dataesr/scientific-paragraphs-categorization huggingface dataset:
We chose not to build our discourse based opinion annotation on the top of MPQA for two reasons. First, text anchors which correspond to opinion in MPQA are not well defined since each annotator is free to identify expression boundaries. This is problematic if we want to integrate rhetorical structures into the opinion identification task. Secondly, MPQA often groups discourse indicators (but, because, etc. ) with opinion expressions not leading to any guarantee that text anchors will correspond to a well formed discourse unit. The Sentiment Polarity DataSet consists of 1,000 positive and 1,000 negative processed reviews annotated at the document level. However, it was not appropriate for our purposes because the documents in this corpus are very long (more than 30 sentences per document) which would have made the annotation of the discourse structure too hard.
On the other hand, the Subjectivity DataSet 17. http://www. lina. univ-nantes. fr/?Blogoscopie,762. html 18. http://mpqa. cs. pitt. edu 19. www. cs. cornell. edu/people/pabo/movie-review-data 20. http://www. cs. uic. edu/ ˜liub/FBS/sentiment-analysis. html contains 5,000 subjective and 5,000 objective processed sentences. Only sentences or snippets containing at least 10 tokens were included along with their automating labelling decision (objective vs. subjective), as shown in (  7 ). Since sentences are short (at most 3 discourse units), this corpus also did not meet our criteria. Finally, the Customer Reviews Dataset consists of annotated reviews of five products (digital camera, cellular phone, mp3 player and dvd player), extracted from Amazon. com. This corpus provides only target and polarity annotations at the sentence or the snippet level focusing on explicit opinion sentences (cf.
We selected those topics (movies, products, articles) that are associated to more than 10 reviews/reactions. In order to guarantee that the discourse structure is informative enough, we also filtered out documents containing less than three sentences. In addition, for F MR, we balanced the number of positive and negative reviews according to their corresponding general evaluation (i. e. , stars 21 ). For FNR, reactions that are responses to other reactions were removed. • The English data are movie reviews (EMR) from MetaCritic 22. The choice of movie reviews is motivated firstly by the fact that this genre is widely used in the field and secondly, by our aim to compare how opinions are expressed in discourse in different languages (movie reviews were also selected for the French annotation campaign).
Acknowledgments This work was supported by a  DGA-RAPID  project under grant number  0102906143. We also thank our annotators:  Simon Leva ,  Nicolas Nouno ,  Anny Soubeille ,  Lisa Petersen , and  Julie Hall  for their efforts during the annotation campaign. The authors also thank  ERC  grant  269427  for research support. We finally thank the editor and three anonymous reviewers for their constructive comments, which helped us to improve the manuscript.
We used a test data set of this well-studied breeding colony of Common Tern at Banter Lake, Wilhelmshaven, Germany, to perform a parentage test. Parent-chick relationships, known from the social pedigree of that colony, were compared with genetically calculated ones. In order to test our markers and the used parentage program COLONY, we conducted six competing data sets with varying completeness of included parental genotypes. By including fully sampled parent pairs of known family assignment, results were correct for nest mates, single parents and parent pairs. Our marker set provides a powerful tool to investigate life-time reproductive success and other issues of population and socio-biology for Common Terns, e. g. in the aforementioned colony monitored for decades.
To test the ability of parentage assignment in reality we used samples from a well-studied breeding colony of Common Tern. The investigated breeding colony is located on an artificial island on the Banter Lake in Wilhelmshaven, Germany (53°30' N, 8°06' E)  [38, 39]. The widely used parentage program COLONY 2. 0  [40]  was used. The test data set comprised samples of a known social pedigree. Since 1992 the social pedigree of the Banter Lake tern colony has been investigated via an ingenious registration system that enables a completely monitored colony. Adults and chicks are marked with subcutaneous transponders that can be recorded automatically at the breeding sites  [38]. Numerous different studies dealing with population ecology have been derived from these data  [41 -44]. By investigating this well studied colony, we were able to compare a data set of known kinships (chicks and their social parents) with genetically assigned ones.
