dataesr/scientific-paragraphs-categorization huggingface dataset:
In silico SNP identification If not otherwise stated, software was used with default parameters. The Fastx-toolkit version 0. 0. 13 (http://hannonlab. cshl. edu/fastx_toolkit/index. html) was used for quality control and initial cleaning of the sequencing reads. Adaptators were first removed and the three last bases were trimmed from reads because they showed global poor quality scores (Phred quality score ,24) in most reads. We then filtered reads by their quality score and retained only reads for which at least 90% of the bases had a minimum Phred quality score of 24 (corresponding to less than 1 incorrect base call in 100 and more than 99% of base call accuracy). Paired-end data were then considered as two subsets of single-end data. Reads were aligned onto the reference genome version 5.
2  [27]  with the Bowtie software version 0. 12. 7  [30]. Because 47% of the S. mansoni genome consists of repeated sequences  [31]  we did not allow for alignment of reads that matched more than one time to avoid false positives in subsequent SNP calling. We allowed 2 mismatches and used the ''best'' and ''strata'' options. SNP calling was done with Freebayes software version 0. 9. 5 (https://github. com/ekg/freebayes) with parameters -min-coverage -min-alternate-fraction -min-alternate-totalpooled -ploidy 20. We tested a minimum coverage of 10 or 20 reads per SNP position (-min-coverage 10 or 20) with a minimum of 2 reads supporting the variant allele (-min-alternate-total 2) and with a minimum frequency of variant of 0. 02 (-min-alternatefraction 0. 02). By retaining only SNP that met these conditions, we prevented mistakes in SNP discovery due to errors during base calling and we allowed the discovery of rare variants (up to a frequency of 0.
02). Among all SNPs, we identified rare and frequent SNPs by using a 20% threshold as proposed in other studies  [17, 32]. In silico verification of SNP calls was performed by choosing at random 20 SNPs per chromosome and per strain for each minimum coverage value 10 and 20, corresponding to a total of 640 SNPs. We visually checked the number of reads, the number of variant allele and the alternative base identity with the Next Generation Sequence Assembly Visualization software Tablet  [33]. All further analyses were done on the dataset obtained with the option -min-coverage 20.
Structural variants We used CNV-seq to detect copy number variation  [24]  using a 10 26 p-value threshold. Briefly, this algorithm uses variation in sequence coverage in a sliding window between genomic reads mapped to a reference genome to detect variations in sequence copy numbers. Plots were drawn using the associated cnv package in R. We manually checked protein-coding genes contained in the 20 largest genomic regions and the 20 regions with the highest difference in copy number between the BRE and GH2 strain by using the coordinates of the regions in the Schistosoma mansoni local Gbrowse instance of the genome (http://genome. univ-perp. fr).
Whole-genome scan for footprints of selection In the current version of the genome, the Z and the W chromosomes are put into a single linkage group  [27]  despite their physical separation into two chromosomes. SNPs could therefore reflect differences between Z and W and not between target and reference genome. For this reason we excluded them from further analyses of selection. To identify footprints of selection on the 7 autosomes of Schistosoma mansoni from BRE and GH2 pool sequences, we relied on the approach recently proposed by Boitard et al. [21]  implemented in the pool_hmm program  [22]. The following option were used to run the analyses:-C 1000 -k 1e-10 -pred -t h w , where h w corresponds to the average unbiased Watterson theta estimator of nucleotide diversity for the population of interest (see above).
We identified overlapping and private selective sweeps between the two strains by comparing their genomic position for each chromosome. As some genomic region swept in one strain could overlap several shorter regions swept in the other strain (and vice versa), we counted overlapping regions as the exact number of genome fragments really overlapping (i. e. counting the number of shorter instead of counting only the larger region covering them). Private selective sweeps of each strain correspond to all genomic regions that strictly did not overlap any sweeps of the other strain. Identification of known transcripts in private selective sweeps for both strains was performed using the genome coordinates of the regions and the Schistosoma mansoni local Gbrowse instance of the genome. Confidence index was calculated for each specific selective sweep as the maximum of 2log(1-q i ) over the window, where q i is the posterior probability of hidden state ''Selection'' given after simulations.
