dataesr/scientific-paragraphs-categorization huggingface dataset:
Upon the last composition step, a final abstraction and minimization step is undertaken, where only key components are kept visible thus obtaining a minimal description of the dynamics of the whole composition. Given the LOTOS NT specifications of the module and of the integration functions, the synchronization (composition) of the whole is specified by way of synchronization vectors  (Lang, 2005). A high-level language provided by CADP called SVL  (Garavel and Lang, 2001 ) is used to specify the generation of the LTSs for each individual LRM, their preliminary minimization step, subsequent synchronization steps, as well as the final abstraction and minimization operations. The intermediate composition steps are automatically produced by CADP. The SVL script as well as the EXP  (Lang, 2005)  file, which specifies the synchronization vectors, are automatically generated from a symbolic representation of the LRM exported from GINsim and processed using Perl scripts.
They correspond to the expected wild-type pattern WE in addition to the TT and EW patterns  (Sa´nchez et al. , 2008). We also consider a reduced (intra-cellular) regulatory graph with 9 components and 31 regulatory interactions. It has been obtained by applying the reduction method available in GINsim and described in  Naldi et al. (2010). Note that with GINsim, it was impossible to construct the STG for the full model, and for the reduced model, the resulting LTS structure is much smaller and thus more amenable to further analysis (see Fig. 2C  and D ). In  Sa´nchez et al. (2008) , the construction of the STG was interrupted as soon as the WT and TT stable states were reached. To investigate the impact of the composition order, we now consider six instances of a further reduced SP module (with three proper components: Wg, En, Hh).
These modules are organized along a line, each having two neighbours, except for the two extreme ones. For each instance i (i ¼ 1,. , 6), our method generates three LTSs: (i) M i , LTS of the i-th module; (ii) H i ðHhÞ, LTS of the integration function of the input Hh in the i-th module; (iii) H i ðWgÞ, LTS of the integration function of the input Wg in the i-th module. Table  1  illustrates the impact of the composition order. It first includes the composition steps as performed by the smart reduction implemented in CADP. The intermediate LTSs grow very fast. Indeed, we can observe that composition operations are performed over LTSs that have no synchronization restrictions [because they are not related, such as, e. g. H 2 (Wg) and H 5 (Wg)]. In this specific case, the smart reduction heuristic performs poorly.
Either stable state can be univocally identified by the value of e. g. Delta, hence there is a single key component in each module. In the case of DN7 as depicted in Figure  3 , if all modules start with both Delta and Notch inactive, then the six stable expression patterns are reachable. The pattern of Delta expression that ultimately emerges depends on which cells express Delta sooner. The STG generated with GINsim shows that from this initial condition, almost the entire state space is explored (there are 16 024 reachable states for a total state space of size 16 384). The minimized LTS (retaining only Delta as a visible component) contains 2290 states, which is a significant reduction (it is then possible to check on this reduced LTS that the six stable expression patterns are reachable).
We rely on GINsim for the definition of the LRMs and on CADP for the composition, abstraction and minimization operations. The procedure has been applied to the Segment-Polarity and the Delta-Notch modules, showing that huge reductions can be obtained. With the study of the SP module, we could discuss the problem of the composition order. At present, CADP provides the smart reduction heuristic, which defines a built-in composition order, and it also offers the proficient user the possibility of manually defining a specific composition order. In general, it is not trivial to determine the optimal composition order. However, considering the application we are dealing with, we propose an alternative heuristic that should lead to reasonable performances. It consists in composing the LTSs of individual LRMs with the integration functions of their inputs and progressively proceeding according to the specified neighbourhood relation.