Furthermore, we wanted to learn about required quality of genotyping data and the reliability of the used parentage program COLONY. This software is able to assign missing parents even without parental genotypes. It defines parents with a symbol ('#' for mothers and '*' for fathers) and a consecutive number, which enables sibship determination. For our quality test, we conducted six different parentage tests and compared them with our known pedigree. Data sets for these tests varied in their completeness of available parental genotypes so that COLONY had to construct the pedigree with more or less complete genetic information. Sampling of genetic material is often difficult in wild animals and achieving material from complete populations is often far from being realistic. Hence, assignment results of these competing data sets may give us an idea about the required amount of parental genotypes in parentage assignment tests for future studies.
Using Primer3 software  [51]  we found 96 primer pairs of which 80 were unique. Of those, 48 microsatellite loci containing at least six repeats were tested for polymorphisms. Ten Common Tern DNA samples were amplified by PCR under the following conditions: a PCR reaction of 25 µL contained 60 ng of total genomic DNA, 0. 4 pmol/µL of each forward and reverse primer, 0. 1 mM of dGTP, dCTP and dTTP, as well as 45 µM of dATP, 1. 5 x PCR buffer (Bioron), 0. 15 units of Top-Taq DNA polymerase (Bioron), 1 µCi [α- 33 P]-dATP (Amersham Biosciences) and a variable amount of mono distilled water to reach a volume of 25 µL. In a TGradient ThermoCycler (Biometra) we performed the following thermocycling program: initial denaturing for 5 min at 95 °C, followed by 38 cycles of 45 s at 95 °C, 60 s at 50-58 °C, 90 s at 72 °C, followed by a final extension step at 72 °C for 10 min and a cooling step at 15 °C for storage.
5 µL sterile filtrated mono distilled water) was analyzed on a Applied Biosystems DNA-Analyzer (ABI 3730) by GATC, Köln, Germany. As internal size standard ET-ROX 500 (Amersham Biosciences) was added to each sample. Peak Scanner Software 2 (Applied Biosystems) was used to analyze result files, which produces decimal numbers for each fragment. We wrote a script in R v3. 1. 0 [54]  to round the decimal numbers for each allele to full allele units in a more objective way. To first define the expected number of alleles per locus, we sorted all occurring unrounded alleles at a specific locus per size and plotted them in a diagram. Allele units were defined when significant gaps between fragment sizes became clear  [17]. For each marker, rounding was performed by comparing each allele with the previous one by taking into account the locus-specific repeat-motif length and a pre-defined minimum discriminatory distance.
Moreover, the program was used to estimate PIC, non-exclusion probabilities for parents, individual identity and sib identity probabilities for the whole marker set. Genepop V4  [56]  was used to calculate exact values for Hardy-Weinberg expectations (HWE) with a Bonferroni correction for multiple comparisons (probability test: dememorization steps: 100000, batches: 500, iterations per batch: 10000) and to estimate null allele frequencies. PowerStats V12 (Promega Corporation 2000) was used to determine power of exclusion values (PE) for each locus and across all loci. COLONY 2. 0  [40]  was chosen for parentage assignment. We did not add information about full-sib relationships for computation, which is facultative in the program's pre-setting. A polygamous mating system was assumed for both sexes in the calculation, since samples from half-sibs of different years were present in the data set.
To test our markers and COLONY software for assignment quality we prepared six contrasting data sets. While genotypes for nestlings remained complete in all of the six calculations, entire used parental genotypes varied: Calculation one (23M. 23F) contained all available parental genotypes, the second one (11M. 23F) contained 100% of paternal genotypes but only 11 randomly selected ones for mothers. The third one (11F. 23M) contained 100% of maternal genotypes but only 11 randomly selected ones for fathers. Calculation four (0M. 23F) contained only paternal genotypes while data set 0F. 23M only maternal ones and data set 0M. 0F no parental genotypes at all. Assignment results concerning correct allocation of nest mates and parent pairs were compared with information from the social pedigree.