Functional characterization of selected genes To characterize the molecular functions of the transcripts contained in selective sweeps, we first performed functional annotations of a recent S. mansoni transcriptome (unpublished results) by using the Blast2GO software  [38]. After the blast step, we mapped gene ontology (GO) terms (BRE: NodeScore = 10, alpha = 0. 4; GH2: NodeScore = 15, alpha = 0. 2). We then scanned the proteins with Interproscan  [39]  and performed GO-Enzyme code mapping to improve annotations before running the annotation step. We finally merged results from these three annotation methods before making functional analyses. We checked if our lists of transcripts matched with proteins that were identified in an earlier study comparing these two strains at the proteomic level  [3]  using tblastn on the 5.
2 version of the S. mansoni genome. Results were verified by visual inspection on a local GBrowse instance of the genome and transcriptome. We then performed functional analyses with Blast2GO tools for the list of transcripts from selective sweeps but not for the list of proteincoding genes from CNV data because this latter list was not exhaustive (see the ''Structural variants'' previous section). Enrichment analysis was performed for each strain by using the Fisher's exact test with the P-value filter mode set at the default value of 0. 05 (Bonferroni correction is applied). Combined graphs were then drawn for each strain from two kinds of data: i) the total list of transcripts found in selective sweeps and ii) the reduced list of transcripts obtained after enrichment analysis. In both cases, Score alpha and Seq Filter value were set at the default values of 0.
Synonymous vs. non-synonymous SNPs SNPEff was used to scan synonymous and non-synonymous SNPs in exons of the whole genome of our parasite model. We downloaded the GFF3 file for the latest assembly (v5. 2, nov. 2011) from the Sanger centre (ftp://ftp. sanger. ac. uk/pub/pathogens/ Schistosoma/mansoni/genome/GFF/Smansoni_gff_21032012. tar. gz) and modified it using custom scripts so that gff data and fasta data were separated. We then built a SnpEff database using snpEff. jar with ''build -gff3''. A total of 13,385 genes and 14,395 transcripts were detected. The snpEff. config file was modified accordingly and snpEff run with the following parameters: -c snpEff. config -i vcf -o txt -upDownStreamLen 5000 -no None -stats.
Pool-sequencing Sequencing of BRE and GH2 strains produced roughly 500,000,000 raw clusters each (Table  S1 ). About 50% passed quality checking (Table  S1 ) and were subsequently aligned to the unique sequences of the reference genome (strain NMRI). Unsurprisingly, we successfully aligned only 46. 95% and 59. 29% of high-quality reads for BRE and GH2 strains, respectively. This is consistent with the large proportion (47%) of repetitive sequences in the S. mansoni genome  [31]. Sequencing data are available under study accession number SRP016500 (alias PRJNA177787) at the NCBI sequence read archive. SNP description. After read alignment on the reference genome and SNP calling for each strain separately, we identified 672,467 polymorphic sites for GH2 and 464,746 for BRE. About 0. 2% of them were multiple alleles (i.
6%) are fixed in one of the two populations (Fst = 1). Identification of structural variants. We used CNV-seq (R version 2. 10. 1, package 'cnv' version 0. 2-7. ) to detect copy number variations between the two populations  [24]. Because our sequences do not come from a single individual but a population, we used a stringent p-value of 10 26 to decrease background variation in coverage. We identified 1,474 CNV for autosomes and 529 for W sex chromosome linkage group with a calculated sliding window of 1,554 and 1,726 nucleotides, respectively (Table  2 , Figure  1 , Table  S3 ). This number of CNVs is probably underestimated due to relatively modest average sequencing depth. We also analysed independently unassembled scaffolds larger than 500 kb. As outlined above, reads that matched the reference genome more than one time were removed before SNP calling.
