dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding information. Giles Foundation ;  Agence Nationale de la Recherche , Grant/Award Numbers:  ANR-10-IAHU-01 ,  ANR-10-LABX-62-IBEID ,  ANR-14-CE14-0007-01 ,  ANR-16-CE12-0023-01 ,  ANR-19-CE15-0010 ;  National Center for Advancing Translational Sciences ;  Rockefeller University ;  National Institute of Allergy and Infectious Diseases , Grant/Award Numbers:  R01AI088364 ,  R37AI095983 ,  R01AI127564 ,  P01AI061093 ;  National Institutes of Health , Grant/Award Number:  UL1TR001866.
Nanopore sequencing detects low CpH mtDNA methylation across diverse biological conditions. In addition to 5mCpG, new algorithms have become recently available for the assessment of 5mC in non-CpG (CpH) contexts from nanopore sequencing data. These algorithms are based on training with reference sequences using deep learning and are contained in the Rerio nanopore repository (ONT). Once the model is defined, a research command line tool (i. e. Megalodon, see "Methods") is used to extract high accuracy modified base from raw nanopore reads by anchoring the basecalling output to a reference genome. From the Rerio repository, we selected the most recent version trained to detect all-context 5mC on MinION raw sequencing data (see "Methods"). To validate the performance of this algorithm for CpG and CpH 5mC, we first used  nuclear DNA extracted from central nervous tissues, known to be relatively abundant in 5mCpH in physiological conditions.
We did not find significant differences in 5mC at those locations between tumors and adjacent tissues. Moreover, in a separate analysis, we excluded all reads aligning to mtDNA with a length below 8798 bp (the longest NUMT in our reference database). After removing those reads, the global differences between tumor and non-tumor HNC tissues was still significant. Therefore, while several cytosines were found differentially methylated in cancer tissues from HCC and HNC, we were able to detect CpH above background levels only in HNC tissues. This could be the result of tissue-dependent profiles, or the different algorithms used to call base modifications from the two devices used in these two independent datasets.
For validation, we also called DNA methylation using the novel tool, Medaka (https:// github. com/ nanop orete ch/ medaka). Medaka is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster. Both tools have been recently benchmarked  22. PycoQC was used for data inspection and quality control (https:// github. com/a-slide/ pycoQC), and methplotlib (https:// github. com/ wdeco ster/ methp lotlib) for read-level visualizations. Called CpG sites in the FU control were used to determine a baseline of methylation.
The following calculation was utilised: FalsePositiveRate = [#called methylated cytosines in FU/#called cytosines in FU]. To call 5mC in all contexts, we first demultiplexed the reads at the raw (fast5) level using Deepbinner (v. 0. 2. 0)  52  , before basecalling and extracting base modification information using the most recent model from the Rerio repository (https:// github. com/ nanop orete ch/ rerio) implemented through the Megalodon tool (v. 2. 2. 9) (https:// github. com/ nanop orete ch/ megal odon). Megalodon extracts high accuracy modified base and sequence variant calls from raw nanopore reads by anchoring the information rich basecalling neural network output to a reference genome. Specifically, we used the corresponding MinION (res_dna_r941_min_modbases_5mC_v001. cfg v. 4. 2. 2) and PromethION (res_dna_r941_prom_modbases_5mC_v001.
cfg v. 4. 2. 2) models, with a -modbinary-threshold of 0. 8, as recommended. To validate CpH analyses, we used Megalodon (as described above) to re-basecall raw fast5 data from a published dataset  53  (https:// www. ebi. ac. uk/ ena/ brows er/ view/ PRJEB 33258). As these libraries were prepared after cas9-targeting of selected genes (3 gRNA designs, that resulted in 19 hg38 genomic regions), we pooled the reads from several samples to be able to plot as enriched heatmaps (Fig. 6D, E ). For differential methylation analyses we used DSS (Dispersion shrinkage for sequencing data)  54  adapted for nanopore sequencing  51. Briefly, DSS tests for differential methylation at single CpG-sites, using a Wald test on the coefficients of a beta-binomial regression of count data with an 'arcsine' link function.