We analyzed parentage of 85 juvenile Common Terns and corresponding 46 parents (23 females and 23 males). In order to test the program as well as the suitability of our marker set, six assignment runs were conducted. Results are shown in Table  4 , where each run differs by the inclusion of involved parental genotypes. For the case of providing all maternal and paternal genotypes (23M, 23F), COLONY 2. 0 identified 23 different parent-chick families with their expected sibship and parent-pair combinations. By omitting 12 maternal genotypes (11M, 23F) maternal and parent pair assignment became incorrect for 1. 2% of all chicks, while nest mates and fathers were correctly assigned to 100%. The erroneous assignment corresponds to one nest with only one chick. For the case of omitting 12 paternal genotypes from calculation (11F, 23M), only maternal genotypes could be correctly assigned for 100% of the cases.
Under this condition, we achieved in our test data sets a value of 96. 5-100% correctness for single and parent pairs. In that way, we conclude that our new marker set is able to discriminate adequate between closely related individuals under realistic sampling conditions. Therefore, we recommend the following points to enable correct assignment results in wild populations and without known pedigree information: Development of species specific, high quality markers, utilization of fully genotyped samples without missing alleles, supplemented as much as possible by corresponding parental genotypes. Furthermore, families having at least three chicks and at least one parental genotype very likely produce unequivocal identifications. Adhering to such a strict data selection the COLONY software should be able to assign the correct missing parent in most cases.
Hence, correct full-and half-sibship determination will become possible. Since in most biological studies, availability of parental genotypes is the most crucial point and samples of parent pairs are often incomplete, COLONY software is a powerful program to identify the missing parent. The three multiplex STR panels will provide a cost and time saving approach to genotype large numbers of individuals of the Common Tern. They will supplement and expand the on the social pedigree of the breeding colony on the Banter Lake. Our aim is to genotype thousands of samples in a long-term population study. Together with the automated transponder based identification system, it will enable us to receive a more detailed insight into breeding system, life-time reproductive success, kinship and family structures within this colony.
ACKNOWLEDGEMENTS We are grateful to the many volunteers from  Institute of Avian Research "Vogelwarte Helgoland", Wilhelmshaven, Germany , for their aid and support during field work. We thank  Götz Wagenknecht  for DNA extraction. Sequencing was financed within the project " GenoSeq " funded by the  Ministry of Science, Research and Culture of the Federal State of Brandenburg (Germany)  in the  EFRE program  " Science and technology transfer for innovation"  ( FKZ 80143246 ). The studies on Common Tern population ecology were funded by the  DFG  ( BE 916/5  to 9). G. Schaub  ( Ruhr University Bochum, Germany ) provided the bugs for blood sampling of adult birds. We also thank all apprentices in the lab of  M. Wink  for helping with primer development and genotyping.
Background: The nature of somatic mutations observed in human tumors at single gene or genome-wide levels can reveal information on past carcinogenic exposures and mutational processes contributing to tumor development. While large amounts of sequencing data are being generated, the associated analysis and interpretation of mutation patterns that may reveal clues about the natural history of cancer present complex and challenging tasks that require advanced bioinformatics skills. To make such analyses accessible to a wider community of researchers with no programming expertise, we have developed within the web-based user-friendly platform Galaxy a first-of-its-kind package called MutSpec. Results: MutSpec includes a set of tools that perform variant annotation and use advanced statistics for the identification of mutation signatures present in cancer genomes and for comparing the obtained signatures with those published in the COSMIC database and other sources.
MutSpec offers an accessible framework for building reproducible analysis pipelines, integrating existing methods and scripts developed in-house with publicly available R packages. MutSpec may be used to analyse data from whole-exome, whole-genome or targeted sequencing experiments performed on human or mouse genomes. Results are provided in various formats including rich graphical outputs. An example is presented to illustrate the package functionalities, the straightforward workflow analysis and the richness of the statistics and publication-grade graphics produced by the tool. Conclusions: MutSpec offers an easy-to-use graphical interface embedded in the popular Galaxy platform that can be used by researchers with limited programming or bioinformatics expertise to analyse mutation signatures present in cancer genomes.