Comparison of combined graphs from Blast2GO gene ontology analysis highlighted several biological processes (Supplementary figure  S5 ). Proteolysis and transport appeared as common biological processes selected in each of the two strains, despite the fact that they correspond to non-overlapping lists of transcripts found in private selective sweeps. Specific processes were also identified, such as cell-cell adhesion for the BRE strain or redox reactions and protein phosphorylation for the GH2 strain. Using the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database, we looked for molecular pathways containing enzymes appearing in regions under selection for each strain. We identified 26 and 34 enzymes (distributed over 24 and 22 pathways) for BRE and GH2, respectively (Table  S7 ). We found five common pathways (Purine metabolism, Riboflavin metabolism, Butanoate metabolism, Aminobenzoate degradation and Carbon fixation).
Acknowledgments We would like to thank  Julie Lepesant  for providing biological material,  Jean-Francois Allienne  for technical support during DNA extraction,  Alexandre Nore  for technical support during in situ validation of  SNPs, Celine Cosseau  for discussion about results and all her precious advice, specifically during in situ validation of SNPs, and  Simon Boitard  for helpful assistance on the implementation of the pool-hmm model. We are grateful to  Christopher Bayne  for carefully reading the manuscript.
This work received funding from the  French National Agency for Research (ANR) , project  ANR-2010-BLAN-1720-01  (EPIGEVOL) (http://www. agencenationale-recherche. fr/programmes-de-recherche/recherches-exploratoires-et-emergentes/blanc-generalite-et-contacts/blanc-presentation-synthetique-duprojet/?tx_lwmsuivibilan_pi2[CODE] =  ANR-10-BLAN-1720 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The World Register of Marine Species is an over 90% complete open-access inventory of all marine species names. Here we illustrate the scale of the problems with species names, synonyms, and their classification, and describe how WoRMS publishes online quality assured information on marine species. Within WoRMS, over 100 global, 12 regional and 4 thematic species databases are integrated with a common taxonomy. Over 240 editors from 133 institutions and 31 countries manage the content. To avoid duplication of effort, content is exchanged with 10 external databases. At present WoRMS contains 460,000 taxonomic names (from Kingdom to subspecies), 368,000 species level combinations of which 215,000 are currently accepted marine species names, and 26,000 related but non-marine species. Associated information includes 150,000 literature sources, 20,000 images, and locations of 44,000 specimens.
aurita (Sluiter, 1890), or the shrimp genera Allopontonia Bruce, 1972, and Altopontonia Bruce, 1990. If they are all included in a common database then these distinctions become more apparent and reduce confusion. Thus to find information on a species one needs to know which names may be in fact referring to the same species. When a comprehensive review of a species is undertaken, a search on synonyms, misspellings and homonyms is required.
Misspellings Misspellings abound in the literature and are perpetuated because authors neither check the original descriptions nor even validated lists of names when available. Some misspellings are not surprising considering the similarities and peculiarities of some accepted species names. oxyrhynchus. What all these species have in common is that they have something that could be called a pointed ''nose'', which the original describers found so striking that they named their species for it. Another issue is that spelling errors from the literature may be entered into databases, perhaps the error may occur during data entry, and then perpetuated, sometimes unknowingly, but on other occasions intentionally. For example the spelling error Ammothea sextarticulata (instead of Ammothea sexarticulata  Munilla, 1990 ) was first published in 1994, and was entered as such in WoRMS in 2005.
It was later corrected by the taxonomic editor but has already had 410 Google hits, whereas the correct spelling has had only 118 (checked 19 April 2012). In this case, the incorrect spelling is present in several online databases and continues to perpetuate in the literature; even the original author used the misspelling in 2008  [41]. To enable tracking of such errors, a taxonomic database should retain all published spellings but indicate which are in error.