In order to set minimum requirements for DSS analysis, an internal comparison of biological replicates of differentiated HepaRG cells was undertaken. From this we were able to better understand the background and determine the minimum smoothing and delta values. These values were set at a delta of 0. 05 with minimum P-value of 0. 05. For transcription factor binding site analyses, we used the bioconductor packages MIRA  21  for methylation data aggregation, and LOLA for dataset selection  55. Mann-Whitney's test was used for pairwise comparisons of 5mCpG distribution.
Acknowledgements The authors would like to thank the patients that participated in this study. In addition, the authors would like to thank  Maud Michelet ,  Jennifer Molle ,  Loic Peyrot ,  Anaelle Dubois ,  Océane Floriot ,  Laura Dimer ,  Marie-Laure Plissonnier  and  Julie Lucifora  for the isolation of primary human hepatocytes as well as Pr Michel Rivoire's,  Pr. Jean-Yves Mabrut 's and  Dr. Guillaume Passot 's staffs for providing the liver resections. The authors also thank  Marcus Stoiber  from  Oxford Nanopore Technologies  for his helpful feedback on the Megalodon repository.
We evaluate the probability P (P / ∈ Q k 2 |P ∈ Q d ) for d ∈ {3 , 4, 5} and k ∈ {1, 2}. In order to evaluate this probability, we first build the basis for the linear span of M k∈{1,2} 2 corresponding to the sets Q k∈{1,2} 2 with the method described in Sec. 3. 1. Then, with the same method, we also sample the sets Q d∈{3,4,5} and compute the visibilities (defined in Eq. 15)  for each sampled data point. For that purpose we used CVXPY  [62, 63]  with the solver MOSEK  [64]. We used about 10000 points to evaluate each probability. The code is available in  [52]. The probability is estimated in the following way: P (P / ∈ Q k 2 |P ∈ Q 3 ) ≈ N (ν < 1) N tot , ( 16 ) where N (ν < 1) represents the number of data points with a visibility ν < 1 and N tot is the total number of data points. Finally all the computed probabilities are represented in the Tab.
Acknowledgements AS and JK have been supported by a  KIAS individual grant  ( CG070301  and  CG014604 ) at  Korea Institute for Advanced Study. DM acknowledges support of the  ANR  through the  ANR-17-CE24-0035  VanQuTe. AS would like to thank  S. Akibue. MTQ would like to thank  M. Araújo. MTQ  acknowledges support of the  Austrian Science Fund (FWF)  through the  SFB  project " Be-yondC ", a grant from the  Foundational Questions Institute (FQXi) Fund  and a grant from the  John Templeton Foundation  (Project No. 61466 ) as part of the The  Quantum Information Structure of Spacetime (QISS)  Project (qiss. fr). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the  John Templeton Foundation. The authors are grateful to the  Center for Advanced Computation at Korea Institute for Advanced Study  for help with computing resources.
INTRODUCTION Food allergy is a growing food safety and public health concern. Well-curated, publicly accessible allergen databases serve a range of public health roles, including informing experimental research programs, clinicians, and allergists and/or providing critical data to patients or the public. In the food safety space, an allergen database serves also the purpose of evaluating concerns over the transfer of known allergens or potentially cross-reactive proteins to genetically-modified (GM) food crops or animals. In fact, regulatory agencies evaluating the safety of biotechnology products for genetically-modified food and feeds require an assessment for potential allergenicity, including comparing novel or newly discovered proteins at the level of their primary amino acid sequence to known allergens, which further emphasizes the need for a comprehensive allergen database.