Tools can be built so that users with no programming skills can perform complex analyses through a user-friendly graphical interface. Here we present a set of Galaxy tools named MutSpec that offer an accessible framework for advanced analyses of mutation spectra and signatures present in human cancers or experimental systems. MutSpec expands on existing approaches and methods and integrates scripts developed in-house with publicly available R packages to offer a user-friendly interface accessible to biologists with no or limited bioinformatics skills. MutSpec is expected to accelerate the interpretation of mutation patterns observed in human cancers by facilitating their analysis by a wider community and should thus contribute to the identification of new human carcinogens and to a better understanding of how these carcinogens impact the genome.
Overview and code sources MutSpec is an implementation of Perl and R scripts or packages into several Galaxy tools designed to (1) annotate genome variations (MutSpec-Annot), (2) filter and parse list of variants (MutSpec-Filter and MutSpec-Split respectively), (3) compute various statistics describing mutation spectra features (MutSpec-Stat), (4) extract mutation signatures defined by the six types of single base substitutions (SBS) in their trinucleotide sequence context (MutSpec-NMF), (  5 ) compare the obtained signatures with published ones (MutSpec-Compare). The tools are designed to work in a logical sequence, using as input the outputs of each preceding tool. A typical analysis workflow is shown in Fig. 1. The public packages used and the Perl scripts that support each tool are described in Table  1.
Data import and formatting The first tool to be run is MutSpec-Annot. This tool will retrieve different types of structural and functional annotations that will be used by the other MutSpec tools. MutSpec-Annot accepts variant call format (VCF, version 4. 1) files as well as tab-delimited text files that may be obtained from popular sources such as the International Cancer Genome Consortium (ICGC) or The Cancer Genome Atlas (TCGA) data portals and Catalogue of Somatic mutations in Cancer (COSMIC) database. The minimal information required is, for each variant, the chromosome number, the start genomic position, the reference allele and the alternate allele. The columns containing this information should have a header. There are different supported names for these header columns (case-sensitive names) that correspond to formats of data retrieved from popular variant callers or public databases (details provided in the tool interface).
These four columns may be in any order, and other columns may be present. The additional columns will be kept in the output file after the retrieved annotations. Galaxy automatically recognises file formats, but if the format of the imported files need to be corrected, it can be easily done by editing the file attribute or by using the tool Convert (a default tool available in Galaxy). The output of MutSpec-Annot is a tab-delimited text file. This file may be used as input of the tools MutSpec-Filter and MutSpec-Split that both require a tab-delimited text file as input. These later tools are optional as the imported data may not need to be filtered or parsed (see an example analysis further below). The next tool to use is MutSpec-Stat that requires a dataset list (also named 'collection') as input, a specific feature of Galaxy.
Annotations, filtering and databases MutSpec-Annot uses the ANNOVAR software  [17]  to provide various functional annotations of variants, as well as Perl scripts developed in-house to retrieve strand orientation of transcripts and sequence context of variants. ANNOVAR includes several databases and annotation types for mouse and human genomes (required databases and corresponding genomes are listed in Table  2 ), among which some are optional and some are required for MutSpec tools to function properly. MutSpec has been validated for hg19 and mm9 genome builds. Other genomes and ANNOVAR databases may be installed to retrieve additional annotations or study other species based on user preferences. Database updates are regularly provided by ANNOVAR, users should thus check ANNOVAR website for these updates and install them as needed (we created an install file, listAVDB.
txt that can be modified by the Galaxy administrator to specify related databases and reference genomes to be used). Once variants are annotated, the MutSpec-Filter tool may be used to filter out variants that are likely neutral polymorphisms or that are contained in duplicated regions of the genome. For the human genome, there are currently three databases available for polymorphisms: dbSNP (http://www. ncbi. nlm. nih. gov/SNP/), the 1000 genomes project (http://www. 1000genomes. org/) and the Exome Sequencing Project (ESP, http://evs. gs. washington. edu/EVS/) databases. Users may filter against all three or any of these databases. Filtering against dbSNP database will remove all variants with an rs number (SNP ID). It is important to note that ANNOVAR provides two versions of dbSNP, dbSNP138 that includes all variants present in dbSNP and dbSNPNonFlagged that includes only variants that are frequent in human populations (>1 %) and that are not flagged as "clinically associated" in dbSNP database.