Biodiversity informatics Several initiatives to better organise species names have been undertaken. In the early 1990s, van der Land  [47]  began to list species names through contacting experts and published the UNESCO-IOC Register of Marine Organisms (URMO) on diskette. In 1972 in the USA, NOAA's National Ocean Data Center developed a list of marine species names with code numbers, the NODC Taxonomic Code. This became part of the Integrated Taxonomic Information system (ITIS) in 1996 (http:// www. nodc. noaa. gov/General/CDR-detdesc/taxonomic-v8. html http://www. nodc. noaa. gov/General/CDR-detdesc/taxonomic-v8. html). In 1997, Frank Bisby and colleagues launched a global effort called Species 2000 to link together and publish Global Species Databases (GSDs) on the internet, and subsequently also as the 'Annual Checklist' on CD-ROM.
Most of the GSDs had not been previously published on the internet but sat on individual scientists' computers. The application of information technologies (IT) to biodiversity data, called biodiversity informatics, enables international collaboration and data management to be fast at low cost  [48, 49, 50, 51, 52, 53]. The compilation of species names is aided by the ability of computers to search names from the literature and other databases  [6]. Indeed, several important compilations of names exist, such as the Index of Organism Names (ION) which includes Zoological Record (www. organismnames. com http://www. organismnames. com/) and the Global Names Index (http://gni. globalnames. org/ http://gni. globalnames. org/). The latter now has 20 million name strings but this represents about 1.
Thus Moretzsohn  [54]  proposed a special online database called TaxonBank, to register the location and other details of type specimens. The Australian Faunal Directory  [55]  includes type specimen information. Such a resource is needed for all species. Scientific natural history museums and herbaria are depositories for reference collections of botanical, zoological, and paleontological specimens used in taxonomy and other life science disciplines. Synonymies are difficult to establish without reference to type specimens. These are kept in such collections and are accessible for that purpose  [56]. Museum collections store specimens with collection data indicating locality and date of sampling. When there is uncertainty about species records if similar species are involved (including sibling species), then the study of museum specimens may yield solutions.
This was notable in (a) bringing together over 170 experts to pool their knowledge on what species occurred in European seas into one database, (b) legally establishing the Society for the Management of Electronic Biodiversity Data (SMEBD) to hold the Intellectual Property Rights (IPR) of the contributors and thus facilitate the systems succession planning, and (c) having all the content in one standardised database  [69, 74]. In 2000, the A. P. Sloan Foundation launched the Census of Marine Life (CoML), a decade of globally coordinated discovery in marine biology. CoML established an Ocean Biogeographic Information System (OBIS), which published species distribution data over the internet. This used a similar standard to, and is the largest marine contributor to the Global Biodiversity Information Facility (GBIF), established in 2001.
These initiatives, and the increased use of databases to manage biological data, increased the demand for a standard checklist of marine species names and their relationships to synonyms. Following the completion of its start-up project, ERMS became hosted by a professional marine data centre at the Flanders Marine Institute (VLIZ). This provided the computing and data management infrastructure and support team on which to expand ERMS to become a World Register of Marine Species (WoRMS), and SMEBD provided the community of experts who invited colleagues to expand the content  [51, 74, 75]. WoRMS was thus independent of, but collaborated with and contributed to, CoML and Species 2000. In this paper, we report how WoRMS has become an established part of the global biodiversity infrastructure, and is playing an increasingly important role in taxonomic data management.
Expert community WoRMS editors were selected by their peers through knowledge of their publications and expertise in a taxon. The advantage of this approach was that the best known and most senior experts were first involved. They provided leadership and example to younger researchers and the wider community. However, the editors were encouraged to invite their colleagues to spread the workload and provide succession, including young researchers who may be more comfortable with using online databases for publication. Engaging potential editors was greatly helped by personal relationships and contacts at scientific meetings. In particular, the frequent workshops and meetings of the Census of Marine Life significantly helped such interactions, and most of the WoRMS Steering Committee (SC) members were involved in CoML.
Host institution Another important factor in attracting editors is the security a professional data centre provides for the continuity of the database. The data centre provides a database support team, so that changes in staff do not interrupt WoRMS development, and can provide 24/7 support, archiving, and professional IT system design and management. The host institution, VLIZ, is a leading Ocean Data Centre within the Intergovernmental Oceanographic Commission's (IOC) International Oceanographic Data and Information Exchange (IODE) programme, and a certified member of the World Data System of the International Council of Scientific Unions (ICSU). It finds the WoRMS database invaluable in its wider data-management activities. Thus it can support WoRMS as it supports other projects.