Lynch syndrome is an autosomal dominant disease caused by germ line heterozygous mutations mainly involving the MSH2, MLH1 and MSH6 genes that belong to the DNA MisMatch Repair (MMR) genes family. The French network counting the 16 licensed laboratories involved in Lynch syndrome genetic testing developed three locus-specific databases with the UMD Õ software (www. umd. be/MLH1/, www. umd. be/MSH2/ and www. umd. be/MSH6/) that presently contain a total of 7047 sequence variations including 707 distinct variations of a priori unknown functional significance (VUS) that were identified through complete mutation screening or targeted predictive testing. Mutation carriers are at high risk for developing early-onset colorectal and endometrial adenocarcinomas. Consensus clinical guidelines have been proposed, allowing the efficient detection of curable lesions.
The major challenge of genetic testing is to reliably classify the genomic variations in those patients who seek genetic counseling. Combining the interactive tools of the software, the relevant published data and mainly original information produced by the French MisMatch Repair network, the UMD-MLH1/MSH2/ MSH6 databases provide interpretation data for the 707 VUS that were classified according to the IARC 5-Class system. These public databases are regularly updated to improve the classification of all registered VUS, exploring their role in cancer pre-disposition based on structural and functional approaches.
To refine the functional consequence of such variations, several criteria are generally used. In silico predictions first give an orientation toward a more likely neutral or causal effect. In theses studies, degree of conservation at the variation position among species and gene families, physico-chemical consequences on the predicted variant protein and splicing effect are investigated  (7, 8). Second, published reports and public databases, such as functional tests and single nucleotide polymorphisms records, are important to consider. Lastly, additional biological tests may be attempted to reach an unambiguous conclusion. A combined approach is therefore recommended to determine the contribution of VUS to Lynch syndrome  (9). Mutation databases can help in the classification of VUS by providing a compiled source of a large amount of information.
We present here the databases of all genetic variations encountered by the French MMR network, which is made of the 16 licensed laboratories in France involved in the molecular characterization of Lynch syndrome, i. e. the MMR genes germ line analyses. These databases were developed with the universal mutation database UMD Õ software  (10). They have been endorsed by the French Cancer National Institute INCa. They are available online (UMD-MLH1/MSH2/MSH6: http://www. umd. be/MLH1/, http:// www. umd. be/MSH2/ and http://www. umd. be/MSH6/). Two curators collect and compile information from all 16 laboratories. The UMD-MLH1/MSH2/MSH6 databases centralize all identified variations whether causal, neutral or VUS; these variations are linked together through a unique sample ID, allowing the simultaneous retrieval of all genotypes reported in the databases for a given sample, called co-occurrences.
In June 2012, the UMD-MLH1/MSH2/MSH6 databases contained data from 2389 entries for MLH1, 2380 for MSH2 and 1711 for MSH6. In this report, we first analyze variations observed in the French population. Then, we describe the 707 distinct MLH1, MSH2 and MSH6 VUS and the associated clinical and biological data. Finally, we explore the co-occurrence data in an attempt to classify VUS as either causal or neutral.
Methods MLH1/MSH2/MSH6-UMD databases Supported by the French national cancer institute, the Universal Mutation Database core database was retained to register the MMR germ line variations observed in the French population (http://www. umd. be/, UMD: PMID10612827 and PMID16086365)  (10, 11). The UMD generic software includes an optimized structure to assist and secure data entry and to allow the input of various clinical or biological data. Offline copies of UMD-MLH1/MSH2/ MSH6 are continuously edited and updated by the curator; edited copies are regularly deposited on the server. These public databases are available online. Their integrity is ensured by the original template offline. The curators would gratefully receive notification of errors in the current version. Databases design complies with the general recommendations for Locus-Specific Data Bases (LSDB) characteristics and follow-up as proposed by the Human Genome Variation Society  (12).
Each website is divided in six parts. The 'gene', 'protein' and 'clinics' are dedicated to information on the MLH1, MSH2 or MSH6 genes (numbering of exons, cDNA sequences and information on the protein) and the Lynch syndrome. The 'mutations' and 'statistics' options allow the user to search into the databases. Hyperlinks are provided to access the data search options. The sixth part, 'references', aims at providing a list of all published references related to the VUS reported in the databases. It is currently in the validation process. The web pages also contain links to external information including other UMD databases and public central databases (Pubmed, OMIM, Unigene, GeneCard as examples). The UMD knowledgebase system includes many analytical tools  (10, 11). The user is able to optimize multicriteria search tools to select records from any field.
