H, and p. R156H found, respectively, in five, four, and four datasets were not included as they are well-known pathogenic variants included in the CSD. Set 21 thus included 19 novel TP53 variants as well as rs1042522 (p. P72R) and rs1800371 (p. P47S).
Molecular modeling and stability predictions Models of p53 DBD mutants were generated using the SWISS-MODEL server  [67] , with PDB entry 2XWR as a template  [27]. Differences in the melting temperatures of the mutant proteins compared with the wild-type protein were predicted using the HoTMuSiC server  [68]  based on PDB entry 2XWR and a wild-type DBD melting temperature of 46 °C. Variant effect prediction dbNSFP v3. 5 was downloaded from the Jpopgen website (https://sites. google. com/site/jpopgen/dbNSFP). This database compiles prediction or conservation scores from multiple prediction algorithms  [12]. Prediction scores from each algorithm were available as normalized data from 0 (less deleterious) to 1 (most deleterious). Five other predictors not included in dbNSFP were also used: Revel  [69] , PON-P2  [70] , MutPred2  [71] , Mut_Pred  [72] , and Envision  [73].
Acknowledgements This study was supported by the  Radiumhemmets Forskningsfonder  (to TS), the  Czech Science Foundation  ( GA19-15737S ) (to SP), and the  German Research Foundation (DFG)  grant  JO 1473/1-1  (to ACJ). We would also like to thank the core facility at  Novum, BEA, Bioinformatics  and  Expression Analysis , which is supported by the board of research at the  Karolinska Institute  and the research committee at the Karolinska hospital. We thank  Vladimir Bykov  ( Bioclinicum, Karolinska Institute ) for help with the FACS analysis,  Patrick Muller  ( BEA, Karolinska Institute ) for help in QPCR analyses, and  Alan Sauvat  ( INSERM U1138 ,  Centre de Recherche des Cordeliers ) for data analyses.
The rendering of glycan drawing and symbol representations motivated the development of several computer applications using a standardised notation. The earliest glycan editors allowed manual drawing similar to ChemDraw or used input files with glycan sequence KCF (KEGG Chemical Function)  [9]  in text format for similarity search against other structures deposited in the databases. Later developments supported the construction and representation of glycan structures in symbolic form by computational tools like GlycanBuilder  [10]. Since then, several advancements have been made to allow the user to both draw glycans manually or by importing and exporting the structure files in different text formats  [11]. Along the same line, the development of various other applications allowed the users to sketch 2D-glycan structures by dragging and dropping monosaccharides to canvas to generate 3D structures for further usages.
These depictions comply with protein data bank (PDB)  [12]  format, or in the form of images  [13, 14]. Besides, these tools for representing glycans in 2D and 3D shape  [15]  allowed the integration of glycans into protein structures or complexes. The tools developed in the last few years have automated the sketching of glycans and glycopeptides, allowing rapid display of structures using IUPAC format  [16]  as input. This article explores and illustrates the concepts of "sketching", "building" and "viewing" glycans (Figure  1 ). It provides a descriptive analysis of the tools available for such activities, which can be useful for researchers looking for a ready-to-use simple program for sketching, building and 3D structure analysis of glycans and glycoconjugates. The scope of this work is relevant to N-and O-linked glycans, glycolipids, proteoglycans and glycosaminoglycans, lipopolysaccharides, plant, algal and bacterial polysaccharides.
Review Methods To facilitate glycoscience research, we have identified the tools and databases that are freely available on the internet and are regularly updated and improved  [1]. The variety and complexity of glycan structures make their interpretation challenging. Consequently, in the past few years, several sketching, building and visualisation tools have been developed to depict better and understand the complex glycan structures. In this study, the freely available tools have been visited (April 2020) and analysed to highlight their core features but also explore their unique advancements to facilitate glycan research. Each of the computational tools was inspected for general features related to sketching, representing and model building, all of which could be further used as input for translation into other formats, search from glycan databases or complex calculations such as molecular simulations.
Several tools feature an interactive interface which allows for manual editing of the structures. Examples of such tools are DrawRINGS  [17] , KegDraw  [18] , Glycano (available at http://glycano. cs. uct. ac. za/), GlycoEditor  [19] , GlycanBuilder  [20] , etc. These tools (except KegDraw) are provided with the list of CFG symbols to freely build glycan structures using the mouse on the canvas. In addition to manual sketching, some of these tools also can import text formats including IUPAC-condensed, GlycoCT and KEGG Chemical Function (KCF) format to display the glycan structures. Some applications also facilitate glycan search in various databases. Another category of tools included in this study involved glycan viewers which can only depict structures using the IUPAC three letter code or IUPAC-condensed nomenclature as input.
Energy minimisation methods can further refine the models. These models are essential for structure-based studies and complex calculations like Molecular Dynamics simulations. Therefore, the accurate model building requires the use of reliable databases to generate atomic coordinates and topology to provide an acceptable model. Some of the computational tools usually contain atom coordinates of generally used monosaccharides (as templates) and also use libraries of bond and angle parameters from various force fields dedicated for carbohydrates. The accurately predicted oligosaccharide conformations are good starting points for further investigations. Of particular interest are the evaluations of the dynamics of glycans and their interactions with proteins which is a most significant concern in glycoscience.
The proposed formats provide rules to extract the structure of the branches and create a unique sequence for the carbohydrate. The most commonly used formats are IUPAC  [16] , GlycoCT  [29] , KCF  [9] , and WURCS  [30]. The sketching of carbohydrates using computational tools generally requires the textual input and output in at least one of these formats (Figure  3 ). An alternate input method involves manual sketching of 2D glycan structures by dragging and dropping monosaccharide symbols on canvas (with or without grids) to connect them further. This method makes the sketching tools more friendly and interactive as it does not require large text code as input. Both input methods are compliant to the Symbol Nomenclature for Glycans (SNFG). Another symbolic representation that could clearly distinguish monosaccharides in monochrome colours is the Oxford notation  [5].
Glycan sketchers GlyCosmos is a web portal that integrates resources linking glycosciences with life sciences. Besides elements such as "SugarDrawer" and GlyTouCan (carbohydrate database), the platform GlyCosmos assembles data resources ranging from glycoscience standard ontologies to pathologies associated with glycans. GlyCosmos is recognized as the official portal of the Japanese Society for Carbohydrate Research and provides information about genes, proteins, lipids, pathways and diseases. GlyTouCan (Figure  5 ) is a repository for glycans which is freely available for the registry of glycan structures. The repository can register structures ranging from monosaccharide compositions to fully defined structures of glycans. It assigns a unique accession number to any glycan to identify its structure and even allows to know its ID number in other databases.