The goal in that assessment is to identify proteins that may be known allergens or have sufficient structural similarity to warrant further experimental investigation into their potential to elicit allergic responses based on cross-reactivity. Therefore, allergen databases support allergy science and safety by two distinct but related processes: (1) comparative processes to identify potential similarity between query sequences and allergens using bioinformatics tools, and (2) identification of the source organism for any listed allergen, allowing researchers to assess taxonomic relatedness among the organisms producing the allergens/proteins. Some individual companies maintain their own allergen databases. Additionally, several public databases are now available, such as the University of Texas Medical Branch's Structural Database of Allergen Proteins-SDAP (https:// fermi.
utmb. edu/) (1, 2) , World Health Organization (WHO)/International Union of Immunological Societies (IUIS) Allergen Nomenclature Database (http://www. allergen. org/)  (3) (4) (5) (6) (7) , and AllergenOnline (http://www. allergenonline. org) supported by the University of Nebraska Food Allergy Research and Resource Program (FARRP)  (8). Others include AllFam  (9) , Allergome  (10, 11)  and Allermatch  (12) , released in 2003 and 2004, respectively. Recently, another allergen database, AllerBase was established  (13). Some additional allergen databases are available but not all are maintained regularly and have varying inclusion criteria  (14). Reviews of these various allergen databases are available  (15) (16) (17) , in which the features and specific purposes of each resource are described.
SDAP for example, can be used to find sequence and structural neighbors for an allergen, and to search for the presence of an epitope  (18) (19) (20). AllerML  (21)  is a markup language designed to facilitate exchange of information between databases. Currently SDAP is the only allergen database using AllerML and therefore we have not yet implemented this attribute in the COMPARE database. We do not see this as an impediment to efficient searching in COMPARE as the entities in the COMPARE database are clearly annotated and distinct from the content. Data is easily available for download in both tabular and fasta formats. COMPARE data is presently being used by other recent databases and tools: AllerCatPro, developed to predict the allergenic potential of proteins based on their 3D protein structure as well as their amino acid sequence  (22)  and a Random Forest allergenicity prediction model  (23).
The initial number of sequences to be filtered when screening new potential allergens has grown exponentially, necessitating higher-density data handling methods to sort and identify information relevant for food safety. As the publication of genomic sequence information increased, the challenge of updating an accurate allergen database intensified as public repositories become diluted with automatically annotated sequence information and sequences that are not expressed proteins. For example, only a minute proportion of proteins in the National Center for Biotechnology Information (NCBI) Protein sequence database have been identified as allergens, with almost 60% of these belonging to four protein families  (24, 25). In contrast, the February 2021 release of UniProtKB/TrEMBL counted over 200 million protein sequences; of these, over 67% are "predicted" sequences, 31.
7% are inferred from homology, and only about 1% have evidence at the transcript level (0. 65%) or protein level (0. 08%) data obtained from "UniProtKB/TrEMBL Protein Database Release Statistics, " https://www. ebi. ac. uk/uniprot/TrEMBLstats, last accessed February 12, 2021. To address both the dilution effect and annotation quality issues, the Health and Environmental Sciences Institute (HESI) Protein Allergens, Toxins and Bioinformatics (PATB) committee has developed the COMprehensive Protein Allergen REsource (COMPARE) database, a new protein allergen database along with a sequence and literature screening process for public access. COMPARE's structure and process is designed to facilitate efficient annual updates. In its initial build, COMPARE started from the foundational sequence list developed by FARRP AllergenOnline database v.
16, which shares COMPARE's commitment to blinded peer review and annual curation and updating, to which new sequences identified through the COMPARE process have been added to in 2017 and on each subsequent annual update. The critical methodological components of the COMPARE database consist of scientific, structural, procedural, and quality/rigor assurance components. The COMPARE process engages a broad international multi-sector scientific steering committee with representation from government (US Environmental Protection Agency and US Food and Drug Administration [FDA]), academia, and private-sector scientists who bring real-world know-how and experience of gaps and challenges to be considered. Oversight and coordination by HESI, an independent and objective institute dedicated to advancing the understanding of scientific issues related to human health and the environment, as well as programmatic support from the Joint Institute for Food Safety and Nutrition (established between the FDA and the University of Maryland, JIFSAN; www.
jifsan. umd. edu) in the development of bioinformatics analytical tools associated with the COMPARE database provide organizational support for a structured collaborative effort, made possible with pooled financial support from private sector partners, as well as significant in-kind support from both public and private sector collaborators. The quality and scientific integrity of the COMPARE resource is safeguarded through various mechanisms inherent to HESI's operating model: maintaining public-private representation, transparent documentation, and open access to the database. All database design and search algorithm decisions are publicly documented on the COMPARE website (http://www. comparedatabase. org), under the "Process Development" tab and presently, in this article.