Patients who were detected as mutation carriers received a specific information relative to the registration of their genetic results in the national database during a second interview. The full coding sequences and exon-intron junctions of the MLH1, MSH2 and MSH6 genes were screened for variations, based on prescreening methods or more recently by systematic exon sequencing. Large genomic rearrangements were identified by Multiplex Ligation-dependent Probe Amplification or Quantitative Multiplex PCR of Short Fluorescent Fragments. The corresponding references are displayed in the 'clinics' section. The French MMR network of laboratories involved in the genetic testing of Lynch syndrome collected all MMR variations identified in blood samples of the affected patients and at-risk relatives.
Quality control procedures were in place throughout the collection, processing and storing of data as required by the standard ISO 15189 in all laboratories. Genotypes were double checked on two independent biological samples from the same individuals except in case of index case death before availability of genetic result. Reference sequences for the MLH1, MSH2 and MSH6 genes were NM_000249. 3, NM_000251. 1 and NM_000179. 2, respectively, as mentioned in the Locus Reference Genomic database. Genomic variations were described according to recommendations of the human genome variation society (www. hgvs. org/mutnomen/), and the predicted proteins were deduced using the algorithms of the UMD core system. Data are freely accessible at www. umd. be/MLH1/, www. umd. be/MSH2/ and www. umd. be/ MSH6/.
An anonymous and unique sample identifier (sample_ID) was generated for each individual and was used in the three databases. In June 2012 release, 2562 entries were recorded for MLH1, 2731 for MSH2 and 1754 for MSH6. The UMD software automatically checked for the correct description of the sequence variations at the nucleotide level and generated the variation name at the protein level. Collection of phenotypic data is in progress as well as pictures of Sanger-sequencing profiles. As the treatment of genomic events like large genomic deletions, insertions or deletions surrounding splicing junctions, deletion/insertions and variations located in the 5 0 UTR and 3 0 UTR regions, was recently integrated to UMD database structure, these variations are currently in the validation process and will be soon uploaded.
Variations analysis The system of five variation groups developed at the International Anticancer Research Center was used for functional classification  (6). Mutations predicted to result in protein truncation were classified as deleterious and no longer discussed. The remaining variations were classified of VUS and were selected for additional investigation. Reports about these VUS either in publications or in other MLH1/MSH2/MSH6 databases are regularly reviewed. Information is individually available by clicking on 'I found a mutation' of the 'mutations' section. It will be soon fully accessible in the 'references' section. For each VUS, in silico prediction, including splicing analyses, conservation level, SIFT and UMD-Predictor estimates were directly integrated into the UMD database structure.
They are automatically activated when opening the summary of the corresponding variation. In addition, five pieces of information were asked to French contributors then evaluated according to their influence on MMR function impairment  (14) : Cosegregation analyses were performed in the relevant families whenever possible, and the cumulative LOD scores were calculated, but the corresponding pedigrees were not displayed to keep data anonymous; Clinical phenotype was detailed for each patient according to the enlarged Amsterdam criteria; Assessment of the MMR function in tumor cells by genotyping and immunohistochemistry analysis. MSI/ MicroSatellite Stability (MSS) classification followed the consensus recommendations and was validated only if the proportion of cancer cells in the tumor fragment exceeded 10%.
Immunohistochemistry was performed using antibodies against the two proteins MSH2 and MLH1, completed with MSH6 and PMS2 when possible; Splicing impairment was explored using a functional splicing assay developed and validated by Gaildrat et al. (15). In case of major abnormal splicing, confirmation by RT-PCR after mRNA extraction (PAXgene TM Blood RNA Tube, Qiagen France) was required; Transcription-translation assays were performed for VUS involving the Kozak sequence or the 5 0 UTR. Functional protein assays were traced through a systematic PubMed search that allowed collecting data from in vitro or in/ex vivo assays according to the classification proposed by Ou et al. (16). Allele frequencies were derived from on-line databases or specific studies when at least 100 control individuals (200 alleles) were tested.
