dataesr/scientific-paragraphs-categorization huggingface dataset:
Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases.
This constraint strongly implies that highly relevant metadata should always be collected prior to data collection. All the same, we are flooded by genome data merely because sequencing has become so accessible and costeffective  (Schmidt and Hildebrandt, 2017) , with significant, large-scale efforts regularly making the headlines of popular dailies. In early days, when sequencing was extremely tedious, sequences were obtained with a specific view in mind, linking them in a straightforward way to relevant and rich meta-information. With rapid progress and radical improvements of sequencing techniques, it became important to start collecting further informative annotations, some of which might be less obvious. The creation of databanks has always played a central role as reference repositories of all biological information that could be linked to DNA and protein sequences.
This process of submission and re-distribution was first performed manually until, not without some controversy, journals decided to start accepting articles containing novel sequence data. Their condition was that all molecular information would become publicly available so that data sharing would automatically feed into the collection process  (Roberts and Koetzle, 1989; Blaxter et al. , 2016). After a long series of discussions and efforts, this resulted in the establishment of the International Nucleotide Sequence Database  Collaboration [INSDC (Karsch-Mizrachi et al. , 2018) ], exchanging and updating sequences on a daily basis between three entry points, DDBJ in Japan  (Kodama et al. , 2018) , ENA in Europe  (Silvester et al. , 2018)  and GenBank in the United States  (Benson et al. , 2018).
This resulted in the unprecedented success of the BLAST program  (Altschul et al. , 1990) , which, used under a variety of flavours, is still the most efficient and widely available way to compare sequences. Furthermore, similarities were grouped together using BLAST bidirectionally, creating the Clusters of Orthologs that are still in use  (Galperin and Koonin, 1999) , the TRIBEs resource  (Enright et al. , 2003)  and others that followed. A family of tools, critical both for the identification of important sites in proteins and their evolution, allowed the multialignment of protein sequences  (Lipman et al. , 1989). These are now used in a large variety of software that rest on specific hypotheses monitoring similarities and evolution [see  (Pearson et al. , 2017; Zambrano-Vega et al. , 2017; Sievers and Higgins, 2018)  for recent developments in multiple protein sequence alignment].
, 2018) , riboswitches  (Serganov and Nudler, 2013) , ribozymes  (Wilson and Lilley, 2015)  and other miscellaneous RNAs  (Nelson and Breaker, 2017) ] completed the picture. As relevant metadata, this involved the tagging of gene sequences for control regions  (Nikolaichik and Damienikan, 2016)  as well as other elements of genome organization, and of course the source of the sequence, often a scientific article. Taken together, all these pieces of metadata must form an explicit data structure that will be recognized by a variety of database schemas. The core data object associated with multiple metadata tags is usually a DNA sequence, the 'genomic object' (Fig. 1 ). However, in some databases the choice is different: the Genes section of FlyBase includes information on Drosophila genes that has been curated from the literature and sequence databases.
In this way, the scientific article is the key field connecting other fields together in the database  (Gramates et al. , 2017). It should be obvious, then, that input and extraction of information from such diverse data collections will lead to widely different pathways to discovery. An inconspicuous but immense (ongoing) effort in this domain is undertaken by all participants of the INSDC  (Karsch-Mizrachi et al. , 2018) , who endeavour to set up data structures in order to collect and make freely available nucleotide data sequences described in scientific articles, patents or deposited directly at one of its three entry points (DDBJ, ENA, GenBank). An important point here is that how data is structured has a far-reaching impact on data annotation quality. Data structures affect the ease with which a community of investigators can submit annotations.
They can facilitate, or alternatively prevent, accurate annotation. Furthermore, discoveries made in experimental laboratories depend heavily on the quality of the data annotation, organization and user-friendliness of a variety of databases designed for the community by investigators essentially unknown to the end users. Work on data structures is therefore vital for biological and medical research but rarely brought into the limelight. Here, for the sake of brevity, we restrict our discussion to prokaryotic genome data. A further step involving data structuration appears when a subset of genome-derived data is organized into specialized databases. A first key step in this effort was the attempt to create data structure-aware databases of proteins, as developed by SwissProt  (Bairoch, 1982; Bairoch and Boeckmann, 1991)  and the Protein Identification Resource [PIR  (Barker et al.
, 1983; Sidman et al. , 1988) ], now united as the UniProt protein annotated database  (UniProt Consortium, 2018). Subsequently, query languages devoted to biological sequence management such as the ACNUC language  (Gouy et al. , 1984)  were developed. First microbial databases with a minimal data structure were then meant to make whole genome sequences and annotation available to the community  (Higgins and Danchin, 1990; Kunisawa et al. , 1990). A further refinement was based on statistical analyses of the genome structure, leading to the discovery of the key role of horizontal gene transfer in bacterial genomes  (Medigue et al. , 1991). Links with the previously known restriction maps  (Kroger et al. , 1990; Medigue et al. , 1990)  allowed the building up of a more evolved data structure that resulted in the reference Colibri database for the Escherichia coli genome  (Medigue et al.