SYSTEM AND METHODS The Database Build Process: Criteria-Based, Transparent, and Reproducible The COMPARE process implements a consistent and transparent mechanism to identify protein allergens via the following: (1) The development of a high-throughput, automated sequence sorting algorithm; (2) The systematic collection of scientific literature supporting the identification of allergens; (3) The coordinated review by an external peer review panel (PRP) of internationally recognized allergy experts from the public sector; (4) An annual public release of the database by repeating steps 1-3 to identify new allergens; and (5) Independent management and documentation practices by the non-profit scientific organization HESI, that ensures the integrity of the database and transparency of the process used to populate the database (e.
Development of a High-Throughput, Automated Sequence Sorting Algorithm The Pilot Process Development and Validation Presently, three primary databases contain all the existing sequence data so far generated (GenBank, EMBL database and DNA Data Bank of Japan). As these databanks are interconnected through the International Nucleotide Sequence Database Collaboration (http://www. insdc. org/), data submitted to any one of these databases are shared by, and hence can be retrieved from, all three. The starting point for the COMPARE screening process is the National Center for Biotechnology Information (NCBI) Protein sequence database (https://www. ncbi. nlm. nih. gov/protein), which includes translations from annotated coding regions in GenBank, RefSeq and TPA, as well as records from SwissProt, PIR, PRF, and PDB.
COMPARE's initial pilot search was designed in 2016 to identify NCBI Protein sequence entries that contain the term "allerg * " (which encompasses allergen, allergy, allergenic, and allergic) and include proteins that are derived from animals, plants, fungi, and protists. This pilot search of the NCBI Protein sequence database was completed through the main query window (https://www. ncbi. nlm. nih. gov/protein) using the following Boolean search: By design, the initial search term "allerg * " is very broad, can be found anywhere in each GenPept record, and does not necessarily relate to the identification of a protein allergen. Extended search criteria were thus applied in a stepwise fashion to further refine the broad "allerg * " search results. Keywords for filtering were selected by informatics, allergy, toxicology, and clinical experts (participating members of the public-private COMPARE Steering Team) through an iterative observationbased process, taking into account contextual categorical features such as the sequence's source organism, whether the sequence was submitted through an automated annotation pipeline (i.
Testing and Validation of the Filtering Algorithm The COMPARE process was validated by testing it against the AllergenOnline v. 16 database. The results indicated that many of the sequences containing "allerg * " in their annotation were classified as candidates by the COMPARE algorithm, as expected. Yet this testing also found that some sequences in AllergenOnline v. 16 (GenPept format) do not contain the term "allerg * " in their annotation, and as such were not being captured. Manual examination of those sequences revealed that they are typically associated with specific allergen designations such as profilin or tropomyosin. This validation step allowed the identification of additional keywords (Supplementary Table  1 ) to be used in parallel to the "allerg * " search of the NCBI Protein database.
This supplemental search and filtering was performed for the 2017 and 2018 COMPARE database builds. After conducting the supplemental search in parallel of the main search for 2 years, it appeared that the results did not produce significantly new information compared to the outcome of the main algorithm search. This parallel approach was therefore discontinued in subsequent years. The final COMPARE keyword-based filter consisted of 13 steps and 28 elements (i. e. , decision steps/points) (Supplementary Figure  1 ).