All these data are displayed in a table entitled 'Complementary data about this mutation' following the links 'mutations', then 'I found a mutation' and then indicating the amino acid position. Finally, the search for the presence of other variations found simultaneously within the three databases in the same sample_ID is currently implemented, and a co-occurrences table is automatically carried out on the same page. All data are also available following the links directly from the general table of mutations.
UMD-MLH1/MSH2/MSH6 databases In June 2012, the 16 licensed laboratories belonging to the French MMR network reported 2562 variations for MLH1, 2731 for MSH2 and 1754 for MSH6. The data on frequent polymorphisms were not systematically collected. With the exception of large genomic deletions, insertions or deletions surrounding splicing junctions, deletion/insertions and variations located in the 5 0 UTR and 3 0 UTR regions that are not yet uploaded, 2389, 2380 and 1711 records could be integrated in the UMD-MLH1/ MSH2/MSH6 databases, respectively. They represent 429 different variations on MLH1, 455 on MSH2 and 290 on MSH6. These different variations were predicted to result in protein truncation (nonsense mutations, insertions or deletions associated with a frame shift, mutations involving nucleotides AE1 and AE2 within splicing junctions) in 171 (40%) occasions on MLH1, 199 (44%) on MSH2 and 97 (33%) on MSH6.
Characterization of VUS The 707 VUS were scattered in 258 on the MLH1 gene, 256 on MSH2 and 193 on MSH6. In either the 'mutations' or 'statistics' option, in silico estimates can be displayed when clicking on a variation/variation type (missense, splicing sites, etc. ) in a table or when asking for a specific position in the coding sequence or when searching using the different modules. When the non-truncating variation occurs within the coding sequence, the UMD-Predictor tool gives first an estimate of the functional impairment based on structural alteration of the corresponding protein, pathogenicity at the physic-chemical level together with the conservation level, the SIFT prediction and a graphical view of the amino acid involved in the variation (Figure  2 ). When the variation occurs within non-coding sequences located at the close vicinity of the splicing junctions (<10 nucleotides) or mid-intronic, prediction of the modification of the splicing process is graphically presented and summarized in a table giving Consensus Values and variation (Figure  3 ).
The co-occurrence data are extracted from the three databases simultaneously based on the sample ID. They consist in a table presenting all samples carrying the variation and a list of all variations entered for each sample in the three databases, using color codes to highlight deleterious variations (Figures  2  and 3 ). Then the complementary data from the corresponding patients and tumors obtained by the French MMR network are listed regarding the clinical phenotype, the cosegregation within families, the status of MMR function in tumor cells and/or the splicing/protein characterization. These are original and mostly unpublished data. Each corresponding provider (i. e. the French contributing laboratory) is mentioned with the validation date of information. When available, data published by other groups are also displayed (Figures  2  and 3 ).
A link toward the corresponding PubMed access has been incorporated to the relevant entry. Finally, the consensus conclusion of the French MMR network regarding the role of the VUS in Lynch syndrome is stated as a functional class (neutral or class-1, likely neutral or class-2, causal or class-5, likely causal or class-4, VUS or class-3). It is reviewed when new data become available and only the most recent conclusion is presented and time stamped (Figures  2  and 3 ). The review of all 707 VUS reported in the UMD-MLH1/MSH2/MSH6 database allowed the French MMR network to classify 370 of them as causal (55 variations), likely causal (54 variations), likely neutral (80 variations) and neutral (181 variations) (Table  1 ).
Discussion The UMD-MLH1/MSH2/MSH6 databases provide an overview of MMR variations present in the French population We examined MLH1/MSH2/MSH6 variations collected by the 16 licensed laboratories located in France and belonging to the French MMR network during the past 18 years. Updates on new variations or new samples found to carry MMR variations are done twice a year. In June 2012, a total of 7047 variations were provided for registration, and 6480 could be integrated by the UMD software for further analyses. They represent 1174 different variations corresponding to 467 truncating mutations (40%) and 707 VUS (60%). Entries correspond to all variations found through a complete exon screening of the three genes not only in index cases but also in those relatives that were genetically screened and found to be mutation carriers.