, 1993)  based on the concept of genomic object as its core item. This database also combined gene annotation with specific methods meant to extract biologically relevant sequence-based information, as first illustrated in the case of Bacillus subtilis  (Moszer et al. , 1999). With the explosion of microbial genome data, it now seems essential that new work begins to build up on these past works [for a discussion see  (Borriss et al. , 2018) ] at a time when it appears that interest in building up specialized databases relying on high level data structures is vanishing.
e. with a proposed translation start site. A popular software, PRODIGAL, created at the Department of Energy Joint Genome Institute, is often used to identify bacterial translation start sites  (Hyatt et al. , 2010). However, despite its qualities, it remains imperfect as our knowledge advances. For instance, the genomic G+C content is highly variable, and this may influence ribosome initiation addressing so that rulebased approaches mimicking biological behaviour should still be explored  (Makita et al. , 2007). Automatic validation of starts can be obtained via alignment of the CDSs of multiple orthologous genes (the initiation codon and ribosome binding sites should be somewhat conserved). Routinely, however, much of automatic annotation still conflates CDSs and ORFs and this is perpetuated by the unfortunate habit of many investigators to use the name ORF instead of CDS.
, 2001). As a consequence, experimentally based evidence remains a critical issue  (Chang et al. , 2016). In parallel, many experiments revealed that annotation by similarity kept producing errors that percolated throughout databases  (Promponas et al. , 2015). Many of these erroneous annotations still lurk in public databases and have increased in size purely by similarity searches and 'novel' assignments  (Gilks et al. , 2005). The combination of ignorance and percolation of errors can be vividly illustrated by the fact that, quite recently, the first synthetic genome of a bacterium  (Hutchison et al. , 2016)  was imperfectly annotated despite the importance of the experiment and the quality of the sequencing team. Annotation based on accurately annotated model genome databases improved the outcome, demonstrating that there is a need for such knowledge  (Danchin and Fang, 2016).
People tend to be confident in what they say, even when they are wrong. How could we proceed, then? Prelec and coworkers suggest to ask people to predict among several possible answers what they think will be the majority answer proposed by others and subsequently select the answer that gained more support than expected. Apparently, this 'surprisingly popular' approach gives results that are much better than those collected from a direct poll  (Prelec et al. , 2017). Yet, we are still far from real wisdom and it may be somewhat difficult to implement Prelec's approach in an automatic annotation pipeline. Furthermore, when exploring a new dataset we need baselines to test, using well-established knowledge and in realistic conditions, whether the outcome of any procedure gives the relevant answer or fails to do so  (Danchin and Braham, 2017).
This was the raison d'être of the SwissProt protein database, where protein sequences were directly matched with experimental data  (Bairoch et al. , 2004). Emphasis on connection with properly annotated databases of experimental observations has repeatedly been highlighted but with not always with positive outcomes. Along this line, Roberts and co-workers have initiated an effort to construct a database of experimentally validated gene annotations, COMBREX  (Chang et al. , 2016). Others have proposed species-specific, not protein-centric (re-)annotation efforts  (Ouzounis and Karp, 2002). While this type of validation is key to allow significant progress from data accumulation, only very few investigators have been willing to devote a significant part of their work to this essential activity.
Bias in data choice (our anthropocentric view of microbes highlights pathogens) as well as bias in data importance (abundance of species, versus relevance of species) plagues all statistical approaches. There are also many biases in statistics because a probability depends on the model of 'randomness' which is used in the background, often unknown to the authors (see Bertrand's paradox, Fig. 3 ). Another difficulty, rarely realized by many, is that the vast majority of statistical approaches rely on specific properties of the data sets. As a matter of fact, data samples are expected either to differ because of a large variety of additive causes, or of multiplicative causes. The former case should give a normal (i. e. Laplace-Gauss) distribution of data items in the dataset, while the latter should give a log-normal distribution.
A good many multivariate analysis approaches exist to explore this type of data  (Lebart et al. , 1984; Neely et al. , 2012 Neely et al. , , 2013)). Their role is to provide a statistical procedure to explore the data in a pertinent way. In exploratory statistics, the aim of the method is to try and reduce the number of qualities (viz. variables) that contribute to the data order. Mathematical methods used to this end rest on specific hypotheses about the distribution of entities within the data. Normal or log-normal datasets have been used to generate a variety of multivariate analyses based on this widespread yet limited statistical constraint. Among those, Principal Component Analysis (PCA) is quite popular. With this technique, the measure that monitors the distance between entities of interest submitted to analysis commonly uses the variance of each quality or of a linear combination of qualities as a normalization factor.