Additional Sources of Allergen "Candidates" A robust search for potential new allergen candidates needs to cast a broader net than a year's worth of NCBI Protein database entries for several reasons. First, an allergen may have been identified but not submitted to the NCBI Protein database. This is likely to occur when the allergen discovery is extremely recent. Second, there may be older entries in the NCBI Protein database for which new evidence of allergenicity may have become available. A solution to both situations is a thorough search of the scientific literature each year to capture new evidence. Such a process was implemented in the development of COMPARE 2018 and future iterations of the database, the parallel direct literature search strategy used is described in (Appendix-1 in Supplementary Materials).
Lastly, there are other important protein sequence databases available to the scientific community. For example, UniProtKB is a protein database partially hand-curated by experts  (26, 27) , it was included in the bioinformatics screening for identification of candidate allergens as of COMPARE 2019. There are other allergen-specific databases with various degrees of overlap, as reviewed by  Radauer (16)  and  Radauer and Breiteneder (15) , from which new allergen candidates not included in COMPARE could be evaluated by the COMPARE expert panel. Such databases include the World Health Organization (WHO)/International Union of Immunological Societies (IUIS) Allergen Nomenclature Database (http://www. allergen. org/) (6), responsible for maintaining and developing a unique, unambiguous, and systematic nomenclature for allergenic proteins, and AllergenOnline (http://www.
Incorporation of Sequence Comparison Capabilities Accessible to COMPARE Users A key to understanding cross-reactivity from a sequence-based perspective is to identify a minimum degree of similarity between a query protein and a known allergen that can prompt further investigation. Global guidelines on a minimum similarity (e. g. , > 35% sequence identity over a sliding window of at least 80 amino acids) have evolved in several ways  (28) (29) (30) (31) (32) (33) (34) (35)  and the validity of the various criteria has been discussed in various reviews  (36) (37) (38) (39) (40) (41). However, while the scientific merits of these criteria are still evaluated and discussed, all approaches rely on a common element: a database containing protein sequences of all currently recognized allergens, to enable in silico processes of assessing a protein's potential allergenicity.
In January 2019, COMPARE 2019 was released with a more dynamic interface to facilitate the ease of access to relevant information to all users (clickable links for easier access to literature sources) and for increased functionality. This new interface allowed the incorporation of a built-in FASTA-based sequence comparison tools, "COMPASS" (COMPare Analysis of Sequences with Software), where users can compare their selected (query) protein sequence to the allergens in the database. The software of choice has traditionally been the FASTA algorithm  (42)  and is recommended for assessing similarity between protein sequences. Therefore, COMPASS operates with the open source FASTA software package (FASTA v36 at the time of this publication; https://fasta. bioch. virginia. edu/fasta_www2/ fasta_list2.
shtml). The incorporation of FASTA search algorithm as the core of the COMPASS search tool on the COMPARE database supports real-time sequence comparisons using the following three approaches without downloading the database and installing software on local computers/servers: (1) full-length sequence comparison; (2) 80-aa (amino acids) sliding window comparison; (3) 8-aa exact match [based on FAO/WHO  (30)  and CODEX Alimentarius Commission  (28, 43)  guidelines on the evaluation of proteins derived from modern biotechnology for allergenicity]. As of July 2020, the COMPASS search tool offers a visualization component, allowing users to view results in a graphical display. Additionally, other features such as a reference list and synopses of relevant literature and guidelines, as well as a sort-and-search function of the database listing are available for educational purposes in the COMPASS homepage and "About" tab.
Literature Procurement One of the key requirements of the COMPARE process is that a "candidate allergen sequence" retained for review by the expert panel has at least one supporting scientific publication related to the specific candidate sequence. This rule is strictly A "Parent accession" field was added in the details view page of entries. This field applies particularly to smaller fragments, derived from mass-spectrometry studies, and is intended to connect the fragment to the full protein from which it is derived (when indicated in the literature associated). A "parent accession" number will be indicated in that field when applicable and when available (not all entries will have one). The parent accession is NOT an entry in the database and merely provided as additional metadata for users' reference.