Position of the UMD-MLH1/MSH2/MSH6 databases as public databases Several MMR mutation databases already exist. The historical InSiGHT (International Society for Gastrointestinal Hereditary Tumors) database gives an exhaustive description of the data submitted by the contributors (www. in sight-group. org). A dedicated database (www. mmruv. info) is linked to the previous one and contains the great majority of data available in the literature, but does not provide its own interpretation  (16). The link from the UMD-MLH1/MSH2/MSH6 databases toward mmruv for all shared VUS is available by clicking on the amino acid change in the paragraph 'data for this mutation' (Figure  2 ). Another substantial work proposes a VUS classification based on a series of 932 early onset colorectal cancer patients registered for clinical data, MMR function in tumor cells, in silico analysis and genotyping studies in both relevant families and control population  (17).
A classification score is proposed, but the threshold indicating a pathogenic effect to be taken into account for patient management is not defined. Although the methodology for the score calculation is not given, we anticipate that this computation requires fulfilling all criteria, such situation being infrequent in routine practice. Finally, a bioinformatic prediction method called PON-MMR has been recently published, which proposes an online tool returning pathogenicity scores by email after submission of MMR missense variations  (8). This tool is not connected to mutation databases or to related published data, thus precluding an easy review of eventual discordances.
Conclusion The UMD-MLH1/MSH2/MSH6 databases presented here are an integrated system, allowing the simultaneous access to selected published data and original information provided by the French MMR network in the three MLH1, MSH2 and MSH6 genes. Newly recorded information becomes available for consultation after consensus validation by the French working group. The databases are updated twice a year. In addition to the exhaustive registration of mutations found in the French population, the database presently contains the relevant data, allowing the classification of 370 variations either in (likely) neutral or causal. This classification has been achieved based on original data collection from patients referred to the French MMR network. The tool also displays all co-occurrences within the three genes on each VUS-specific page of the UMD-MLH1/ MSH2/MSH6 databases.
ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bat036, doi:10. 1093/database/bat036.
Acknowledgments We are grateful to the  French molecular biologists involved in genetic identification of Lynch syndrome , who regularly contribute to the national mutation database with their data: Bonnet F and  Sevenet N (CLCC Bergonie, Bordeaux France), Bezieau S, Kury S and Airaud F (CHU Hotel Dieu, Nantes France) ,  Bronner M  and  Sokolowska J  ( CHU Brabois, Nancy France ),  Burnichon N and Abermil N (CHU Georges Pompidou, Paris France) ,  Colas C and Coulet F  ( CHU Pitie-Salpetriere, Paris France ),  Guilbaut-Bataille M  and  Bressac B  ( CLCC Gustave Roussy, Villejuif France ),  Leclerc J  ( CHRU Lille), Maugard C (Hopitaux Civils, Strasbourg France ),  Noguchi T and Remenieras A (CLCC Paoli-Calmettes ,  Marseille France ),  North MO  ( CHU Cochin, Paris France ),  Rey JM  ( CHU Arnaud de Villeneuve, Montpellier France ),  Rouleau E  ( CLCC ReneHuguenin, Saint-Cloud France), Tinat J (Hopital Charles Nicolle, Rouen France), Toulas C (CLCC Claudius Regaud, Toulouse France ) and  Uhrhammer N (CLCC Jean Perrin, Clermont-Ferrand France ).
Funding French national cancer institute  INCa  for the  UMD-MLH1/ MSH2/MSH6  database construction and updating [to S. O. ], the splicing reporter minigene assays [to P. G. ];European Community's  Seventh Framework Program  for the  GEN2PHEN  project (development of the UMD tool) [FP7/ 2007-2013 number  200754  to C. B. ].