4 ). This is of considerable help to associate biological knowledge to large datasets. Datasets often deviate from the normal or log-normal distribution. In particular the distribution may be asymmetrical, have long tails or both. Asymmetrical heavytailed distributions may follow Cauchy's law that accounts of the ratio of two independent variables, and What is the probability that the length of the segment in the circle is longer or shorter than the side of the equilateral inscribed triangle? Genomics update 595 this is equivalent to Student's variable of degree 1, comparing well with standard statistical tests  (Zimmerman and Zumbo, 1990 ). Gumbel's law is used for analysis of the distribution of extreme values in a data sample. It has been shown to be important to understand the validity of sequence alignments compared using the Smith-Waterman algorithm  (Comet et al.
Perhaps the most often used techniques are those related to K-means, where, starting from a random set of possible centres of gravity of clusters one progressively moves them to allow the formation of consistent clusters. The investigator can influence the outcome by deciding, at the outset of the computation, the number of expected clusters  (Do and Choi, 2008). Performing this computation while increasing this number allows one to find a situation that can be regarded as optimal, considering the dataset and based on measures such as the In CA, the data and their qualitative variables can be swapped so that they can be visualized within the same spatial representation. Here, each dot represents a protein extracted from the proteome of a bacterial species, and spatial distribution of the amino acids is superimposed on the corresponding cloud of points that differentiates between membrane proteins (blue) and the rest of the proteome.
Analysis of this type of neighbourhood is highly rewarding in terms of functional inferences [some illustration in  (Nitschke et al. , 1998; Szklarczyk et al. , 2017) ]. Another useful trend, certainly not utilized as it should be, is proximity in the literature. To be sure, various investigators have reasons to put together particular genes in the article they write, and this often might signify some type of deep, not immediately obvious, connections. The idea of proximity in articles was at the origin of a smart feature of the ENTREZ software  (Benson et al. , 1996). It has also been at the origin of much research based on automatic exploration of the literaturehere too, not used enough in biological research, other than the identification of plagiarism  (Nawab et al. , 2014). The idea that two genes can be linked because they are cited in the same bibliographical source lies at the heart of the iHOP software resource  (Hoffmann and Valencia, 2005).
ACKNOWLEDGMENTS We acknowledge funding from  ANR   2D-vdW-Spin, ANR VallEx, Labex NEXT  projects VWspin and MILO, ITN  Spin-NANO Marie Sklodowska-Curie  grant agreement No  676108  and  ITN 4PHOTON  Nr. 721394. X. M. also acknowledges the  Institut Universitaire de France. Growth of hexagonal boron nitride crystals was supported by the  Elemental Strategy Initiative  conducted by the  MEXT, Japan  and the  CREST  ( JPMJCR15F3 ),  JST. L. E. G. and M. M. G. acknowledge partial support from LIA ILNACS, RFBR projects  17-02-00383 ,  17-52-16020 ,  RF President Grant   MD-1555. 2017. 2  and " BA-SIS " foundation.
A B S T R A C T A collaborative effort was carried out by the Spanish and Portuguese Speaking Working Group of the International Society for Forensic Genetics (GHEP-ISFG) to promote knowledge exchange between associate laboratories interested in the implementation of indel-based methodologies and build allele frequency databases of 38 indels for forensic applications. These databases include populations from different countries that are relevant for identification and kinship investigations undertaken by the participating laboratories. Before compiling population data, participants were asked to type the 38 indels in blind samples from annual GHEP-ISFG proficiency tests, using an amplification protocol previously described. Only laboratories that reported correct results contributed with population data to this study.
GHEP-ISFG working commission on autosomal indels for human identification In 2012, a GHEP-ISFG working commission on autosomal indels for human identification was established to coordinate a collaborative exercise aiming to promote knowledge exchange between associate laboratories interested in implementing indel-based methodologies, and creating population databases for forensic use (https://ghep-isfg. org/ en/working-commissions/autosomal-indels-for-identification/). This study was approved by vote at the GHEP-ISFG general meeting and complies with the ethical principles of the 2000 Helsinki Declaration of the World Medical Association. In brief, the exercise was organized in two steps. In a first phase, four samples from the annual GHEP-ISFG proficiency tests were used for quality control purposes (samples 1 to 4 included in 2012 or 2013 kinship basic modules; see  [15]  for details on GHEP-ISFG proficiency tests).
Genotyping protocol All samples were genotyped for a panel of 38 autosomal indels in a single PCR multiplex reaction using a previously described protocol  [8]. For this collaborative exercise, a new stock of primer mix was prepared and tested by the coordinating laboratory, and then distributed in aliquots of 500 μl to all participants. Any other necessary materials for the genotyping of the samples were the responsibility of each laboratory. To facilitate the genotyping process, dedicated files for the marker panel and allelic bins were made available for different versions of GeneMapper software (Applied Biosystems). Support to adjust markers and bins to specific conditions of electrophoretic mobility was offered upon request. The same applied for generic questions related with the method implementation in the laboratories.