We prefer to use dbSNPNonFlagged but users should decide. Another caution about filtering with dbSNP concerns the fact that rs numbers in dbSNP database may correspond to several variants. Although ANNOVAR will only identify exact match by taking into account not only position but also nucleotide change, annotations about a specific variant may not be accurate (more details in: "Assigning dbSNP Identifiers" at http://annovar. openbioinformatics. org/en/latest/articles/dbSNP). With 1000 genomes and ESP filters, the tool will remove variants according to a predefined standard frequency. To use different frequencies, it is recommended to use other tools proposed in Galaxy. For the mouse genome, there is currently only one SNP database available, dbSNP.
SBS counts and chromosome size). An option in MutSpec-Stat is to compute statistics that can be used to estimate the number of mutation signatures present in the analysed dataset (an NMF R package is used  [18] , see next section). Another option available is the computation of statistics on the pooled samples. The output of MutSpec-Stat is an html page that contains links to summary results for each individual sample and to an Excel file (called "Report") that contains sample datasheets with all results displayed in various formats (tables, heatmaps, bar graphs, matrices, WebLogo). Each datasheet is named after the sample ID. It is of note that because Excel has a limitation on datasheet names, sample identifiers must be within a limit of 31 characters. All individual tables and graphs can also be downloaded as individual files in a compressed archive.
Extraction of mutation signatures MutSpec-NMF extracts the minimal set of mutation signatures that optimally explains the proportion of each mutation type (96 types represented by the 6 base substitutions in their trinucleotide sequence context) found in each sample and then estimates the contribution of each signature to each sample. MutSpec-NMF uses the non-negative matrix factorization algorithm from Brunet et al. [19]  implemented in the NMF R package developed by Gaujoux and Seoighe  [18]. The Brunet algorithm has been successfully used to extract mutation signatures from somatic mutation data in human cancers  [3, 8]. Here we use the default algorithm of Brunet with the Kullback-Leibler divergence penalty and a number of iterations set to 200 in order to achieve stability of the results.
The aim of the method is to reduce the dimension of the original data, with the caveat that the factorisation rank needs to be specified. It is thus necessary to first estimate the factorisation rank (number of expected signatures) for the analysed dataset. This can be done with the option available in MutSpec-Stat that performs NMF with different rank values (2 to 8 by default) and compute some quality measures of the results, including the cophenic coefficient and the rss curve. The NMF R package cited above is also used to perform these analyses. The best rank value indicated by the quality measures may be used as the number of expected signatures in MutSpec-NMF as suggested by the authors of the NMF package  [18]. The calculation of these statistics is optional in MutSpec-Stat because running NMF on a large dataset requires intensive computations (for estimating the rank value a total of 50 runs are performed for each value while 200 runs are performed for the full analysis).
Comparison of obtained signatures with published signatures MutSpec-Compare computes the similarity between the signatures identified by MutSpec-NMF and a set of published signatures using the cosine similarity method implemented in the LSAfun R package  [20]. This method measures the similarity between two vectors of an inner product space by calculating the cosine of the angle between them. The resulting values range between 0 and 1, corresponding respectively to an absence or a complete similarity. Results are displayed graphically as a heatmap and provided as a tabular matrix. A cosine value above 0. 9 can be considered as a good match. For the reference signatures, user may select the matrix provided with the tool or their own matrix. The matrix provided includes 30 signatures published in the COSMIC database (v72)  [21]  plus four experimental signatures (methylnitronitrosoguanidine, aristolochic acid, benzo(a) pyrene, activation-induced cytidine deaminase) previously published in Olivier et al.