Role of SMEBD SMEBD was established to hold the Intellectual Property Rights of the ERMS, the precursor to WoRMS. The WoRMS SC was established within SMEBD to manage WoRMS. All contributors to WoRMS have the right to become honorary life-members of SMEBD. WoRMS editors nominate and elect people to the SC. As a legal entity and holder of the contributors' IPR, SMEBD has a key role in formally approving the host institution of the database, how it is disseminated, negotiating exceptional uses of the database, and following up on misuse of the data. For example, SMEBD successfully had a book withdrawn from publication because it had largely republished a WoRMS GSD without attribution of the source. SMEBD can also act as a contractor in research projects and manage their finances. It has been a full partner in two European Commission research contracts.
SMEBD thus provides the governance for WoRMS. Its legal incorporation in Ireland requires it to have a detailed annual audit, and limits the financial liability of its Directors and members from any claims made against them in relation to the activities and assets of SMEBD. In contributing to the database, past and present, the editors have agreed to voluntarily provide data, information, opinion, or other expert assistance to the database. They retain the right to use and publish any data and intellectual property created by themselves, but authorise SMEBD to store, compile, modify, revise, and disseminate the data provided and derived by any means (e. g. electronic, World Wide Web, book). This includes appointing new editors who may add to and modify the original contributions of previous editors.
Citability We recognised the importance of making the editors responsible for WoRMS visible on the web pages for two reasons. First, doing this indicates the authority behind the database content. Second, it was recognised that the editors wished their work to be recognised and attributed to them. We thus follow the well-established method of citing publications  [76]. Each species and higher-taxon page has a citation at the foot of the page. Thus a user is expected to cite the species page, a higher-taxon page (e. g. , Amphipoda), a GSD or the database as a whole, that is  [77]  depending on how they use it. WoRMS may be the first online biodiversity database to provide multiple levels of citation.
The glossary can be expanded as users demand and experts are willing; modified based on feedback and changing use of terminology; contributes to data management by providing definitions for use of terminology in databases, and assists the development of ontologies that relate terms to each other. It is permanent with editors being replaced as their availability changes and new expertise is desirable; and contributes to associated initiatives including the Encyclopedia of Life (EoL), CoML, WoRMS, GBIF, OBIS, and IODE of IOC. It does not intend to provide a review or history of all uses of particular terms, nor how they may be used in other fields of research. However, a further development may be to make relationships between terms apparent in a 'semantic ontology'. The definitions are those recommended for use in marine biology, ecology and geology.
Infrastructure The WoRMS data are stored in a relational MS SQL 2008 database called Aphia. An MS Access front end is built for administration purposes to control edit rights and perform quality control. The database contains over 440 fields, of which accepted species name is the most complete (100%). These fields are organised into 79 related tables described on the website at http:// www. marinespecies. org/structure http://www. marinespecies. org/ structure/. AphiaID provides a unique and permanent number for every species name within WoRMS (e. g. AphiaID 127160). It enables users to match up names in their databases with future versions of WoRMS, particularly where the status of a name may have changed (e. g. become a synonym) or the classification of the species may have changed. The AphiaID is included within the WoRMS LifeSience Identifier (LSID, http://sourceforge.
net/ projects/lsid), which is an implementation of a persistent Globally Unique Identifier (GUID). An example of a GUID is urn:lsid:marinespecies. org:taxname:127160. In addition, these LSIDs are resolvable and that they can produce structured taxonomic information in RDF (Resource Description Framework) format. The editorial board has direct access to the database via a PHP (Hypertext Preprocessor) web interface. If editors prefer to work off-line they can use an MS Excel template, which is often also used for bulk updates. The WoRMS website is running on an Apache2 windows server, which backs up the data on a daily basis. The entire database is archived each month and users can download previous versions upon request. Copies of the database can be downloaded by organisations or individuals following approval by SMEBD.