Statistical analyses Allele frequency estimates, gene diversity values, observed and expected heterozygosities, Hardy-Weinberg equilibrium (HWE) exact tests, and pairwise F ST genetic distances were all calculated using Arlequin software v3. 5. 2. 2  [19]. A Neighbor-joining tree was built from the pairwise F ST matrix with the Neighbor program implemented in the Phylip v3. 695 software package  [20]  and plotted using TreeView v1. 6. 6  [21]. Principal component analysis was performed using Statistica v13. 0 (Statsoft; http://www. statsoft. com/). In the statistical analyses and interpretation of genetic data, a Bonferroni correction was considered whenever multiple testing was performed. In HWE exact tests, the significance level was adjusted for the total number of loci in the set (p = 0.
In a second stage, the laboratories that reported correct results were asked to genotype a population sample, to create a database of allele frequencies to be used as reference in forensic casework. A total of 40 laboratories sent results for at least 100 unrelated individuals for 54 population samples from different countries: Angola, Cape Verde, South Africa, Sudan, Uganda, Iraq, China, East Timor, Czech Republic, France, Germany, Italy, Portugal, Spain, Argentina, Brazil, Colombia, Ecuador, Mexico, Panama, and Uruguay. The genotyping results obtained in these populations for a total of 5839 individuals (after removing a total of 8 samples with more than 10% missing data) are listed in Supplementary Table  S1.
This was the case for two samples from South African Bantu groups (Xhosa and Zulu) (F ST = 0. 00029; P = 0. 3499); the two samples from Ecuador (Sierra and Costa) (F ST = 0. 00380; P = 0. 00554); the three samples from Portugal (North, Central and South regions) (F ST ≤ 0. 00181; P ≥ 0. 02772); and the 6 samples from Argentina, including Buenos Aires, Entre Rios, Mendoza, Resistencia, San Luis and Tucuman (F ST ≤ 0. 00475; P ≥ 0. 00079). Small F ST genetic distances were also found among samples from Spain (F ST ≤ 0. 00424; P ≥ 0. 00208), except for the Basques that showed significant differences in 4 out of the 6 comparisons performed with other Spanish samples. Therefore, samples from Galicia, Cantabria, Madrid, Valencian Community, Malaga, Majorca and Canarias were pooled in a single database from Spain, separated from the Basque Country database.
Among four Brazilian samples, Rio de Janeiro, Espírito Santo and São Paulo showed non-significant differences, and were pooled in a single database representing Brazilian Southeast region. In the comparison of Rio de Janeiro or Espírito Santo with Porto Alegre (in South region) significant differences were observed. Concerning Colombia and Mexico, none of their samples could be combined, since statistically significant differences were found between the four Colombian samples from Antioquia, Bogotá, Norte de Santander and Chocó, and between the three samples from Mexico (from Guerrero natives and the provinces of Chihuahua and Yucatán).
The variant A allele, likely to impair primer annealing during PCR, was reported with a frequency of 0. 0050 in East Asians, 0. 0229 in Europeans, 0. 0129 in Africans, 0. 0130 in Admixed Americans, and 0. 0491 in South Asians (1000 Genomes phase 3 data;  [30] ). The frequencies estimated in this study for silent alleles were compatible with those reported for Africans and Europeans. In East Timor, a frequency of 14% was estimated for the A allele (based on the two putative homozygotes observed in a total sample of 101 individuals), which is much higher than the reported for East Asian populations in public databases. Exact tests of Hardy-Weinberg equilibrium revealed no-significant deviations in most populations for the 38 indels (Supplementary Table  S5 ). The only exceptions were markers G01 and G09 in Panama that presented significant differences between expected and observed heterozygosity values (P = 0.
Conclusions The present collaborative study allowed the successful implementation of an indel-based multiplex in the vast majority of participant laboratories and the compilation of allele frequency data from a large number of populations of different continental origin. Population comparisons showed that differences are higher among populations within Africa, America and Asia than within Europe. American admixed populations from Mexico, Brazil and Colombia showed high variation within countries, indicating that a correction for population substructure should be applied when databases from general populations are used for these markers in forensic casework. Conversely, F ST values were low within Europe, supporting the use of single databases for populations from more than one country, for example a single Iberian database, excluding Basques.
Ethics and funding source All patients agreed to participate. This study was approved by the local ethics committee (CPP: Comité de Protection des Personnes) and declared to the CNIL (Commission Nationale Informatique et Libertés) to ensure protection and confidentiality of data. The reference number on http://www. clinicaltrials. gov is NCT02800395. Biochemical assays were carried out with funds from Université Claude Bernard, Lyon 1. This study received no other funding.